10 텍스트파일 엑셀파일.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>이름</th>\n",
       "      <th>직업</th>\n",
       "      <th>국적</th>\n",
       "      <th>트위터</th>\n",
       "      <th>생년월일</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>이성주</td>\n",
       "      <td>컴퓨터 프로그래머/데이터 과학자</td>\n",
       "      <td>대한민국</td>\n",
       "      <td>@LeeSeongjoo</td>\n",
       "      <td>1982-12-27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Rossum</td>\n",
       "      <td>컴퓨터 프로그래머</td>\n",
       "      <td>네덜란드</td>\n",
       "      <td>@gvanrossum</td>\n",
       "      <td>1956-01-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Turing</td>\n",
       "      <td>컴퓨터과학자</td>\n",
       "      <td>영국</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1912-06-23</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       이름                  직업     국적            트위터         생년월일\n",
       "0     이성주   컴퓨터 프로그래머/데이터 과학자   대한민국   @LeeSeongjoo   1982-12-27\n",
       "1  Rossum           컴퓨터 프로그래머   네덜란드    @gvanrossum   1956-01-31\n",
       "2  Turing              컴퓨터과학자     영국            NaN   1912-06-23"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# text 파일은 csv로 읽기\n",
    "pd.read_csv('./data/geeks.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>이름</th>\n",
       "      <th>기술</th>\n",
       "      <th>국적</th>\n",
       "      <th>트위터</th>\n",
       "      <th>생년월일</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>이성주</td>\n",
       "      <td>컴퓨터 프로그래머/데이터 과학자</td>\n",
       "      <td>대한민국</td>\n",
       "      <td>@LeeSeongjoo</td>\n",
       "      <td>1982-12-27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Rossum</td>\n",
       "      <td>컴퓨터 프로그래머</td>\n",
       "      <td>네덜란드</td>\n",
       "      <td>@gvanrossum</td>\n",
       "      <td>1956-01-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Turing</td>\n",
       "      <td>컴퓨터과학자</td>\n",
       "      <td>영국</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1912-06-23</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       이름                  기술     국적            트위터         생년월일\n",
       "0     이성주   컴퓨터 프로그래머/데이터 과학자   대한민국   @LeeSeongjoo   1982-12-27\n",
       "1  Rossum           컴퓨터 프로그래머   네덜란드    @gvanrossum   1956-01-31\n",
       "2  Turing              컴퓨터과학자     영국            NaN   1912-06-23"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_csv('./data/geeks_cp949.txt', encoding='cp949')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "ename": "UnicodeDecodeError",
     "evalue": "'utf-8' codec can't decode byte 0xc0 in position 0: invalid start byte",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mUnicodeDecodeError\u001b[0m                        Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-4-1af94a2ae14e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'./data/geeks_cp949.txt'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/anaconda3/envs/py-study/lib/python3.6/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36mparser_f\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, escapechar, comment, encoding, dialect, tupleize_cols, error_bad_lines, warn_bad_lines, skipfooter, skip_footer, doublequote, delim_whitespace, as_recarray, compact_ints, use_unsigned, low_memory, buffer_lines, memory_map, float_precision)\u001b[0m\n\u001b[1;32m    707\u001b[0m                     skip_blank_lines=skip_blank_lines)\n\u001b[1;32m    708\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 709\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    710\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    711\u001b[0m     \u001b[0mparser_f\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/py-study/lib/python3.6/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m    447\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    448\u001b[0m     \u001b[0;31m# Create the parser.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 449\u001b[0;31m     \u001b[0mparser\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    450\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    451\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0miterator\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/py-study/lib/python3.6/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m    816\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'has_index_names'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'has_index_names'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    817\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 818\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    819\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    820\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/py-study/lib/python3.6/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[0;34m(self, engine)\u001b[0m\n\u001b[1;32m   1047\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mengine\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'c'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1048\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'c'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1049\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mCParserWrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1050\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1051\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'python'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/py-study/lib/python3.6/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, src, **kwds)\u001b[0m\n\u001b[1;32m   1693\u001b[0m         \u001b[0mkwds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'allow_leading_cols'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex_col\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1694\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1695\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reader\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mparsers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTextReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1696\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1697\u001b[0m         \u001b[0;31m# XXX\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader.__cinit__\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader._get_header\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mUnicodeDecodeError\u001b[0m: 'utf-8' codec can't decode byte 0xc0 in position 0: invalid start byte"
     ]
    }
   ],
   "source": [
    "pd.read_csv('./data/geeks_cp949.txt')"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>이성주</th>\n",
       "      <th>컴퓨터 프로그래머/데이터 과학자</th>\n",
       "      <th>대한민국</th>\n",
       "      <th>@LeeSeongjoo</th>\n",
       "      <th>1982-12-27</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Rossum</td>\n",
       "      <td>컴퓨터 프로그래머</td>\n",
       "      <td>네덜란드</td>\n",
       "      <td>@gvanrossum</td>\n",
       "      <td>1956-01-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Turing</td>\n",
       "      <td>컴퓨터과학자</td>\n",
       "      <td>영국</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1912-06-23</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      이성주  컴퓨터 프로그래머/데이터 과학자   대한민국  @LeeSeongjoo   1982-12-27\n",
       "0  Rossum          컴퓨터 프로그래머   네덜란드   @gvanrossum   1956-01-31\n",
       "1  Turing             컴퓨터과학자     영국           NaN   1912-06-23"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_csv('./data/geeks_no_header.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>이성주</td>\n",
       "      <td>컴퓨터 프로그래머/데이터 과학자</td>\n",
       "      <td>대한민국</td>\n",
       "      <td>@LeeSeongjoo</td>\n",
       "      <td>1982-12-27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Rossum</td>\n",
       "      <td>컴퓨터 프로그래머</td>\n",
       "      <td>네덜란드</td>\n",
       "      <td>@gvanrossum</td>\n",
       "      <td>1956-01-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Turing</td>\n",
       "      <td>컴퓨터과학자</td>\n",
       "      <td>영국</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1912-06-23</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        0                   1      2              3            4\n",
       "0     이성주   컴퓨터 프로그래머/데이터 과학자   대한민국   @LeeSeongjoo   1982-12-27\n",
       "1  Rossum           컴퓨터 프로그래머   네덜란드    @gvanrossum   1956-01-31\n",
       "2  Turing              컴퓨터과학자     영국            NaN   1912-06-23"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_csv('./data/geeks_no_header.csv', header=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>이름</th>\n",
       "      <th>직업</th>\n",
       "      <th>국적</th>\n",
       "      <th>트위터</th>\n",
       "      <th>생년월일</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>이성주</td>\n",
       "      <td>컴퓨터 프로그래머/데이터 과학자</td>\n",
       "      <td>대한민국</td>\n",
       "      <td>@LeeSeongjoo</td>\n",
       "      <td>1982-12-27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Rossum</td>\n",
       "      <td>컴퓨터 프로그래머</td>\n",
       "      <td>네덜란드</td>\n",
       "      <td>@gvanrossum</td>\n",
       "      <td>1956-01-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Turing</td>\n",
       "      <td>컴퓨터과학자</td>\n",
       "      <td>영국</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1912-06-23</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       이름                  직업     국적            트위터         생년월일\n",
       "0     이성주   컴퓨터 프로그래머/데이터 과학자   대한민국   @LeeSeongjoo   1982-12-27\n",
       "1  Rossum           컴퓨터 프로그래머   네덜란드    @gvanrossum   1956-01-31\n",
       "2  Turing              컴퓨터과학자     영국            NaN   1912-06-23"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_csv('./data/geeks_no_header.csv',names=['이름', '직업', '국적', '트위터', '생년월일'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>이름</th>\n",
       "      <th>직업</th>\n",
       "      <th>국적</th>\n",
       "      <th>트위터</th>\n",
       "      <th>생년월일</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>이성주</td>\n",
       "      <td>컴퓨터 프로그래머/데이터 과학자</td>\n",
       "      <td>대한민국</td>\n",
       "      <td>@LeeSeongjoo</td>\n",
       "      <td>1982-12-27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Rossum</td>\n",
       "      <td>컴퓨터 프로그래머</td>\n",
       "      <td>네덜란드</td>\n",
       "      <td>@gvanrossum</td>\n",
       "      <td>1956-01-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Turing</td>\n",
       "      <td>컴퓨터과학자</td>\n",
       "      <td>영국</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1912-06-23</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       이름                  직업     국적            트위터         생년월일\n",
       "0     이성주   컴퓨터 프로그래머/데이터 과학자   대한민국   @LeeSeongjoo   1982-12-27\n",
       "1  Rossum           컴퓨터 프로그래머   네덜란드    @gvanrossum   1956-01-31\n",
       "2  Turing              컴퓨터과학자     영국            NaN   1912-06-23"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "프로그래머 = pd.read_csv('./data/geeks.csv', encoding='utf-8')\n",
    "프로그래머"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'직업'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m~/anaconda3/envs/py-study/lib/python3.6/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   2524\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2525\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2526\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: '직업'",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-9-98fd09dd93a5>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0m프로그래머\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'직업'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/anaconda3/envs/py-study/lib/python3.6/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   2137\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2138\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2139\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_column\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2140\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2141\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_getitem_column\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/py-study/lib/python3.6/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m_getitem_column\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   2144\u001b[0m         \u001b[0;31m# get column\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2145\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_unique\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2146\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_item_cache\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2147\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2148\u001b[0m         \u001b[0;31m# duplicate columns & possible reduce dimensionality\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/py-study/lib/python3.6/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m_get_item_cache\u001b[0;34m(self, item)\u001b[0m\n\u001b[1;32m   1840\u001b[0m         \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcache\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1841\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mres\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1842\u001b[0;31m             \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1843\u001b[0m             \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_box_item_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1844\u001b[0m             \u001b[0mcache\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/py-study/lib/python3.6/site-packages/pandas/core/internals.py\u001b[0m in \u001b[0;36mget\u001b[0;34m(self, item, fastpath)\u001b[0m\n\u001b[1;32m   3841\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3842\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3843\u001b[0;31m                 \u001b[0mloc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3844\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3845\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0misna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/py-study/lib/python3.6/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   2525\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2526\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2527\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_cast_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2528\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2529\u001b[0m         \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: '직업'"
     ]
    }
   ],
   "source": [
    "프로그래머['직업']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     컴퓨터 프로그래머/데이터 과학자\n",
       "1             컴퓨터 프로그래머\n",
       "2                컴퓨터과학자\n",
       "Name:  직업, dtype: object"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 공백이 있음...\n",
    "프로그래머[' 직업']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    컴퓨터 프로그래머/데이터 과학자\n",
       "1            컴퓨터 프로그래머\n",
       "2               컴퓨터과학자\n",
       "Name: 직업, dtype: object"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 공백을 지우고 가져와 보자\n",
    "프로그래머 = pd.read_csv('./data/geeks.csv', encoding='utf-8', skipinitialspace=True)\n",
    "프로그래머['직업']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "프로그래머.to_csv('프로그래머.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "프로그래머.to_csv('프로그래머.csv', encoding='utf-8')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>이름</th>\n",
       "      <th>직업</th>\n",
       "      <th>국적</th>\n",
       "      <th>트위터</th>\n",
       "      <th>생년월일</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>이성주</td>\n",
       "      <td>컴퓨터 프로그래머/데이터 과학자</td>\n",
       "      <td>대한민국</td>\n",
       "      <td>@LeeSeongjoo</td>\n",
       "      <td>1982-12-27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Rossum</td>\n",
       "      <td>컴퓨터 프로그래머</td>\n",
       "      <td>네덜란드</td>\n",
       "      <td>@gvanrossum</td>\n",
       "      <td>1956-01-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Turing</td>\n",
       "      <td>컴퓨터과학자</td>\n",
       "      <td>영국</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1912-06-23</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0      이름                 직업    국적           트위터        생년월일\n",
       "0           0     이성주  컴퓨터 프로그래머/데이터 과학자  대한민국  @LeeSeongjoo  1982-12-27\n",
       "1           1  Rossum          컴퓨터 프로그래머  네덜란드   @gvanrossum  1956-01-31\n",
       "2           2  Turing             컴퓨터과학자    영국           NaN  1912-06-23"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "프로그래머 = pd.read_csv('프로그래머.csv', encoding='utf-8')\n",
    "프로그래머"
   ]
  },
  {
   "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>이름</th>\n",
       "      <th>직업</th>\n",
       "      <th>국적</th>\n",
       "      <th>트위터</th>\n",
       "      <th>생년월일</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>이성주</td>\n",
       "      <td>컴퓨터 프로그래머/데이터 과학자</td>\n",
       "      <td>대한민국</td>\n",
       "      <td>@LeeSeongjoo</td>\n",
       "      <td>1982-12-27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Rossum</td>\n",
       "      <td>컴퓨터 프로그래머</td>\n",
       "      <td>네덜란드</td>\n",
       "      <td>@gvanrossum</td>\n",
       "      <td>1956-01-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Turing</td>\n",
       "      <td>컴퓨터과학자</td>\n",
       "      <td>영국</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1912-06-23</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       이름                 직업    국적           트위터        생년월일\n",
       "0     이성주  컴퓨터 프로그래머/데이터 과학자  대한민국  @LeeSeongjoo  1982-12-27\n",
       "1  Rossum          컴퓨터 프로그래머  네덜란드   @gvanrossum  1956-01-31\n",
       "2  Turing             컴퓨터과학자    영국           NaN  1912-06-23"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "프로그래머.iloc[:, 1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "파일경로 = './data/koreaexim/international/Global+Market+Daily+Table%28%EA%B0%92%EB%B3%B5%EC%82%AC%29_0803.xls'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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></th>\n",
       "      <th>Global Market Daily Update</th>\n",
       "      <th>Unnamed: 1</th>\n",
       "      <th>Unnamed: 2</th>\n",
       "      <th>Unnamed: 3</th>\n",
       "      <th>Unnamed: 4</th>\n",
       "      <th>Unnamed: 5</th>\n",
       "      <th>As of:</th>\n",
       "      <th>2014-10-20 00:00:00</th>\n",
       "      <th>2014-10-18 00:00:00</th>\n",
       "      <th>Unnamed: 9</th>\n",
       "      <th>2015-08-03 00:00:00</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"52\" valign=\"top\">NaN</th>\n",
       "      <th>NaN</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>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NaN</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>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NaN</th>\n",
       "      <td>Equity</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>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NaN</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Last Price</td>\n",
       "      <td>D-1</td>\n",
       "      <td>D-7</td>\n",
       "      <td>MTD</td>\n",
       "      <td>QTD</td>\n",
       "      <td>YTD</td>\n",
       "      <td>Yr High</td>\n",
       "      <td>Yr Low</td>\n",
       "      <td>Low-high</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MXWD Index</th>\n",
       "      <td>MSCI AC World</td>\n",
       "      <td>425.32</td>\n",
       "      <td>-0.00342097</td>\n",
       "      <td>0.0141638</td>\n",
       "      <td>-0.00342097</td>\n",
       "      <td>-0.00561115</td>\n",
       "      <td>0.0196586</td>\n",
       "      <td>442.7</td>\n",
       "      <td>392.13</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SPX Index</th>\n",
       "      <td>S&amp;P 500</td>\n",
       "      <td>2098.04</td>\n",
       "      <td>-0.00275686</td>\n",
       "      <td>0.0147028</td>\n",
       "      <td>-0.00275686</td>\n",
       "      <td>0.0150366</td>\n",
       "      <td>0.0190102</td>\n",
       "      <td>2130.82</td>\n",
       "      <td>1862.49</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SX5E Index</th>\n",
       "      <td>Euro Stoxx 50</td>\n",
       "      <td>3635.4</td>\n",
       "      <td>0.00963982</td>\n",
       "      <td>0.0348126</td>\n",
       "      <td>0.00963982</td>\n",
       "      <td>-0.0214978</td>\n",
       "      <td>0.155405</td>\n",
       "      <td>3828.78</td>\n",
       "      <td>2874.65</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DAX Index</th>\n",
       "      <td>DAX</td>\n",
       "      <td>11443.7</td>\n",
       "      <td>0.0119135</td>\n",
       "      <td>0.0350313</td>\n",
       "      <td>0.0119135</td>\n",
       "      <td>-0.0437581</td>\n",
       "      <td>0.167066</td>\n",
       "      <td>12374.7</td>\n",
       "      <td>8571.95</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NKY Index</th>\n",
       "      <td>Nikkei 225</td>\n",
       "      <td>20548.1</td>\n",
       "      <td>-0.00180372</td>\n",
       "      <td>0.00973017</td>\n",
       "      <td>-0.00180372</td>\n",
       "      <td>0.0572691</td>\n",
       "      <td>0.17749</td>\n",
       "      <td>20868</td>\n",
       "      <td>14532.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SHCOMP Index</th>\n",
       "      <td>상해</td>\n",
       "      <td>3622.91</td>\n",
       "      <td>-0.0111417</td>\n",
       "      <td>-0.0275537</td>\n",
       "      <td>-0.0111417</td>\n",
       "      <td>-0.062378</td>\n",
       "      <td>0.120021</td>\n",
       "      <td>5166.35</td>\n",
       "      <td>2187.67</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NaN</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>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NaN</th>\n",
       "      <td>Fixed Income</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>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NaN</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Last Price(%)</td>\n",
       "      <td>D-1</td>\n",
       "      <td>D-7</td>\n",
       "      <td>MTD</td>\n",
       "      <td>QTD</td>\n",
       "      <td>YTD</td>\n",
       "      <td>Yr High</td>\n",
       "      <td>Yr Low</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>USGG10YR Index</th>\n",
       "      <td>UST 10Y</td>\n",
       "      <td>2.148</td>\n",
       "      <td>-3</td>\n",
       "      <td>-6.95</td>\n",
       "      <td>-3.21</td>\n",
       "      <td>30.91</td>\n",
       "      <td>-2.32</td>\n",
       "      <td>2.6198</td>\n",
       "      <td>1.6407</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>USGG2YR Index</th>\n",
       "      <td>UST 2Y</td>\n",
       "      <td>0.6646</td>\n",
       "      <td>0</td>\n",
       "      <td>1.51</td>\n",
       "      <td>0.4</td>\n",
       "      <td>18.84</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.7393</td>\n",
       "      <td>0.3076</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>USGG10YR-USGG2YR</th>\n",
       "      <td>Spread(10Y-2Y)</td>\n",
       "      <td>1.4834</td>\n",
       "      <td>-3</td>\n",
       "      <td>-8.46</td>\n",
       "      <td>-3.61</td>\n",
       "      <td>12.07</td>\n",
       "      <td>-2.33</td>\n",
       "      <td>2.0562</td>\n",
       "      <td>1.1919</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GECU10YR Index</th>\n",
       "      <td>Bund 10Y</td>\n",
       "      <td>0.628</td>\n",
       "      <td>-1</td>\n",
       "      <td>-6.3</td>\n",
       "      <td>-1.6</td>\n",
       "      <td>43.4</td>\n",
       "      <td>8.7</td>\n",
       "      <td>1.167</td>\n",
       "      <td>0.075</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GECU2YR Index</th>\n",
       "      <td>Bund 2Y</td>\n",
       "      <td>-0.244</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.8</td>\n",
       "      <td>-0.9</td>\n",
       "      <td>1.3</td>\n",
       "      <td>-13.4</td>\n",
       "      <td>0.027</td>\n",
       "      <td>-0.283</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GECU10YR - GECU2YR</th>\n",
       "      <td>Spread(10Y-2Y)</td>\n",
       "      <td>0.872</td>\n",
       "      <td>-1</td>\n",
       "      <td>-4.5</td>\n",
       "      <td>-0.7</td>\n",
       "      <td>42.1</td>\n",
       "      <td>22.1</td>\n",
       "      <td>1.157</td>\n",
       "      <td>0.342</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NaN</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>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NaN</th>\n",
       "      <td>FX</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>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NaN</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Last Price</td>\n",
       "      <td>D-1</td>\n",
       "      <td>D-7</td>\n",
       "      <td>MTD</td>\n",
       "      <td>QTD</td>\n",
       "      <td>YTD</td>\n",
       "      <td>Yr High</td>\n",
       "      <td>Yr Low</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DXY Curncy</th>\n",
       "      <td>Dollar Index</td>\n",
       "      <td>97.494</td>\n",
       "      <td>0.00162324</td>\n",
       "      <td>0.0102796</td>\n",
       "      <td>0.00162324</td>\n",
       "      <td>0.00982961</td>\n",
       "      <td>0.0800386</td>\n",
       "      <td>100.33</td>\n",
       "      <td>81.328</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EURUSD Curncy</th>\n",
       "      <td>EUR</td>\n",
       "      <td>1.095</td>\n",
       "      <td>-0.00309541</td>\n",
       "      <td>-0.0124459</td>\n",
       "      <td>-0.00309541</td>\n",
       "      <td>-0.00173215</td>\n",
       "      <td>-0.0948917</td>\n",
       "      <td>1.3422</td>\n",
       "      <td>1.0496</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GBPUSD Curncy</th>\n",
       "      <td>GBP</td>\n",
       "      <td>1.5587</td>\n",
       "      <td>-0.00224043</td>\n",
       "      <td>0.0017996</td>\n",
       "      <td>-0.00224043</td>\n",
       "      <td>0.0447051</td>\n",
       "      <td>0.000641972</td>\n",
       "      <td>1.6886</td>\n",
       "      <td>1.4632</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>USDJPY Curncy</th>\n",
       "      <td>JPY</td>\n",
       "      <td>124.03</td>\n",
       "      <td>-0.00112876</td>\n",
       "      <td>-0.0062888</td>\n",
       "      <td>-0.00112876</td>\n",
       "      <td>-0.0407966</td>\n",
       "      <td>-0.0342659</td>\n",
       "      <td>102.03</td>\n",
       "      <td>125.612</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AUDUSD Curncy</th>\n",
       "      <td>AUD</td>\n",
       "      <td>0.7286</td>\n",
       "      <td>-0.0030104</td>\n",
       "      <td>0.0023387</td>\n",
       "      <td>-0.0030104</td>\n",
       "      <td>-0.0454605</td>\n",
       "      <td>-0.108746</td>\n",
       "      <td>0.9378</td>\n",
       "      <td>0.7269</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NaN</th>\n",
       "      <td>* D-1,D-7,MTD,QTD,YTD 변동분은 USD 대비 해당 통화 가치의 변동</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>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NaN</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>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NaN</th>\n",
       "      <td>Commodity</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>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NaN</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Last Price</td>\n",
       "      <td>D-1</td>\n",
       "      <td>D-7</td>\n",
       "      <td>MTD</td>\n",
       "      <td>QTD</td>\n",
       "      <td>YTD</td>\n",
       "      <td>Yr High</td>\n",
       "      <td>Yr Low</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CLA COMDTY</th>\n",
       "      <td>WTI</td>\n",
       "      <td>45.17</td>\n",
       "      <td>-0.0413837</td>\n",
       "      <td>-0.0468453</td>\n",
       "      <td>-0.0413837</td>\n",
       "      <td>-0.154278</td>\n",
       "      <td>-0.158845</td>\n",
       "      <td>93.38</td>\n",
       "      <td>47.12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GCA COMDTY</th>\n",
       "      <td>Gold</td>\n",
       "      <td>1089.4</td>\n",
       "      <td>-0.005205</td>\n",
       "      <td>-0.00683745</td>\n",
       "      <td>-0.005205</td>\n",
       "      <td>-0.0947316</td>\n",
       "      <td>-0.0805975</td>\n",
       "      <td>1321</td>\n",
       "      <td>1086</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C A COMDTY</th>\n",
       "      <td>Corn</td>\n",
       "      <td>376.5</td>\n",
       "      <td>-0.012459</td>\n",
       "      <td>-0.0182529</td>\n",
       "      <td>-0.012459</td>\n",
       "      <td>-0.0828258</td>\n",
       "      <td>-0.0720887</td>\n",
       "      <td>451.75</td>\n",
       "      <td>363.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NaN</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>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NaN</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>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NaN</th>\n",
       "      <td>CDS</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>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NaN</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Last Price</td>\n",
       "      <td>D-1</td>\n",
       "      <td>D-7</td>\n",
       "      <td>MTD</td>\n",
       "      <td>QTD</td>\n",
       "      <td>YTD</td>\n",
       "      <td>Yr High</td>\n",
       "      <td>Yr Low</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CCHIN1U5 CBIT CURNCY</th>\n",
       "      <td>China</td>\n",
       "      <td>101</td>\n",
       "      <td>0.831</td>\n",
       "      <td>-0.501</td>\n",
       "      <td>0.831</td>\n",
       "      <td>8.333</td>\n",
       "      <td>14.013</td>\n",
       "      <td>104.336</td>\n",
       "      <td>66.838</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CINO1U5 CBIT CURNCY</th>\n",
       "      <td>Indonesia</td>\n",
       "      <td>186.757</td>\n",
       "      <td>3.249</td>\n",
       "      <td>5.545</td>\n",
       "      <td>3.249</td>\n",
       "      <td>30.208</td>\n",
       "      <td>26.445</td>\n",
       "      <td>183.748</td>\n",
       "      <td>127.162</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CMLAY1U5 CBIT CURNCY</th>\n",
       "      <td>Malaysia</td>\n",
       "      <td>152.159</td>\n",
       "      <td>4.173</td>\n",
       "      <td>10.277</td>\n",
       "      <td>4.173</td>\n",
       "      <td>24.706</td>\n",
       "      <td>45.332</td>\n",
       "      <td>151.325</td>\n",
       "      <td>73.677</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CTHAI1U5 CBIT CURNCY</th>\n",
       "      <td>Thailand</td>\n",
       "      <td>109</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5.5</td>\n",
       "      <td>6</td>\n",
       "      <td>119.511</td>\n",
       "      <td>80.84</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cruss1u5 cbit curncy</th>\n",
       "      <td>Russia</td>\n",
       "      <td>345.773</td>\n",
       "      <td>6.039</td>\n",
       "      <td>3.628</td>\n",
       "      <td>6.039</td>\n",
       "      <td>-27.167</td>\n",
       "      <td>-126.441</td>\n",
       "      <td>628.765</td>\n",
       "      <td>221.835</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CSOAF1U5 CBIT CURNCY</th>\n",
       "      <td>South Africa</td>\n",
       "      <td>219.178</td>\n",
       "      <td>-0.717</td>\n",
       "      <td>-0.887</td>\n",
       "      <td>-0.717</td>\n",
       "      <td>11.862</td>\n",
       "      <td>29.295</td>\n",
       "      <td>240.794</td>\n",
       "      <td>159</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CTURK1U5 CBIT CURNCY</th>\n",
       "      <td>Turkey</td>\n",
       "      <td>236.839</td>\n",
       "      <td>-1.569</td>\n",
       "      <td>6.529</td>\n",
       "      <td>-1.569</td>\n",
       "      <td>20.735</td>\n",
       "      <td>60.005</td>\n",
       "      <td>238.408</td>\n",
       "      <td>156</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CBRZ1U5 CBIT CURNCY</th>\n",
       "      <td>Brazil</td>\n",
       "      <td>292.362</td>\n",
       "      <td>4.642</td>\n",
       "      <td>-4.678</td>\n",
       "      <td>4.642</td>\n",
       "      <td>27.912</td>\n",
       "      <td>95.564</td>\n",
       "      <td>306.576</td>\n",
       "      <td>123.25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CKREA1U5 CBIT CURNCY</th>\n",
       "      <td>Korea</td>\n",
       "      <td>54.165</td>\n",
       "      <td>0.331</td>\n",
       "      <td>-0.751</td>\n",
       "      <td>0.331</td>\n",
       "      <td>2.08</td>\n",
       "      <td>-0.326</td>\n",
       "      <td>67.609</td>\n",
       "      <td>45.491</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NaN</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>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NaN</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Last Price</td>\n",
       "      <td>D-1</td>\n",
       "      <td>D-7</td>\n",
       "      <td>MTD</td>\n",
       "      <td>QTD</td>\n",
       "      <td>YTD</td>\n",
       "      <td>Yr High</td>\n",
       "      <td>Yr Low</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NaN</th>\n",
       "      <td>Japan</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.713</td>\n",
       "      <td>0</td>\n",
       "      <td>4.511</td>\n",
       "      <td>-27.5</td>\n",
       "      <td>73.833</td>\n",
       "      <td>31.335</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NaN</th>\n",
       "      <td>Italy</td>\n",
       "      <td>114.915</td>\n",
       "      <td>-0.611</td>\n",
       "      <td>-0.215</td>\n",
       "      <td>-0.611</td>\n",
       "      <td>6.397</td>\n",
       "      <td>-24.382</td>\n",
       "      <td>160.16</td>\n",
       "      <td>84.16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NaN</th>\n",
       "      <td>Spain</td>\n",
       "      <td>94.888</td>\n",
       "      <td>-0.057</td>\n",
       "      <td>2.668</td>\n",
       "      <td>-0.057</td>\n",
       "      <td>9.649</td>\n",
       "      <td>-2.852</td>\n",
       "      <td>130.438</td>\n",
       "      <td>56.493</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              Global Market Daily Update  \\\n",
       "NaN NaN                                                              NaN   \n",
       "    NaN                                                              NaN   \n",
       "    NaN                                                           Equity   \n",
       "    NaN                                                              NaN   \n",
       "    MXWD Index                                             MSCI AC World   \n",
       "    SPX Index                                                    S&P 500   \n",
       "    SX5E Index                                             Euro Stoxx 50   \n",
       "    DAX Index                                                        DAX   \n",
       "    NKY Index                                                 Nikkei 225   \n",
       "    SHCOMP Index                                                      상해   \n",
       "    NaN                                                              NaN   \n",
       "    NaN                                                     Fixed Income   \n",
       "    NaN                                                              NaN   \n",
       "    USGG10YR Index                                               UST 10Y   \n",
       "    USGG2YR Index                                                 UST 2Y   \n",
       "    USGG10YR-USGG2YR                                      Spread(10Y-2Y)   \n",
       "    GECU10YR Index                                              Bund 10Y   \n",
       "    GECU2YR Index                                                Bund 2Y   \n",
       "    GECU10YR - GECU2YR                                    Spread(10Y-2Y)   \n",
       "    NaN                                                              NaN   \n",
       "    NaN                                                               FX   \n",
       "    NaN                                                              NaN   \n",
       "    DXY Curncy                                              Dollar Index   \n",
       "    EURUSD Curncy                                                    EUR   \n",
       "    GBPUSD Curncy                                                    GBP   \n",
       "    USDJPY Curncy                                                    JPY   \n",
       "    AUDUSD Curncy                                                    AUD   \n",
       "    NaN                   * D-1,D-7,MTD,QTD,YTD 변동분은 USD 대비 해당 통화 가치의 변동   \n",
       "    NaN                                                              NaN   \n",
       "    NaN                                                       Commodity    \n",
       "    NaN                                                              NaN   \n",
       "    CLA COMDTY                                                       WTI   \n",
       "    GCA COMDTY                                                      Gold   \n",
       "    C A COMDTY                                                      Corn   \n",
       "    NaN                                                              NaN   \n",
       "    NaN                                                              NaN   \n",
       "    NaN                                                              CDS   \n",
       "    NaN                                                              NaN   \n",
       "    CCHIN1U5 CBIT CURNCY                                           China   \n",
       "    CINO1U5 CBIT CURNCY                                        Indonesia   \n",
       "    CMLAY1U5 CBIT CURNCY                                        Malaysia   \n",
       "    CTHAI1U5 CBIT CURNCY                                        Thailand   \n",
       "    cruss1u5 cbit curncy                                          Russia   \n",
       "    CSOAF1U5 CBIT CURNCY                                    South Africa   \n",
       "    CTURK1U5 CBIT CURNCY                                          Turkey   \n",
       "    CBRZ1U5 CBIT CURNCY                                           Brazil   \n",
       "    CKREA1U5 CBIT CURNCY                                           Korea   \n",
       "    NaN                                                              NaN   \n",
       "    NaN                                                              NaN   \n",
       "    NaN                                                            Japan   \n",
       "    NaN                                                            Italy   \n",
       "    NaN                                                            Spain   \n",
       "\n",
       "                             Unnamed: 1  Unnamed: 2  Unnamed: 3  Unnamed: 4  \\\n",
       "NaN NaN                             NaN         NaN         NaN         NaN   \n",
       "    NaN                             NaN         NaN         NaN         NaN   \n",
       "    NaN                             NaN         NaN         NaN         NaN   \n",
       "    NaN                      Last Price         D-1         D-7         MTD   \n",
       "    MXWD Index                   425.32 -0.00342097   0.0141638 -0.00342097   \n",
       "    SPX Index                   2098.04 -0.00275686   0.0147028 -0.00275686   \n",
       "    SX5E Index                   3635.4  0.00963982   0.0348126  0.00963982   \n",
       "    DAX Index                   11443.7   0.0119135   0.0350313   0.0119135   \n",
       "    NKY Index                   20548.1 -0.00180372  0.00973017 -0.00180372   \n",
       "    SHCOMP Index                3622.91  -0.0111417  -0.0275537  -0.0111417   \n",
       "    NaN                             NaN         NaN         NaN         NaN   \n",
       "    NaN                             NaN         NaN         NaN         NaN   \n",
       "    NaN                   Last Price(%)         D-1         D-7         MTD   \n",
       "    USGG10YR Index                2.148          -3       -6.95       -3.21   \n",
       "    USGG2YR Index                0.6646           0        1.51         0.4   \n",
       "    USGG10YR-USGG2YR             1.4834          -3       -8.46       -3.61   \n",
       "    GECU10YR Index                0.628          -1        -6.3        -1.6   \n",
       "    GECU2YR Index                -0.244           0        -1.8        -0.9   \n",
       "    GECU10YR - GECU2YR            0.872          -1        -4.5        -0.7   \n",
       "    NaN                             NaN         NaN         NaN         NaN   \n",
       "    NaN                             NaN         NaN         NaN         NaN   \n",
       "    NaN                      Last Price         D-1         D-7         MTD   \n",
       "    DXY Curncy                   97.494  0.00162324   0.0102796  0.00162324   \n",
       "    EURUSD Curncy                 1.095 -0.00309541  -0.0124459 -0.00309541   \n",
       "    GBPUSD Curncy                1.5587 -0.00224043   0.0017996 -0.00224043   \n",
       "    USDJPY Curncy                124.03 -0.00112876  -0.0062888 -0.00112876   \n",
       "    AUDUSD Curncy                0.7286  -0.0030104   0.0023387  -0.0030104   \n",
       "    NaN                             NaN         NaN         NaN         NaN   \n",
       "    NaN                             NaN         NaN         NaN         NaN   \n",
       "    NaN                             NaN         NaN         NaN         NaN   \n",
       "    NaN                      Last Price         D-1         D-7         MTD   \n",
       "    CLA COMDTY                    45.17  -0.0413837  -0.0468453  -0.0413837   \n",
       "    GCA COMDTY                   1089.4   -0.005205 -0.00683745   -0.005205   \n",
       "    C A COMDTY                    376.5   -0.012459  -0.0182529   -0.012459   \n",
       "    NaN                             NaN         NaN         NaN         NaN   \n",
       "    NaN                             NaN         NaN         NaN         NaN   \n",
       "    NaN                             NaN         NaN         NaN         NaN   \n",
       "    NaN                      Last Price         D-1         D-7         MTD   \n",
       "    CCHIN1U5 CBIT CURNCY            101       0.831      -0.501       0.831   \n",
       "    CINO1U5 CBIT CURNCY         186.757       3.249       5.545       3.249   \n",
       "    CMLAY1U5 CBIT CURNCY        152.159       4.173      10.277       4.173   \n",
       "    CTHAI1U5 CBIT CURNCY            109           2           1           2   \n",
       "    cruss1u5 cbit curncy        345.773       6.039       3.628       6.039   \n",
       "    CSOAF1U5 CBIT CURNCY        219.178      -0.717      -0.887      -0.717   \n",
       "    CTURK1U5 CBIT CURNCY        236.839      -1.569       6.529      -1.569   \n",
       "    CBRZ1U5 CBIT CURNCY         292.362       4.642      -4.678       4.642   \n",
       "    CKREA1U5 CBIT CURNCY         54.165       0.331      -0.751       0.331   \n",
       "    NaN                             NaN         NaN         NaN         NaN   \n",
       "    NaN                      Last Price         D-1         D-7         MTD   \n",
       "    NaN                              40           0      -0.713           0   \n",
       "    NaN                         114.915      -0.611      -0.215      -0.611   \n",
       "    NaN                          94.888      -0.057       2.668      -0.057   \n",
       "\n",
       "                          Unnamed: 5       As of: 2014-10-20 00:00:00  \\\n",
       "NaN NaN                          NaN          NaN                 NaN   \n",
       "    NaN                          NaN          NaN                 NaN   \n",
       "    NaN                          NaN          NaN                 NaN   \n",
       "    NaN                          QTD          YTD             Yr High   \n",
       "    MXWD Index           -0.00561115    0.0196586               442.7   \n",
       "    SPX Index              0.0150366    0.0190102             2130.82   \n",
       "    SX5E Index            -0.0214978     0.155405             3828.78   \n",
       "    DAX Index             -0.0437581     0.167066             12374.7   \n",
       "    NKY Index              0.0572691      0.17749               20868   \n",
       "    SHCOMP Index           -0.062378     0.120021             5166.35   \n",
       "    NaN                          NaN          NaN                 NaN   \n",
       "    NaN                          NaN          NaN                 NaN   \n",
       "    NaN                          QTD          YTD             Yr High   \n",
       "    USGG10YR Index             30.91        -2.32              2.6198   \n",
       "    USGG2YR Index              18.84         0.01              0.7393   \n",
       "    USGG10YR-USGG2YR           12.07        -2.33              2.0562   \n",
       "    GECU10YR Index              43.4          8.7               1.167   \n",
       "    GECU2YR Index                1.3        -13.4               0.027   \n",
       "    GECU10YR - GECU2YR          42.1         22.1               1.157   \n",
       "    NaN                          NaN          NaN                 NaN   \n",
       "    NaN                          NaN          NaN                 NaN   \n",
       "    NaN                          QTD          YTD             Yr High   \n",
       "    DXY Curncy            0.00982961    0.0800386              100.33   \n",
       "    EURUSD Curncy        -0.00173215   -0.0948917              1.3422   \n",
       "    GBPUSD Curncy          0.0447051  0.000641972              1.6886   \n",
       "    USDJPY Curncy         -0.0407966   -0.0342659              102.03   \n",
       "    AUDUSD Curncy         -0.0454605    -0.108746              0.9378   \n",
       "    NaN                          NaN          NaN                 NaN   \n",
       "    NaN                          NaN          NaN                 NaN   \n",
       "    NaN                          NaN          NaN                 NaN   \n",
       "    NaN                          QTD          YTD             Yr High   \n",
       "    CLA COMDTY             -0.154278    -0.158845               93.38   \n",
       "    GCA COMDTY            -0.0947316   -0.0805975                1321   \n",
       "    C A COMDTY            -0.0828258   -0.0720887              451.75   \n",
       "    NaN                          NaN          NaN                 NaN   \n",
       "    NaN                          NaN          NaN                 NaN   \n",
       "    NaN                          NaN          NaN                 NaN   \n",
       "    NaN                          QTD          YTD             Yr High   \n",
       "    CCHIN1U5 CBIT CURNCY       8.333       14.013             104.336   \n",
       "    CINO1U5 CBIT CURNCY       30.208       26.445             183.748   \n",
       "    CMLAY1U5 CBIT CURNCY      24.706       45.332             151.325   \n",
       "    CTHAI1U5 CBIT CURNCY         5.5            6             119.511   \n",
       "    cruss1u5 cbit curncy     -27.167     -126.441             628.765   \n",
       "    CSOAF1U5 CBIT CURNCY      11.862       29.295             240.794   \n",
       "    CTURK1U5 CBIT CURNCY      20.735       60.005             238.408   \n",
       "    CBRZ1U5 CBIT CURNCY       27.912       95.564             306.576   \n",
       "    CKREA1U5 CBIT CURNCY        2.08       -0.326              67.609   \n",
       "    NaN                          NaN          NaN                 NaN   \n",
       "    NaN                          QTD          YTD             Yr High   \n",
       "    NaN                        4.511        -27.5              73.833   \n",
       "    NaN                        6.397      -24.382              160.16   \n",
       "    NaN                        9.649       -2.852             130.438   \n",
       "\n",
       "                         2014-10-18 00:00:00 Unnamed: 9  2015-08-03 00:00:00  \n",
       "NaN NaN                                  NaN        NaN                  NaN  \n",
       "    NaN                                  NaN        NaN                  NaN  \n",
       "    NaN                                  NaN        NaN                  NaN  \n",
       "    NaN                               Yr Low   Low-high                  NaN  \n",
       "    MXWD Index                        392.13        NaN                  NaN  \n",
       "    SPX Index                        1862.49        NaN                  NaN  \n",
       "    SX5E Index                       2874.65        NaN                  NaN  \n",
       "    DAX Index                        8571.95        NaN                  NaN  \n",
       "    NKY Index                        14532.5        NaN                  NaN  \n",
       "    SHCOMP Index                     2187.67        NaN                  NaN  \n",
       "    NaN                                  NaN        NaN                  NaN  \n",
       "    NaN                                  NaN        NaN                  NaN  \n",
       "    NaN                               Yr Low        NaN                  NaN  \n",
       "    USGG10YR Index                    1.6407        NaN                  NaN  \n",
       "    USGG2YR Index                     0.3076        NaN                  NaN  \n",
       "    USGG10YR-USGG2YR                  1.1919        NaN                  NaN  \n",
       "    GECU10YR Index                     0.075        NaN                  NaN  \n",
       "    GECU2YR Index                     -0.283        NaN                  NaN  \n",
       "    GECU10YR - GECU2YR                 0.342        NaN                  NaN  \n",
       "    NaN                                  NaN        NaN                  NaN  \n",
       "    NaN                                  NaN        NaN                  NaN  \n",
       "    NaN                               Yr Low        NaN                  NaN  \n",
       "    DXY Curncy                        81.328        NaN                  NaN  \n",
       "    EURUSD Curncy                     1.0496        NaN                  NaN  \n",
       "    GBPUSD Curncy                     1.4632        NaN                  NaN  \n",
       "    USDJPY Curncy                    125.612        NaN                  NaN  \n",
       "    AUDUSD Curncy                     0.7269        NaN                  NaN  \n",
       "    NaN                                  NaN        NaN                  NaN  \n",
       "    NaN                                  NaN        NaN                  NaN  \n",
       "    NaN                                  NaN        NaN                  NaN  \n",
       "    NaN                               Yr Low        NaN                  NaN  \n",
       "    CLA COMDTY                         47.12        NaN                  NaN  \n",
       "    GCA COMDTY                          1086        NaN                  NaN  \n",
       "    C A COMDTY                         363.5        NaN                  NaN  \n",
       "    NaN                                  NaN        NaN                  NaN  \n",
       "    NaN                                  NaN        NaN                  NaN  \n",
       "    NaN                                  NaN        NaN                  NaN  \n",
       "    NaN                               Yr Low        NaN                  NaN  \n",
       "    CCHIN1U5 CBIT CURNCY              66.838        NaN                  NaN  \n",
       "    CINO1U5 CBIT CURNCY              127.162        NaN                  NaN  \n",
       "    CMLAY1U5 CBIT CURNCY              73.677        NaN                  NaN  \n",
       "    CTHAI1U5 CBIT CURNCY               80.84        NaN                  NaN  \n",
       "    cruss1u5 cbit curncy             221.835        NaN                  NaN  \n",
       "    CSOAF1U5 CBIT CURNCY                 159        NaN                  NaN  \n",
       "    CTURK1U5 CBIT CURNCY                 156        NaN                  NaN  \n",
       "    CBRZ1U5 CBIT CURNCY               123.25        NaN                  NaN  \n",
       "    CKREA1U5 CBIT CURNCY              45.491        NaN                  NaN  \n",
       "    NaN                                  NaN        NaN                  NaN  \n",
       "    NaN                               Yr Low        NaN                  NaN  \n",
       "    NaN                               31.335        NaN                  NaN  \n",
       "    NaN                                84.16        NaN                  NaN  \n",
       "    NaN                               56.493        NaN                  NaN  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_excel(파일경로)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Unnamed: 1</th>\n",
       "      <th>Equity</th>\n",
       "      <th>Unnamed: 3</th>\n",
       "      <th>Unnamed: 4</th>\n",
       "      <th>Unnamed: 5</th>\n",
       "      <th>Unnamed: 6</th>\n",
       "      <th>Unnamed: 7</th>\n",
       "      <th>Unnamed: 8</th>\n",
       "      <th>Unnamed: 9</th>\n",
       "      <th>Unnamed: 10</th>\n",
       "      <th>Unnamed: 11</th>\n",
       "      <th>Unnamed: 12</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>Last Price</td>\n",
       "      <td>D-1</td>\n",
       "      <td>D-7</td>\n",
       "      <td>MTD</td>\n",
       "      <td>QTD</td>\n",
       "      <td>YTD</td>\n",
       "      <td>Yr High</td>\n",
       "      <td>Yr Low</td>\n",
       "      <td>Low-high</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>MXWD Index</td>\n",
       "      <td>MSCI AC World</td>\n",
       "      <td>425.32</td>\n",
       "      <td>-0.00342097</td>\n",
       "      <td>0.0141638</td>\n",
       "      <td>-0.00342097</td>\n",
       "      <td>-0.00561115</td>\n",
       "      <td>0.0196586</td>\n",
       "      <td>442.7</td>\n",
       "      <td>392.13</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>SPX Index</td>\n",
       "      <td>S&amp;P 500</td>\n",
       "      <td>2098.04</td>\n",
       "      <td>-0.00275686</td>\n",
       "      <td>0.0147028</td>\n",
       "      <td>-0.00275686</td>\n",
       "      <td>0.0150366</td>\n",
       "      <td>0.0190102</td>\n",
       "      <td>2130.82</td>\n",
       "      <td>1862.49</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>SX5E Index</td>\n",
       "      <td>Euro Stoxx 50</td>\n",
       "      <td>3635.4</td>\n",
       "      <td>0.00963982</td>\n",
       "      <td>0.0348126</td>\n",
       "      <td>0.00963982</td>\n",
       "      <td>-0.0214978</td>\n",
       "      <td>0.155405</td>\n",
       "      <td>3828.78</td>\n",
       "      <td>2874.65</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>DAX Index</td>\n",
       "      <td>DAX</td>\n",
       "      <td>11443.7</td>\n",
       "      <td>0.0119135</td>\n",
       "      <td>0.0350313</td>\n",
       "      <td>0.0119135</td>\n",
       "      <td>-0.0437581</td>\n",
       "      <td>0.167066</td>\n",
       "      <td>12374.7</td>\n",
       "      <td>8571.95</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NKY Index</td>\n",
       "      <td>Nikkei 225</td>\n",
       "      <td>20548.1</td>\n",
       "      <td>-0.00180372</td>\n",
       "      <td>0.00973017</td>\n",
       "      <td>-0.00180372</td>\n",
       "      <td>0.0572691</td>\n",
       "      <td>0.17749</td>\n",
       "      <td>20868</td>\n",
       "      <td>14532.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>NaN</td>\n",
       "      <td>SHCOMP Index</td>\n",
       "      <td>상해</td>\n",
       "      <td>3622.91</td>\n",
       "      <td>-0.0111417</td>\n",
       "      <td>-0.0275537</td>\n",
       "      <td>-0.0111417</td>\n",
       "      <td>-0.062378</td>\n",
       "      <td>0.120021</td>\n",
       "      <td>5166.35</td>\n",
       "      <td>2187.67</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Fixed Income</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>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Last Price(%)</td>\n",
       "      <td>D-1</td>\n",
       "      <td>D-7</td>\n",
       "      <td>MTD</td>\n",
       "      <td>QTD</td>\n",
       "      <td>YTD</td>\n",
       "      <td>Yr High</td>\n",
       "      <td>Yr Low</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>NaN</td>\n",
       "      <td>USGG10YR Index</td>\n",
       "      <td>UST 10Y</td>\n",
       "      <td>2.148</td>\n",
       "      <td>-3</td>\n",
       "      <td>-6.95</td>\n",
       "      <td>-3.21</td>\n",
       "      <td>30.91</td>\n",
       "      <td>-2.32</td>\n",
       "      <td>2.6198</td>\n",
       "      <td>1.6407</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>NaN</td>\n",
       "      <td>USGG2YR Index</td>\n",
       "      <td>UST 2Y</td>\n",
       "      <td>0.6646</td>\n",
       "      <td>0</td>\n",
       "      <td>1.51</td>\n",
       "      <td>0.4</td>\n",
       "      <td>18.84</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.7393</td>\n",
       "      <td>0.3076</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>NaN</td>\n",
       "      <td>USGG10YR-USGG2YR</td>\n",
       "      <td>Spread(10Y-2Y)</td>\n",
       "      <td>1.4834</td>\n",
       "      <td>-3</td>\n",
       "      <td>-8.46</td>\n",
       "      <td>-3.61</td>\n",
       "      <td>12.07</td>\n",
       "      <td>-2.33</td>\n",
       "      <td>2.0562</td>\n",
       "      <td>1.1919</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>NaN</td>\n",
       "      <td>GECU10YR Index</td>\n",
       "      <td>Bund 10Y</td>\n",
       "      <td>0.628</td>\n",
       "      <td>-1</td>\n",
       "      <td>-6.3</td>\n",
       "      <td>-1.6</td>\n",
       "      <td>43.4</td>\n",
       "      <td>8.7</td>\n",
       "      <td>1.167</td>\n",
       "      <td>0.075</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>NaN</td>\n",
       "      <td>GECU2YR Index</td>\n",
       "      <td>Bund 2Y</td>\n",
       "      <td>-0.244</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.8</td>\n",
       "      <td>-0.9</td>\n",
       "      <td>1.3</td>\n",
       "      <td>-13.4</td>\n",
       "      <td>0.027</td>\n",
       "      <td>-0.283</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>NaN</td>\n",
       "      <td>GECU10YR - GECU2YR</td>\n",
       "      <td>Spread(10Y-2Y)</td>\n",
       "      <td>0.872</td>\n",
       "      <td>-1</td>\n",
       "      <td>-4.5</td>\n",
       "      <td>-0.7</td>\n",
       "      <td>42.1</td>\n",
       "      <td>22.1</td>\n",
       "      <td>1.157</td>\n",
       "      <td>0.342</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FX</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>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Last Price</td>\n",
       "      <td>D-1</td>\n",
       "      <td>D-7</td>\n",
       "      <td>MTD</td>\n",
       "      <td>QTD</td>\n",
       "      <td>YTD</td>\n",
       "      <td>Yr High</td>\n",
       "      <td>Yr Low</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>NaN</td>\n",
       "      <td>DXY Curncy</td>\n",
       "      <td>Dollar Index</td>\n",
       "      <td>97.494</td>\n",
       "      <td>0.00162324</td>\n",
       "      <td>0.0102796</td>\n",
       "      <td>0.00162324</td>\n",
       "      <td>0.00982961</td>\n",
       "      <td>0.0800386</td>\n",
       "      <td>100.33</td>\n",
       "      <td>81.328</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>NaN</td>\n",
       "      <td>EURUSD Curncy</td>\n",
       "      <td>EUR</td>\n",
       "      <td>1.095</td>\n",
       "      <td>-0.00309541</td>\n",
       "      <td>-0.0124459</td>\n",
       "      <td>-0.00309541</td>\n",
       "      <td>-0.00173215</td>\n",
       "      <td>-0.0948917</td>\n",
       "      <td>1.3422</td>\n",
       "      <td>1.0496</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>NaN</td>\n",
       "      <td>GBPUSD Curncy</td>\n",
       "      <td>GBP</td>\n",
       "      <td>1.5587</td>\n",
       "      <td>-0.00224043</td>\n",
       "      <td>0.0017996</td>\n",
       "      <td>-0.00224043</td>\n",
       "      <td>0.0447051</td>\n",
       "      <td>0.000641972</td>\n",
       "      <td>1.6886</td>\n",
       "      <td>1.4632</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>NaN</td>\n",
       "      <td>USDJPY Curncy</td>\n",
       "      <td>JPY</td>\n",
       "      <td>124.03</td>\n",
       "      <td>-0.00112876</td>\n",
       "      <td>-0.0062888</td>\n",
       "      <td>-0.00112876</td>\n",
       "      <td>-0.0407966</td>\n",
       "      <td>-0.0342659</td>\n",
       "      <td>102.03</td>\n",
       "      <td>125.612</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>NaN</td>\n",
       "      <td>AUDUSD Curncy</td>\n",
       "      <td>AUD</td>\n",
       "      <td>0.7286</td>\n",
       "      <td>-0.0030104</td>\n",
       "      <td>0.0023387</td>\n",
       "      <td>-0.0030104</td>\n",
       "      <td>-0.0454605</td>\n",
       "      <td>-0.108746</td>\n",
       "      <td>0.9378</td>\n",
       "      <td>0.7269</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>* D-1,D-7,MTD,QTD,YTD 변동분은 USD 대비 해당 통화 가치의 변동</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>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Commodity</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>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Last Price</td>\n",
       "      <td>D-1</td>\n",
       "      <td>D-7</td>\n",
       "      <td>MTD</td>\n",
       "      <td>QTD</td>\n",
       "      <td>YTD</td>\n",
       "      <td>Yr High</td>\n",
       "      <td>Yr Low</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>NaN</td>\n",
       "      <td>CLA COMDTY</td>\n",
       "      <td>WTI</td>\n",
       "      <td>45.17</td>\n",
       "      <td>-0.0413837</td>\n",
       "      <td>-0.0468453</td>\n",
       "      <td>-0.0413837</td>\n",
       "      <td>-0.154278</td>\n",
       "      <td>-0.158845</td>\n",
       "      <td>93.38</td>\n",
       "      <td>47.12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>NaN</td>\n",
       "      <td>GCA COMDTY</td>\n",
       "      <td>Gold</td>\n",
       "      <td>1089.4</td>\n",
       "      <td>-0.005205</td>\n",
       "      <td>-0.00683745</td>\n",
       "      <td>-0.005205</td>\n",
       "      <td>-0.0947316</td>\n",
       "      <td>-0.0805975</td>\n",
       "      <td>1321</td>\n",
       "      <td>1086</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>NaN</td>\n",
       "      <td>C A COMDTY</td>\n",
       "      <td>Corn</td>\n",
       "      <td>376.5</td>\n",
       "      <td>-0.012459</td>\n",
       "      <td>-0.0182529</td>\n",
       "      <td>-0.012459</td>\n",
       "      <td>-0.0828258</td>\n",
       "      <td>-0.0720887</td>\n",
       "      <td>451.75</td>\n",
       "      <td>363.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CDS</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>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Last Price</td>\n",
       "      <td>D-1</td>\n",
       "      <td>D-7</td>\n",
       "      <td>MTD</td>\n",
       "      <td>QTD</td>\n",
       "      <td>YTD</td>\n",
       "      <td>Yr High</td>\n",
       "      <td>Yr Low</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>NaN</td>\n",
       "      <td>CCHIN1U5 CBIT CURNCY</td>\n",
       "      <td>China</td>\n",
       "      <td>101</td>\n",
       "      <td>0.831</td>\n",
       "      <td>-0.501</td>\n",
       "      <td>0.831</td>\n",
       "      <td>8.333</td>\n",
       "      <td>14.013</td>\n",
       "      <td>104.336</td>\n",
       "      <td>66.838</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>NaN</td>\n",
       "      <td>CINO1U5 CBIT CURNCY</td>\n",
       "      <td>Indonesia</td>\n",
       "      <td>186.757</td>\n",
       "      <td>3.249</td>\n",
       "      <td>5.545</td>\n",
       "      <td>3.249</td>\n",
       "      <td>30.208</td>\n",
       "      <td>26.445</td>\n",
       "      <td>183.748</td>\n",
       "      <td>127.162</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>NaN</td>\n",
       "      <td>CMLAY1U5 CBIT CURNCY</td>\n",
       "      <td>Malaysia</td>\n",
       "      <td>152.159</td>\n",
       "      <td>4.173</td>\n",
       "      <td>10.277</td>\n",
       "      <td>4.173</td>\n",
       "      <td>24.706</td>\n",
       "      <td>45.332</td>\n",
       "      <td>151.325</td>\n",
       "      <td>73.677</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>NaN</td>\n",
       "      <td>CTHAI1U5 CBIT CURNCY</td>\n",
       "      <td>Thailand</td>\n",
       "      <td>109</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5.5</td>\n",
       "      <td>6</td>\n",
       "      <td>119.511</td>\n",
       "      <td>80.84</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>NaN</td>\n",
       "      <td>cruss1u5 cbit curncy</td>\n",
       "      <td>Russia</td>\n",
       "      <td>345.773</td>\n",
       "      <td>6.039</td>\n",
       "      <td>3.628</td>\n",
       "      <td>6.039</td>\n",
       "      <td>-27.167</td>\n",
       "      <td>-126.441</td>\n",
       "      <td>628.765</td>\n",
       "      <td>221.835</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>NaN</td>\n",
       "      <td>CSOAF1U5 CBIT CURNCY</td>\n",
       "      <td>South Africa</td>\n",
       "      <td>219.178</td>\n",
       "      <td>-0.717</td>\n",
       "      <td>-0.887</td>\n",
       "      <td>-0.717</td>\n",
       "      <td>11.862</td>\n",
       "      <td>29.295</td>\n",
       "      <td>240.794</td>\n",
       "      <td>159</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>NaN</td>\n",
       "      <td>CTURK1U5 CBIT CURNCY</td>\n",
       "      <td>Turkey</td>\n",
       "      <td>236.839</td>\n",
       "      <td>-1.569</td>\n",
       "      <td>6.529</td>\n",
       "      <td>-1.569</td>\n",
       "      <td>20.735</td>\n",
       "      <td>60.005</td>\n",
       "      <td>238.408</td>\n",
       "      <td>156</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>NaN</td>\n",
       "      <td>CBRZ1U5 CBIT CURNCY</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>292.362</td>\n",
       "      <td>4.642</td>\n",
       "      <td>-4.678</td>\n",
       "      <td>4.642</td>\n",
       "      <td>27.912</td>\n",
       "      <td>95.564</td>\n",
       "      <td>306.576</td>\n",
       "      <td>123.25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>NaN</td>\n",
       "      <td>CKREA1U5 CBIT CURNCY</td>\n",
       "      <td>Korea</td>\n",
       "      <td>54.165</td>\n",
       "      <td>0.331</td>\n",
       "      <td>-0.751</td>\n",
       "      <td>0.331</td>\n",
       "      <td>2.08</td>\n",
       "      <td>-0.326</td>\n",
       "      <td>67.609</td>\n",
       "      <td>45.491</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Last Price</td>\n",
       "      <td>D-1</td>\n",
       "      <td>D-7</td>\n",
       "      <td>MTD</td>\n",
       "      <td>QTD</td>\n",
       "      <td>YTD</td>\n",
       "      <td>Yr High</td>\n",
       "      <td>Yr Low</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Japan</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.713</td>\n",
       "      <td>0</td>\n",
       "      <td>4.511</td>\n",
       "      <td>-27.5</td>\n",
       "      <td>73.833</td>\n",
       "      <td>31.335</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Italy</td>\n",
       "      <td>114.915</td>\n",
       "      <td>-0.611</td>\n",
       "      <td>-0.215</td>\n",
       "      <td>-0.611</td>\n",
       "      <td>6.397</td>\n",
       "      <td>-24.382</td>\n",
       "      <td>160.16</td>\n",
       "      <td>84.16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Spain</td>\n",
       "      <td>94.888</td>\n",
       "      <td>-0.057</td>\n",
       "      <td>2.668</td>\n",
       "      <td>-0.057</td>\n",
       "      <td>9.649</td>\n",
       "      <td>-2.852</td>\n",
       "      <td>130.438</td>\n",
       "      <td>56.493</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Unnamed: 0            Unnamed: 1  \\\n",
       "0          NaN                   NaN   \n",
       "1          NaN            MXWD Index   \n",
       "2          NaN             SPX Index   \n",
       "3          NaN            SX5E Index   \n",
       "4          NaN             DAX Index   \n",
       "5          NaN             NKY Index   \n",
       "6          NaN          SHCOMP Index   \n",
       "7          NaN                   NaN   \n",
       "8          NaN                   NaN   \n",
       "9          NaN                   NaN   \n",
       "10         NaN        USGG10YR Index   \n",
       "11         NaN         USGG2YR Index   \n",
       "12         NaN      USGG10YR-USGG2YR   \n",
       "13         NaN        GECU10YR Index   \n",
       "14         NaN         GECU2YR Index   \n",
       "15         NaN    GECU10YR - GECU2YR   \n",
       "16         NaN                   NaN   \n",
       "17         NaN                   NaN   \n",
       "18         NaN                   NaN   \n",
       "19         NaN            DXY Curncy   \n",
       "20         NaN         EURUSD Curncy   \n",
       "21         NaN         GBPUSD Curncy   \n",
       "22         NaN         USDJPY Curncy   \n",
       "23         NaN         AUDUSD Curncy   \n",
       "24         NaN                   NaN   \n",
       "25         NaN                   NaN   \n",
       "26         NaN                   NaN   \n",
       "27         NaN                   NaN   \n",
       "28         NaN            CLA COMDTY   \n",
       "29         NaN            GCA COMDTY   \n",
       "30         NaN            C A COMDTY   \n",
       "31         NaN                   NaN   \n",
       "32         NaN                   NaN   \n",
       "33         NaN                   NaN   \n",
       "34         NaN                   NaN   \n",
       "35         NaN  CCHIN1U5 CBIT CURNCY   \n",
       "36         NaN   CINO1U5 CBIT CURNCY   \n",
       "37         NaN  CMLAY1U5 CBIT CURNCY   \n",
       "38         NaN  CTHAI1U5 CBIT CURNCY   \n",
       "39         NaN  cruss1u5 cbit curncy   \n",
       "40         NaN  CSOAF1U5 CBIT CURNCY   \n",
       "41         NaN  CTURK1U5 CBIT CURNCY   \n",
       "42         NaN   CBRZ1U5 CBIT CURNCY   \n",
       "43         NaN  CKREA1U5 CBIT CURNCY   \n",
       "44         NaN                   NaN   \n",
       "45         NaN                   NaN   \n",
       "46         NaN                   NaN   \n",
       "47         NaN                   NaN   \n",
       "48         NaN                   NaN   \n",
       "\n",
       "                                            Equity     Unnamed: 3  Unnamed: 4  \\\n",
       "0                                              NaN     Last Price         D-1   \n",
       "1                                    MSCI AC World         425.32 -0.00342097   \n",
       "2                                          S&P 500        2098.04 -0.00275686   \n",
       "3                                    Euro Stoxx 50         3635.4  0.00963982   \n",
       "4                                              DAX        11443.7   0.0119135   \n",
       "5                                       Nikkei 225        20548.1 -0.00180372   \n",
       "6                                               상해        3622.91  -0.0111417   \n",
       "7                                              NaN            NaN         NaN   \n",
       "8                                     Fixed Income            NaN         NaN   \n",
       "9                                              NaN  Last Price(%)         D-1   \n",
       "10                                         UST 10Y          2.148          -3   \n",
       "11                                          UST 2Y         0.6646           0   \n",
       "12                                  Spread(10Y-2Y)         1.4834          -3   \n",
       "13                                        Bund 10Y          0.628          -1   \n",
       "14                                         Bund 2Y         -0.244           0   \n",
       "15                                  Spread(10Y-2Y)          0.872          -1   \n",
       "16                                             NaN            NaN         NaN   \n",
       "17                                              FX            NaN         NaN   \n",
       "18                                             NaN     Last Price         D-1   \n",
       "19                                    Dollar Index         97.494  0.00162324   \n",
       "20                                             EUR          1.095 -0.00309541   \n",
       "21                                             GBP         1.5587 -0.00224043   \n",
       "22                                             JPY         124.03 -0.00112876   \n",
       "23                                             AUD         0.7286  -0.0030104   \n",
       "24  * D-1,D-7,MTD,QTD,YTD 변동분은 USD 대비 해당 통화 가치의 변동            NaN         NaN   \n",
       "25                                             NaN            NaN         NaN   \n",
       "26                                      Commodity             NaN         NaN   \n",
       "27                                             NaN     Last Price         D-1   \n",
       "28                                             WTI          45.17  -0.0413837   \n",
       "29                                            Gold         1089.4   -0.005205   \n",
       "30                                            Corn          376.5   -0.012459   \n",
       "31                                             NaN            NaN         NaN   \n",
       "32                                             NaN            NaN         NaN   \n",
       "33                                             CDS            NaN         NaN   \n",
       "34                                             NaN     Last Price         D-1   \n",
       "35                                           China            101       0.831   \n",
       "36                                       Indonesia        186.757       3.249   \n",
       "37                                        Malaysia        152.159       4.173   \n",
       "38                                        Thailand            109           2   \n",
       "39                                          Russia        345.773       6.039   \n",
       "40                                    South Africa        219.178      -0.717   \n",
       "41                                          Turkey        236.839      -1.569   \n",
       "42                                          Brazil        292.362       4.642   \n",
       "43                                           Korea         54.165       0.331   \n",
       "44                                             NaN            NaN         NaN   \n",
       "45                                             NaN     Last Price         D-1   \n",
       "46                                           Japan             40           0   \n",
       "47                                           Italy        114.915      -0.611   \n",
       "48                                           Spain         94.888      -0.057   \n",
       "\n",
       "    Unnamed: 5  Unnamed: 6  Unnamed: 7   Unnamed: 8 Unnamed: 9 Unnamed: 10  \\\n",
       "0          D-7         MTD         QTD          YTD    Yr High      Yr Low   \n",
       "1    0.0141638 -0.00342097 -0.00561115    0.0196586      442.7      392.13   \n",
       "2    0.0147028 -0.00275686   0.0150366    0.0190102    2130.82     1862.49   \n",
       "3    0.0348126  0.00963982  -0.0214978     0.155405    3828.78     2874.65   \n",
       "4    0.0350313   0.0119135  -0.0437581     0.167066    12374.7     8571.95   \n",
       "5   0.00973017 -0.00180372   0.0572691      0.17749      20868     14532.5   \n",
       "6   -0.0275537  -0.0111417   -0.062378     0.120021    5166.35     2187.67   \n",
       "7          NaN         NaN         NaN          NaN        NaN         NaN   \n",
       "8          NaN         NaN         NaN          NaN        NaN         NaN   \n",
       "9          D-7         MTD         QTD          YTD    Yr High      Yr Low   \n",
       "10       -6.95       -3.21       30.91        -2.32     2.6198      1.6407   \n",
       "11        1.51         0.4       18.84         0.01     0.7393      0.3076   \n",
       "12       -8.46       -3.61       12.07        -2.33     2.0562      1.1919   \n",
       "13        -6.3        -1.6        43.4          8.7      1.167       0.075   \n",
       "14        -1.8        -0.9         1.3        -13.4      0.027      -0.283   \n",
       "15        -4.5        -0.7        42.1         22.1      1.157       0.342   \n",
       "16         NaN         NaN         NaN          NaN        NaN         NaN   \n",
       "17         NaN         NaN         NaN          NaN        NaN         NaN   \n",
       "18         D-7         MTD         QTD          YTD    Yr High      Yr Low   \n",
       "19   0.0102796  0.00162324  0.00982961    0.0800386     100.33      81.328   \n",
       "20  -0.0124459 -0.00309541 -0.00173215   -0.0948917     1.3422      1.0496   \n",
       "21   0.0017996 -0.00224043   0.0447051  0.000641972     1.6886      1.4632   \n",
       "22  -0.0062888 -0.00112876  -0.0407966   -0.0342659     102.03     125.612   \n",
       "23   0.0023387  -0.0030104  -0.0454605    -0.108746     0.9378      0.7269   \n",
       "24         NaN         NaN         NaN          NaN        NaN         NaN   \n",
       "25         NaN         NaN         NaN          NaN        NaN         NaN   \n",
       "26         NaN         NaN         NaN          NaN        NaN         NaN   \n",
       "27         D-7         MTD         QTD          YTD    Yr High      Yr Low   \n",
       "28  -0.0468453  -0.0413837   -0.154278    -0.158845      93.38       47.12   \n",
       "29 -0.00683745   -0.005205  -0.0947316   -0.0805975       1321        1086   \n",
       "30  -0.0182529   -0.012459  -0.0828258   -0.0720887     451.75       363.5   \n",
       "31         NaN         NaN         NaN          NaN        NaN         NaN   \n",
       "32         NaN         NaN         NaN          NaN        NaN         NaN   \n",
       "33         NaN         NaN         NaN          NaN        NaN         NaN   \n",
       "34         D-7         MTD         QTD          YTD    Yr High      Yr Low   \n",
       "35      -0.501       0.831       8.333       14.013    104.336      66.838   \n",
       "36       5.545       3.249      30.208       26.445    183.748     127.162   \n",
       "37      10.277       4.173      24.706       45.332    151.325      73.677   \n",
       "38           1           2         5.5            6    119.511       80.84   \n",
       "39       3.628       6.039     -27.167     -126.441    628.765     221.835   \n",
       "40      -0.887      -0.717      11.862       29.295    240.794         159   \n",
       "41       6.529      -1.569      20.735       60.005    238.408         156   \n",
       "42      -4.678       4.642      27.912       95.564    306.576      123.25   \n",
       "43      -0.751       0.331        2.08       -0.326     67.609      45.491   \n",
       "44         NaN         NaN         NaN          NaN        NaN         NaN   \n",
       "45         D-7         MTD         QTD          YTD    Yr High      Yr Low   \n",
       "46      -0.713           0       4.511        -27.5     73.833      31.335   \n",
       "47      -0.215      -0.611       6.397      -24.382     160.16       84.16   \n",
       "48       2.668      -0.057       9.649       -2.852    130.438      56.493   \n",
       "\n",
       "   Unnamed: 11  Unnamed: 12  \n",
       "0     Low-high          NaN  \n",
       "1          NaN          NaN  \n",
       "2          NaN          NaN  \n",
       "3          NaN          NaN  \n",
       "4          NaN          NaN  \n",
       "5          NaN          NaN  \n",
       "6          NaN          NaN  \n",
       "7          NaN          NaN  \n",
       "8          NaN          NaN  \n",
       "9          NaN          NaN  \n",
       "10         NaN          NaN  \n",
       "11         NaN          NaN  \n",
       "12         NaN          NaN  \n",
       "13         NaN          NaN  \n",
       "14         NaN          NaN  \n",
       "15         NaN          NaN  \n",
       "16         NaN          NaN  \n",
       "17         NaN          NaN  \n",
       "18         NaN          NaN  \n",
       "19         NaN          NaN  \n",
       "20         NaN          NaN  \n",
       "21         NaN          NaN  \n",
       "22         NaN          NaN  \n",
       "23         NaN          NaN  \n",
       "24         NaN          NaN  \n",
       "25         NaN          NaN  \n",
       "26         NaN          NaN  \n",
       "27         NaN          NaN  \n",
       "28         NaN          NaN  \n",
       "29         NaN          NaN  \n",
       "30         NaN          NaN  \n",
       "31         NaN          NaN  \n",
       "32         NaN          NaN  \n",
       "33         NaN          NaN  \n",
       "34         NaN          NaN  \n",
       "35         NaN          NaN  \n",
       "36         NaN          NaN  \n",
       "37         NaN          NaN  \n",
       "38         NaN          NaN  \n",
       "39         NaN          NaN  \n",
       "40         NaN          NaN  \n",
       "41         NaN          NaN  \n",
       "42         NaN          NaN  \n",
       "43         NaN          NaN  \n",
       "44         NaN          NaN  \n",
       "45         NaN          NaN  \n",
       "46         NaN          NaN  \n",
       "47         NaN          NaN  \n",
       "48         NaN          NaN  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_excel(파일경로, skiprows=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>Equity</th>\n",
       "      <th>Unnamed: 1</th>\n",
       "      <th>Unnamed: 2</th>\n",
       "      <th>Unnamed: 3</th>\n",
       "      <th>Unnamed: 4</th>\n",
       "      <th>Unnamed: 5</th>\n",
       "      <th>Unnamed: 6</th>\n",
       "      <th>Unnamed: 7</th>\n",
       "      <th>Unnamed: 8</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Last Price</td>\n",
       "      <td>D-1</td>\n",
       "      <td>D-7</td>\n",
       "      <td>MTD</td>\n",
       "      <td>QTD</td>\n",
       "      <td>YTD</td>\n",
       "      <td>Yr High</td>\n",
       "      <td>Yr Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>MSCI AC World</td>\n",
       "      <td>425.32</td>\n",
       "      <td>-0.00342097</td>\n",
       "      <td>0.0141638</td>\n",
       "      <td>-0.00342097</td>\n",
       "      <td>-0.00561115</td>\n",
       "      <td>0.0196586</td>\n",
       "      <td>442.7</td>\n",
       "      <td>392.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>S&amp;P 500</td>\n",
       "      <td>2098.04</td>\n",
       "      <td>-0.00275686</td>\n",
       "      <td>0.0147028</td>\n",
       "      <td>-0.00275686</td>\n",
       "      <td>0.0150366</td>\n",
       "      <td>0.0190102</td>\n",
       "      <td>2130.82</td>\n",
       "      <td>1862.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Euro Stoxx 50</td>\n",
       "      <td>3635.4</td>\n",
       "      <td>0.00963982</td>\n",
       "      <td>0.0348126</td>\n",
       "      <td>0.00963982</td>\n",
       "      <td>-0.0214978</td>\n",
       "      <td>0.155405</td>\n",
       "      <td>3828.78</td>\n",
       "      <td>2874.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>DAX</td>\n",
       "      <td>11443.7</td>\n",
       "      <td>0.0119135</td>\n",
       "      <td>0.0350313</td>\n",
       "      <td>0.0119135</td>\n",
       "      <td>-0.0437581</td>\n",
       "      <td>0.167066</td>\n",
       "      <td>12374.7</td>\n",
       "      <td>8571.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Nikkei 225</td>\n",
       "      <td>20548.1</td>\n",
       "      <td>-0.00180372</td>\n",
       "      <td>0.00973017</td>\n",
       "      <td>-0.00180372</td>\n",
       "      <td>0.0572691</td>\n",
       "      <td>0.17749</td>\n",
       "      <td>20868</td>\n",
       "      <td>14532.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>상해</td>\n",
       "      <td>3622.91</td>\n",
       "      <td>-0.0111417</td>\n",
       "      <td>-0.0275537</td>\n",
       "      <td>-0.0111417</td>\n",
       "      <td>-0.062378</td>\n",
       "      <td>0.120021</td>\n",
       "      <td>5166.35</td>\n",
       "      <td>2187.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Fixed Income</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Last Price(%)</td>\n",
       "      <td>D-1</td>\n",
       "      <td>D-7</td>\n",
       "      <td>MTD</td>\n",
       "      <td>QTD</td>\n",
       "      <td>YTD</td>\n",
       "      <td>Yr High</td>\n",
       "      <td>Yr Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>UST 10Y</td>\n",
       "      <td>2.148</td>\n",
       "      <td>-3</td>\n",
       "      <td>-6.95</td>\n",
       "      <td>-3.21</td>\n",
       "      <td>30.91</td>\n",
       "      <td>-2.32</td>\n",
       "      <td>2.6198</td>\n",
       "      <td>1.6407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>UST 2Y</td>\n",
       "      <td>0.6646</td>\n",
       "      <td>0</td>\n",
       "      <td>1.51</td>\n",
       "      <td>0.4</td>\n",
       "      <td>18.84</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.7393</td>\n",
       "      <td>0.3076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Spread(10Y-2Y)</td>\n",
       "      <td>1.4834</td>\n",
       "      <td>-3</td>\n",
       "      <td>-8.46</td>\n",
       "      <td>-3.61</td>\n",
       "      <td>12.07</td>\n",
       "      <td>-2.33</td>\n",
       "      <td>2.0562</td>\n",
       "      <td>1.1919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Bund 10Y</td>\n",
       "      <td>0.628</td>\n",
       "      <td>-1</td>\n",
       "      <td>-6.3</td>\n",
       "      <td>-1.6</td>\n",
       "      <td>43.4</td>\n",
       "      <td>8.7</td>\n",
       "      <td>1.167</td>\n",
       "      <td>0.075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Bund 2Y</td>\n",
       "      <td>-0.244</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.8</td>\n",
       "      <td>-0.9</td>\n",
       "      <td>1.3</td>\n",
       "      <td>-13.4</td>\n",
       "      <td>0.027</td>\n",
       "      <td>-0.283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Spread(10Y-2Y)</td>\n",
       "      <td>0.872</td>\n",
       "      <td>-1</td>\n",
       "      <td>-4.5</td>\n",
       "      <td>-0.7</td>\n",
       "      <td>42.1</td>\n",
       "      <td>22.1</td>\n",
       "      <td>1.157</td>\n",
       "      <td>0.342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>FX</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Last Price</td>\n",
       "      <td>D-1</td>\n",
       "      <td>D-7</td>\n",
       "      <td>MTD</td>\n",
       "      <td>QTD</td>\n",
       "      <td>YTD</td>\n",
       "      <td>Yr High</td>\n",
       "      <td>Yr Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Dollar Index</td>\n",
       "      <td>97.494</td>\n",
       "      <td>0.00162324</td>\n",
       "      <td>0.0102796</td>\n",
       "      <td>0.00162324</td>\n",
       "      <td>0.00982961</td>\n",
       "      <td>0.0800386</td>\n",
       "      <td>100.33</td>\n",
       "      <td>81.328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>EUR</td>\n",
       "      <td>1.095</td>\n",
       "      <td>-0.00309541</td>\n",
       "      <td>-0.0124459</td>\n",
       "      <td>-0.00309541</td>\n",
       "      <td>-0.00173215</td>\n",
       "      <td>-0.0948917</td>\n",
       "      <td>1.3422</td>\n",
       "      <td>1.0496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>GBP</td>\n",
       "      <td>1.5587</td>\n",
       "      <td>-0.00224043</td>\n",
       "      <td>0.0017996</td>\n",
       "      <td>-0.00224043</td>\n",
       "      <td>0.0447051</td>\n",
       "      <td>0.000641972</td>\n",
       "      <td>1.6886</td>\n",
       "      <td>1.4632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>JPY</td>\n",
       "      <td>124.03</td>\n",
       "      <td>-0.00112876</td>\n",
       "      <td>-0.0062888</td>\n",
       "      <td>-0.00112876</td>\n",
       "      <td>-0.0407966</td>\n",
       "      <td>-0.0342659</td>\n",
       "      <td>102.03</td>\n",
       "      <td>125.612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>AUD</td>\n",
       "      <td>0.7286</td>\n",
       "      <td>-0.0030104</td>\n",
       "      <td>0.0023387</td>\n",
       "      <td>-0.0030104</td>\n",
       "      <td>-0.0454605</td>\n",
       "      <td>-0.108746</td>\n",
       "      <td>0.9378</td>\n",
       "      <td>0.7269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>* D-1,D-7,MTD,QTD,YTD 변동분은 USD 대비 해당 통화 가치의 변동</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Commodity</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Last Price</td>\n",
       "      <td>D-1</td>\n",
       "      <td>D-7</td>\n",
       "      <td>MTD</td>\n",
       "      <td>QTD</td>\n",
       "      <td>YTD</td>\n",
       "      <td>Yr High</td>\n",
       "      <td>Yr Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>WTI</td>\n",
       "      <td>45.17</td>\n",
       "      <td>-0.0413837</td>\n",
       "      <td>-0.0468453</td>\n",
       "      <td>-0.0413837</td>\n",
       "      <td>-0.154278</td>\n",
       "      <td>-0.158845</td>\n",
       "      <td>93.38</td>\n",
       "      <td>47.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Gold</td>\n",
       "      <td>1089.4</td>\n",
       "      <td>-0.005205</td>\n",
       "      <td>-0.00683745</td>\n",
       "      <td>-0.005205</td>\n",
       "      <td>-0.0947316</td>\n",
       "      <td>-0.0805975</td>\n",
       "      <td>1321</td>\n",
       "      <td>1086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Corn</td>\n",
       "      <td>376.5</td>\n",
       "      <td>-0.012459</td>\n",
       "      <td>-0.0182529</td>\n",
       "      <td>-0.012459</td>\n",
       "      <td>-0.0828258</td>\n",
       "      <td>-0.0720887</td>\n",
       "      <td>451.75</td>\n",
       "      <td>363.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>CDS</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Last Price</td>\n",
       "      <td>D-1</td>\n",
       "      <td>D-7</td>\n",
       "      <td>MTD</td>\n",
       "      <td>QTD</td>\n",
       "      <td>YTD</td>\n",
       "      <td>Yr High</td>\n",
       "      <td>Yr Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>China</td>\n",
       "      <td>101</td>\n",
       "      <td>0.831</td>\n",
       "      <td>-0.501</td>\n",
       "      <td>0.831</td>\n",
       "      <td>8.333</td>\n",
       "      <td>14.013</td>\n",
       "      <td>104.336</td>\n",
       "      <td>66.838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Indonesia</td>\n",
       "      <td>186.757</td>\n",
       "      <td>3.249</td>\n",
       "      <td>5.545</td>\n",
       "      <td>3.249</td>\n",
       "      <td>30.208</td>\n",
       "      <td>26.445</td>\n",
       "      <td>183.748</td>\n",
       "      <td>127.162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Malaysia</td>\n",
       "      <td>152.159</td>\n",
       "      <td>4.173</td>\n",
       "      <td>10.277</td>\n",
       "      <td>4.173</td>\n",
       "      <td>24.706</td>\n",
       "      <td>45.332</td>\n",
       "      <td>151.325</td>\n",
       "      <td>73.677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Thailand</td>\n",
       "      <td>109</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5.5</td>\n",
       "      <td>6</td>\n",
       "      <td>119.511</td>\n",
       "      <td>80.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Russia</td>\n",
       "      <td>345.773</td>\n",
       "      <td>6.039</td>\n",
       "      <td>3.628</td>\n",
       "      <td>6.039</td>\n",
       "      <td>-27.167</td>\n",
       "      <td>-126.441</td>\n",
       "      <td>628.765</td>\n",
       "      <td>221.835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>South Africa</td>\n",
       "      <td>219.178</td>\n",
       "      <td>-0.717</td>\n",
       "      <td>-0.887</td>\n",
       "      <td>-0.717</td>\n",
       "      <td>11.862</td>\n",
       "      <td>29.295</td>\n",
       "      <td>240.794</td>\n",
       "      <td>159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>Turkey</td>\n",
       "      <td>236.839</td>\n",
       "      <td>-1.569</td>\n",
       "      <td>6.529</td>\n",
       "      <td>-1.569</td>\n",
       "      <td>20.735</td>\n",
       "      <td>60.005</td>\n",
       "      <td>238.408</td>\n",
       "      <td>156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>Brazil</td>\n",
       "      <td>292.362</td>\n",
       "      <td>4.642</td>\n",
       "      <td>-4.678</td>\n",
       "      <td>4.642</td>\n",
       "      <td>27.912</td>\n",
       "      <td>95.564</td>\n",
       "      <td>306.576</td>\n",
       "      <td>123.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>Korea</td>\n",
       "      <td>54.165</td>\n",
       "      <td>0.331</td>\n",
       "      <td>-0.751</td>\n",
       "      <td>0.331</td>\n",
       "      <td>2.08</td>\n",
       "      <td>-0.326</td>\n",
       "      <td>67.609</td>\n",
       "      <td>45.491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Last Price</td>\n",
       "      <td>D-1</td>\n",
       "      <td>D-7</td>\n",
       "      <td>MTD</td>\n",
       "      <td>QTD</td>\n",
       "      <td>YTD</td>\n",
       "      <td>Yr High</td>\n",
       "      <td>Yr Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>Japan</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.713</td>\n",
       "      <td>0</td>\n",
       "      <td>4.511</td>\n",
       "      <td>-27.5</td>\n",
       "      <td>73.833</td>\n",
       "      <td>31.335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>Italy</td>\n",
       "      <td>114.915</td>\n",
       "      <td>-0.611</td>\n",
       "      <td>-0.215</td>\n",
       "      <td>-0.611</td>\n",
       "      <td>6.397</td>\n",
       "      <td>-24.382</td>\n",
       "      <td>160.16</td>\n",
       "      <td>84.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>Spain</td>\n",
       "      <td>94.888</td>\n",
       "      <td>-0.057</td>\n",
       "      <td>2.668</td>\n",
       "      <td>-0.057</td>\n",
       "      <td>9.649</td>\n",
       "      <td>-2.852</td>\n",
       "      <td>130.438</td>\n",
       "      <td>56.493</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            Equity     Unnamed: 1  Unnamed: 2  \\\n",
       "0                                              NaN     Last Price         D-1   \n",
       "1                                    MSCI AC World         425.32 -0.00342097   \n",
       "2                                          S&P 500        2098.04 -0.00275686   \n",
       "3                                    Euro Stoxx 50         3635.4  0.00963982   \n",
       "4                                              DAX        11443.7   0.0119135   \n",
       "5                                       Nikkei 225        20548.1 -0.00180372   \n",
       "6                                               상해        3622.91  -0.0111417   \n",
       "7                                              NaN            NaN         NaN   \n",
       "8                                     Fixed Income            NaN         NaN   \n",
       "9                                              NaN  Last Price(%)         D-1   \n",
       "10                                         UST 10Y          2.148          -3   \n",
       "11                                          UST 2Y         0.6646           0   \n",
       "12                                  Spread(10Y-2Y)         1.4834          -3   \n",
       "13                                        Bund 10Y          0.628          -1   \n",
       "14                                         Bund 2Y         -0.244           0   \n",
       "15                                  Spread(10Y-2Y)          0.872          -1   \n",
       "16                                             NaN            NaN         NaN   \n",
       "17                                              FX            NaN         NaN   \n",
       "18                                             NaN     Last Price         D-1   \n",
       "19                                    Dollar Index         97.494  0.00162324   \n",
       "20                                             EUR          1.095 -0.00309541   \n",
       "21                                             GBP         1.5587 -0.00224043   \n",
       "22                                             JPY         124.03 -0.00112876   \n",
       "23                                             AUD         0.7286  -0.0030104   \n",
       "24  * D-1,D-7,MTD,QTD,YTD 변동분은 USD 대비 해당 통화 가치의 변동            NaN         NaN   \n",
       "25                                             NaN            NaN         NaN   \n",
       "26                                      Commodity             NaN         NaN   \n",
       "27                                             NaN     Last Price         D-1   \n",
       "28                                             WTI          45.17  -0.0413837   \n",
       "29                                            Gold         1089.4   -0.005205   \n",
       "30                                            Corn          376.5   -0.012459   \n",
       "31                                             NaN            NaN         NaN   \n",
       "32                                             NaN            NaN         NaN   \n",
       "33                                             CDS            NaN         NaN   \n",
       "34                                             NaN     Last Price         D-1   \n",
       "35                                           China            101       0.831   \n",
       "36                                       Indonesia        186.757       3.249   \n",
       "37                                        Malaysia        152.159       4.173   \n",
       "38                                        Thailand            109           2   \n",
       "39                                          Russia        345.773       6.039   \n",
       "40                                    South Africa        219.178      -0.717   \n",
       "41                                          Turkey        236.839      -1.569   \n",
       "42                                          Brazil        292.362       4.642   \n",
       "43                                           Korea         54.165       0.331   \n",
       "44                                             NaN            NaN         NaN   \n",
       "45                                             NaN     Last Price         D-1   \n",
       "46                                           Japan             40           0   \n",
       "47                                           Italy        114.915      -0.611   \n",
       "48                                           Spain         94.888      -0.057   \n",
       "\n",
       "    Unnamed: 3  Unnamed: 4  Unnamed: 5   Unnamed: 6 Unnamed: 7 Unnamed: 8  \n",
       "0          D-7         MTD         QTD          YTD    Yr High     Yr Low  \n",
       "1    0.0141638 -0.00342097 -0.00561115    0.0196586      442.7     392.13  \n",
       "2    0.0147028 -0.00275686   0.0150366    0.0190102    2130.82    1862.49  \n",
       "3    0.0348126  0.00963982  -0.0214978     0.155405    3828.78    2874.65  \n",
       "4    0.0350313   0.0119135  -0.0437581     0.167066    12374.7    8571.95  \n",
       "5   0.00973017 -0.00180372   0.0572691      0.17749      20868    14532.5  \n",
       "6   -0.0275537  -0.0111417   -0.062378     0.120021    5166.35    2187.67  \n",
       "7          NaN         NaN         NaN          NaN        NaN        NaN  \n",
       "8          NaN         NaN         NaN          NaN        NaN        NaN  \n",
       "9          D-7         MTD         QTD          YTD    Yr High     Yr Low  \n",
       "10       -6.95       -3.21       30.91        -2.32     2.6198     1.6407  \n",
       "11        1.51         0.4       18.84         0.01     0.7393     0.3076  \n",
       "12       -8.46       -3.61       12.07        -2.33     2.0562     1.1919  \n",
       "13        -6.3        -1.6        43.4          8.7      1.167      0.075  \n",
       "14        -1.8        -0.9         1.3        -13.4      0.027     -0.283  \n",
       "15        -4.5        -0.7        42.1         22.1      1.157      0.342  \n",
       "16         NaN         NaN         NaN          NaN        NaN        NaN  \n",
       "17         NaN         NaN         NaN          NaN        NaN        NaN  \n",
       "18         D-7         MTD         QTD          YTD    Yr High     Yr Low  \n",
       "19   0.0102796  0.00162324  0.00982961    0.0800386     100.33     81.328  \n",
       "20  -0.0124459 -0.00309541 -0.00173215   -0.0948917     1.3422     1.0496  \n",
       "21   0.0017996 -0.00224043   0.0447051  0.000641972     1.6886     1.4632  \n",
       "22  -0.0062888 -0.00112876  -0.0407966   -0.0342659     102.03    125.612  \n",
       "23   0.0023387  -0.0030104  -0.0454605    -0.108746     0.9378     0.7269  \n",
       "24         NaN         NaN         NaN          NaN        NaN        NaN  \n",
       "25         NaN         NaN         NaN          NaN        NaN        NaN  \n",
       "26         NaN         NaN         NaN          NaN        NaN        NaN  \n",
       "27         D-7         MTD         QTD          YTD    Yr High     Yr Low  \n",
       "28  -0.0468453  -0.0413837   -0.154278    -0.158845      93.38      47.12  \n",
       "29 -0.00683745   -0.005205  -0.0947316   -0.0805975       1321       1086  \n",
       "30  -0.0182529   -0.012459  -0.0828258   -0.0720887     451.75      363.5  \n",
       "31         NaN         NaN         NaN          NaN        NaN        NaN  \n",
       "32         NaN         NaN         NaN          NaN        NaN        NaN  \n",
       "33         NaN         NaN         NaN          NaN        NaN        NaN  \n",
       "34         D-7         MTD         QTD          YTD    Yr High     Yr Low  \n",
       "35      -0.501       0.831       8.333       14.013    104.336     66.838  \n",
       "36       5.545       3.249      30.208       26.445    183.748    127.162  \n",
       "37      10.277       4.173      24.706       45.332    151.325     73.677  \n",
       "38           1           2         5.5            6    119.511      80.84  \n",
       "39       3.628       6.039     -27.167     -126.441    628.765    221.835  \n",
       "40      -0.887      -0.717      11.862       29.295    240.794        159  \n",
       "41       6.529      -1.569      20.735       60.005    238.408        156  \n",
       "42      -4.678       4.642      27.912       95.564    306.576     123.25  \n",
       "43      -0.751       0.331        2.08       -0.326     67.609     45.491  \n",
       "44         NaN         NaN         NaN          NaN        NaN        NaN  \n",
       "45         D-7         MTD         QTD          YTD    Yr High     Yr Low  \n",
       "46      -0.713           0       4.511        -27.5     73.833     31.335  \n",
       "47      -0.215      -0.611       6.397      -24.382     160.16      84.16  \n",
       "48       2.668      -0.057       9.649       -2.852    130.438     56.493  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_excel(파일경로, skiprows=3, usecols='C:K')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Equity</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Last Price</td>\n",
       "      <td>D-1</td>\n",
       "      <td>D-7</td>\n",
       "      <td>MTD</td>\n",
       "      <td>QTD</td>\n",
       "      <td>YTD</td>\n",
       "      <td>Yr High</td>\n",
       "      <td>Yr Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>MSCI AC World</td>\n",
       "      <td>425.32</td>\n",
       "      <td>-0.00342097</td>\n",
       "      <td>0.0141638</td>\n",
       "      <td>-0.00342097</td>\n",
       "      <td>-0.00561115</td>\n",
       "      <td>0.0196586</td>\n",
       "      <td>442.7</td>\n",
       "      <td>392.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>S&amp;P 500</td>\n",
       "      <td>2098.04</td>\n",
       "      <td>-0.00275686</td>\n",
       "      <td>0.0147028</td>\n",
       "      <td>-0.00275686</td>\n",
       "      <td>0.0150366</td>\n",
       "      <td>0.0190102</td>\n",
       "      <td>2130.82</td>\n",
       "      <td>1862.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Euro Stoxx 50</td>\n",
       "      <td>3635.4</td>\n",
       "      <td>0.00963982</td>\n",
       "      <td>0.0348126</td>\n",
       "      <td>0.00963982</td>\n",
       "      <td>-0.0214978</td>\n",
       "      <td>0.155405</td>\n",
       "      <td>3828.78</td>\n",
       "      <td>2874.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>DAX</td>\n",
       "      <td>11443.7</td>\n",
       "      <td>0.0119135</td>\n",
       "      <td>0.0350313</td>\n",
       "      <td>0.0119135</td>\n",
       "      <td>-0.0437581</td>\n",
       "      <td>0.167066</td>\n",
       "      <td>12374.7</td>\n",
       "      <td>8571.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Nikkei 225</td>\n",
       "      <td>20548.1</td>\n",
       "      <td>-0.00180372</td>\n",
       "      <td>0.00973017</td>\n",
       "      <td>-0.00180372</td>\n",
       "      <td>0.0572691</td>\n",
       "      <td>0.17749</td>\n",
       "      <td>20868</td>\n",
       "      <td>14532.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>상해</td>\n",
       "      <td>3622.91</td>\n",
       "      <td>-0.0111417</td>\n",
       "      <td>-0.0275537</td>\n",
       "      <td>-0.0111417</td>\n",
       "      <td>-0.062378</td>\n",
       "      <td>0.120021</td>\n",
       "      <td>5166.35</td>\n",
       "      <td>2187.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Fixed Income</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Last Price(%)</td>\n",
       "      <td>D-1</td>\n",
       "      <td>D-7</td>\n",
       "      <td>MTD</td>\n",
       "      <td>QTD</td>\n",
       "      <td>YTD</td>\n",
       "      <td>Yr High</td>\n",
       "      <td>Yr Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>UST 10Y</td>\n",
       "      <td>2.148</td>\n",
       "      <td>-3</td>\n",
       "      <td>-6.95</td>\n",
       "      <td>-3.21</td>\n",
       "      <td>30.91</td>\n",
       "      <td>-2.32</td>\n",
       "      <td>2.6198</td>\n",
       "      <td>1.6407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>UST 2Y</td>\n",
       "      <td>0.6646</td>\n",
       "      <td>0</td>\n",
       "      <td>1.51</td>\n",
       "      <td>0.4</td>\n",
       "      <td>18.84</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.7393</td>\n",
       "      <td>0.3076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Spread(10Y-2Y)</td>\n",
       "      <td>1.4834</td>\n",
       "      <td>-3</td>\n",
       "      <td>-8.46</td>\n",
       "      <td>-3.61</td>\n",
       "      <td>12.07</td>\n",
       "      <td>-2.33</td>\n",
       "      <td>2.0562</td>\n",
       "      <td>1.1919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Bund 10Y</td>\n",
       "      <td>0.628</td>\n",
       "      <td>-1</td>\n",
       "      <td>-6.3</td>\n",
       "      <td>-1.6</td>\n",
       "      <td>43.4</td>\n",
       "      <td>8.7</td>\n",
       "      <td>1.167</td>\n",
       "      <td>0.075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Bund 2Y</td>\n",
       "      <td>-0.244</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.8</td>\n",
       "      <td>-0.9</td>\n",
       "      <td>1.3</td>\n",
       "      <td>-13.4</td>\n",
       "      <td>0.027</td>\n",
       "      <td>-0.283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Spread(10Y-2Y)</td>\n",
       "      <td>0.872</td>\n",
       "      <td>-1</td>\n",
       "      <td>-4.5</td>\n",
       "      <td>-0.7</td>\n",
       "      <td>42.1</td>\n",
       "      <td>22.1</td>\n",
       "      <td>1.157</td>\n",
       "      <td>0.342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>FX</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Last Price</td>\n",
       "      <td>D-1</td>\n",
       "      <td>D-7</td>\n",
       "      <td>MTD</td>\n",
       "      <td>QTD</td>\n",
       "      <td>YTD</td>\n",
       "      <td>Yr High</td>\n",
       "      <td>Yr Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Dollar Index</td>\n",
       "      <td>97.494</td>\n",
       "      <td>0.00162324</td>\n",
       "      <td>0.0102796</td>\n",
       "      <td>0.00162324</td>\n",
       "      <td>0.00982961</td>\n",
       "      <td>0.0800386</td>\n",
       "      <td>100.33</td>\n",
       "      <td>81.328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>EUR</td>\n",
       "      <td>1.095</td>\n",
       "      <td>-0.00309541</td>\n",
       "      <td>-0.0124459</td>\n",
       "      <td>-0.00309541</td>\n",
       "      <td>-0.00173215</td>\n",
       "      <td>-0.0948917</td>\n",
       "      <td>1.3422</td>\n",
       "      <td>1.0496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>GBP</td>\n",
       "      <td>1.5587</td>\n",
       "      <td>-0.00224043</td>\n",
       "      <td>0.0017996</td>\n",
       "      <td>-0.00224043</td>\n",
       "      <td>0.0447051</td>\n",
       "      <td>0.000641972</td>\n",
       "      <td>1.6886</td>\n",
       "      <td>1.4632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>JPY</td>\n",
       "      <td>124.03</td>\n",
       "      <td>-0.00112876</td>\n",
       "      <td>-0.0062888</td>\n",
       "      <td>-0.00112876</td>\n",
       "      <td>-0.0407966</td>\n",
       "      <td>-0.0342659</td>\n",
       "      <td>102.03</td>\n",
       "      <td>125.612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>AUD</td>\n",
       "      <td>0.7286</td>\n",
       "      <td>-0.0030104</td>\n",
       "      <td>0.0023387</td>\n",
       "      <td>-0.0030104</td>\n",
       "      <td>-0.0454605</td>\n",
       "      <td>-0.108746</td>\n",
       "      <td>0.9378</td>\n",
       "      <td>0.7269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>* D-1,D-7,MTD,QTD,YTD 변동분은 USD 대비 해당 통화 가치의 변동</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Commodity</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Last Price</td>\n",
       "      <td>D-1</td>\n",
       "      <td>D-7</td>\n",
       "      <td>MTD</td>\n",
       "      <td>QTD</td>\n",
       "      <td>YTD</td>\n",
       "      <td>Yr High</td>\n",
       "      <td>Yr Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>WTI</td>\n",
       "      <td>45.17</td>\n",
       "      <td>-0.0413837</td>\n",
       "      <td>-0.0468453</td>\n",
       "      <td>-0.0413837</td>\n",
       "      <td>-0.154278</td>\n",
       "      <td>-0.158845</td>\n",
       "      <td>93.38</td>\n",
       "      <td>47.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Gold</td>\n",
       "      <td>1089.4</td>\n",
       "      <td>-0.005205</td>\n",
       "      <td>-0.00683745</td>\n",
       "      <td>-0.005205</td>\n",
       "      <td>-0.0947316</td>\n",
       "      <td>-0.0805975</td>\n",
       "      <td>1321</td>\n",
       "      <td>1086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Corn</td>\n",
       "      <td>376.5</td>\n",
       "      <td>-0.012459</td>\n",
       "      <td>-0.0182529</td>\n",
       "      <td>-0.012459</td>\n",
       "      <td>-0.0828258</td>\n",
       "      <td>-0.0720887</td>\n",
       "      <td>451.75</td>\n",
       "      <td>363.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>CDS</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Last Price</td>\n",
       "      <td>D-1</td>\n",
       "      <td>D-7</td>\n",
       "      <td>MTD</td>\n",
       "      <td>QTD</td>\n",
       "      <td>YTD</td>\n",
       "      <td>Yr High</td>\n",
       "      <td>Yr Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>China</td>\n",
       "      <td>101</td>\n",
       "      <td>0.831</td>\n",
       "      <td>-0.501</td>\n",
       "      <td>0.831</td>\n",
       "      <td>8.333</td>\n",
       "      <td>14.013</td>\n",
       "      <td>104.336</td>\n",
       "      <td>66.838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Indonesia</td>\n",
       "      <td>186.757</td>\n",
       "      <td>3.249</td>\n",
       "      <td>5.545</td>\n",
       "      <td>3.249</td>\n",
       "      <td>30.208</td>\n",
       "      <td>26.445</td>\n",
       "      <td>183.748</td>\n",
       "      <td>127.162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Malaysia</td>\n",
       "      <td>152.159</td>\n",
       "      <td>4.173</td>\n",
       "      <td>10.277</td>\n",
       "      <td>4.173</td>\n",
       "      <td>24.706</td>\n",
       "      <td>45.332</td>\n",
       "      <td>151.325</td>\n",
       "      <td>73.677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Thailand</td>\n",
       "      <td>109</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5.5</td>\n",
       "      <td>6</td>\n",
       "      <td>119.511</td>\n",
       "      <td>80.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>Russia</td>\n",
       "      <td>345.773</td>\n",
       "      <td>6.039</td>\n",
       "      <td>3.628</td>\n",
       "      <td>6.039</td>\n",
       "      <td>-27.167</td>\n",
       "      <td>-126.441</td>\n",
       "      <td>628.765</td>\n",
       "      <td>221.835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>South Africa</td>\n",
       "      <td>219.178</td>\n",
       "      <td>-0.717</td>\n",
       "      <td>-0.887</td>\n",
       "      <td>-0.717</td>\n",
       "      <td>11.862</td>\n",
       "      <td>29.295</td>\n",
       "      <td>240.794</td>\n",
       "      <td>159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>Turkey</td>\n",
       "      <td>236.839</td>\n",
       "      <td>-1.569</td>\n",
       "      <td>6.529</td>\n",
       "      <td>-1.569</td>\n",
       "      <td>20.735</td>\n",
       "      <td>60.005</td>\n",
       "      <td>238.408</td>\n",
       "      <td>156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>Brazil</td>\n",
       "      <td>292.362</td>\n",
       "      <td>4.642</td>\n",
       "      <td>-4.678</td>\n",
       "      <td>4.642</td>\n",
       "      <td>27.912</td>\n",
       "      <td>95.564</td>\n",
       "      <td>306.576</td>\n",
       "      <td>123.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>Korea</td>\n",
       "      <td>54.165</td>\n",
       "      <td>0.331</td>\n",
       "      <td>-0.751</td>\n",
       "      <td>0.331</td>\n",
       "      <td>2.08</td>\n",
       "      <td>-0.326</td>\n",
       "      <td>67.609</td>\n",
       "      <td>45.491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Last Price</td>\n",
       "      <td>D-1</td>\n",
       "      <td>D-7</td>\n",
       "      <td>MTD</td>\n",
       "      <td>QTD</td>\n",
       "      <td>YTD</td>\n",
       "      <td>Yr High</td>\n",
       "      <td>Yr Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>Japan</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.713</td>\n",
       "      <td>0</td>\n",
       "      <td>4.511</td>\n",
       "      <td>-27.5</td>\n",
       "      <td>73.833</td>\n",
       "      <td>31.335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>Italy</td>\n",
       "      <td>114.915</td>\n",
       "      <td>-0.611</td>\n",
       "      <td>-0.215</td>\n",
       "      <td>-0.611</td>\n",
       "      <td>6.397</td>\n",
       "      <td>-24.382</td>\n",
       "      <td>160.16</td>\n",
       "      <td>84.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>Spain</td>\n",
       "      <td>94.888</td>\n",
       "      <td>-0.057</td>\n",
       "      <td>2.668</td>\n",
       "      <td>-0.057</td>\n",
       "      <td>9.649</td>\n",
       "      <td>-2.852</td>\n",
       "      <td>130.438</td>\n",
       "      <td>56.493</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 0              1           2  \\\n",
       "0                                           Equity            NaN         NaN   \n",
       "1                                              NaN     Last Price         D-1   \n",
       "2                                    MSCI AC World         425.32 -0.00342097   \n",
       "3                                          S&P 500        2098.04 -0.00275686   \n",
       "4                                    Euro Stoxx 50         3635.4  0.00963982   \n",
       "5                                              DAX        11443.7   0.0119135   \n",
       "6                                       Nikkei 225        20548.1 -0.00180372   \n",
       "7                                               상해        3622.91  -0.0111417   \n",
       "8                                              NaN            NaN         NaN   \n",
       "9                                     Fixed Income            NaN         NaN   \n",
       "10                                             NaN  Last Price(%)         D-1   \n",
       "11                                         UST 10Y          2.148          -3   \n",
       "12                                          UST 2Y         0.6646           0   \n",
       "13                                  Spread(10Y-2Y)         1.4834          -3   \n",
       "14                                        Bund 10Y          0.628          -1   \n",
       "15                                         Bund 2Y         -0.244           0   \n",
       "16                                  Spread(10Y-2Y)          0.872          -1   \n",
       "17                                             NaN            NaN         NaN   \n",
       "18                                              FX            NaN         NaN   \n",
       "19                                             NaN     Last Price         D-1   \n",
       "20                                    Dollar Index         97.494  0.00162324   \n",
       "21                                             EUR          1.095 -0.00309541   \n",
       "22                                             GBP         1.5587 -0.00224043   \n",
       "23                                             JPY         124.03 -0.00112876   \n",
       "24                                             AUD         0.7286  -0.0030104   \n",
       "25  * D-1,D-7,MTD,QTD,YTD 변동분은 USD 대비 해당 통화 가치의 변동            NaN         NaN   \n",
       "26                                             NaN            NaN         NaN   \n",
       "27                                      Commodity             NaN         NaN   \n",
       "28                                             NaN     Last Price         D-1   \n",
       "29                                             WTI          45.17  -0.0413837   \n",
       "30                                            Gold         1089.4   -0.005205   \n",
       "31                                            Corn          376.5   -0.012459   \n",
       "32                                             NaN            NaN         NaN   \n",
       "33                                             NaN            NaN         NaN   \n",
       "34                                             CDS            NaN         NaN   \n",
       "35                                             NaN     Last Price         D-1   \n",
       "36                                           China            101       0.831   \n",
       "37                                       Indonesia        186.757       3.249   \n",
       "38                                        Malaysia        152.159       4.173   \n",
       "39                                        Thailand            109           2   \n",
       "40                                          Russia        345.773       6.039   \n",
       "41                                    South Africa        219.178      -0.717   \n",
       "42                                          Turkey        236.839      -1.569   \n",
       "43                                          Brazil        292.362       4.642   \n",
       "44                                           Korea         54.165       0.331   \n",
       "45                                             NaN            NaN         NaN   \n",
       "46                                             NaN     Last Price         D-1   \n",
       "47                                           Japan             40           0   \n",
       "48                                           Italy        114.915      -0.611   \n",
       "49                                           Spain         94.888      -0.057   \n",
       "\n",
       "             3           4           5            6        7        8  \n",
       "0          NaN         NaN         NaN          NaN      NaN      NaN  \n",
       "1          D-7         MTD         QTD          YTD  Yr High   Yr Low  \n",
       "2    0.0141638 -0.00342097 -0.00561115    0.0196586    442.7   392.13  \n",
       "3    0.0147028 -0.00275686   0.0150366    0.0190102  2130.82  1862.49  \n",
       "4    0.0348126  0.00963982  -0.0214978     0.155405  3828.78  2874.65  \n",
       "5    0.0350313   0.0119135  -0.0437581     0.167066  12374.7  8571.95  \n",
       "6   0.00973017 -0.00180372   0.0572691      0.17749    20868  14532.5  \n",
       "7   -0.0275537  -0.0111417   -0.062378     0.120021  5166.35  2187.67  \n",
       "8          NaN         NaN         NaN          NaN      NaN      NaN  \n",
       "9          NaN         NaN         NaN          NaN      NaN      NaN  \n",
       "10         D-7         MTD         QTD          YTD  Yr High   Yr Low  \n",
       "11       -6.95       -3.21       30.91        -2.32   2.6198   1.6407  \n",
       "12        1.51         0.4       18.84         0.01   0.7393   0.3076  \n",
       "13       -8.46       -3.61       12.07        -2.33   2.0562   1.1919  \n",
       "14        -6.3        -1.6        43.4          8.7    1.167    0.075  \n",
       "15        -1.8        -0.9         1.3        -13.4    0.027   -0.283  \n",
       "16        -4.5        -0.7        42.1         22.1    1.157    0.342  \n",
       "17         NaN         NaN         NaN          NaN      NaN      NaN  \n",
       "18         NaN         NaN         NaN          NaN      NaN      NaN  \n",
       "19         D-7         MTD         QTD          YTD  Yr High   Yr Low  \n",
       "20   0.0102796  0.00162324  0.00982961    0.0800386   100.33   81.328  \n",
       "21  -0.0124459 -0.00309541 -0.00173215   -0.0948917   1.3422   1.0496  \n",
       "22   0.0017996 -0.00224043   0.0447051  0.000641972   1.6886   1.4632  \n",
       "23  -0.0062888 -0.00112876  -0.0407966   -0.0342659   102.03  125.612  \n",
       "24   0.0023387  -0.0030104  -0.0454605    -0.108746   0.9378   0.7269  \n",
       "25         NaN         NaN         NaN          NaN      NaN      NaN  \n",
       "26         NaN         NaN         NaN          NaN      NaN      NaN  \n",
       "27         NaN         NaN         NaN          NaN      NaN      NaN  \n",
       "28         D-7         MTD         QTD          YTD  Yr High   Yr Low  \n",
       "29  -0.0468453  -0.0413837   -0.154278    -0.158845    93.38    47.12  \n",
       "30 -0.00683745   -0.005205  -0.0947316   -0.0805975     1321     1086  \n",
       "31  -0.0182529   -0.012459  -0.0828258   -0.0720887   451.75    363.5  \n",
       "32         NaN         NaN         NaN          NaN      NaN      NaN  \n",
       "33         NaN         NaN         NaN          NaN      NaN      NaN  \n",
       "34         NaN         NaN         NaN          NaN      NaN      NaN  \n",
       "35         D-7         MTD         QTD          YTD  Yr High   Yr Low  \n",
       "36      -0.501       0.831       8.333       14.013  104.336   66.838  \n",
       "37       5.545       3.249      30.208       26.445  183.748  127.162  \n",
       "38      10.277       4.173      24.706       45.332  151.325   73.677  \n",
       "39           1           2         5.5            6  119.511    80.84  \n",
       "40       3.628       6.039     -27.167     -126.441  628.765  221.835  \n",
       "41      -0.887      -0.717      11.862       29.295  240.794      159  \n",
       "42       6.529      -1.569      20.735       60.005  238.408      156  \n",
       "43      -4.678       4.642      27.912       95.564  306.576   123.25  \n",
       "44      -0.751       0.331        2.08       -0.326   67.609   45.491  \n",
       "45         NaN         NaN         NaN          NaN      NaN      NaN  \n",
       "46         D-7         MTD         QTD          YTD  Yr High   Yr Low  \n",
       "47      -0.713           0       4.511        -27.5   73.833   31.335  \n",
       "48      -0.215      -0.611       6.397      -24.382   160.16    84.16  \n",
       "49       2.668      -0.057       9.649       -2.852  130.438   56.493  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "국제0803 = pd.read_excel(파일경로, skiprows=3, usecols='C:K', header=None)\n",
    "국제0803"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Equity</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Last Price</td>\n",
       "      <td>D-1</td>\n",
       "      <td>D-7</td>\n",
       "      <td>MTD</td>\n",
       "      <td>QTD</td>\n",
       "      <td>YTD</td>\n",
       "      <td>Yr High</td>\n",
       "      <td>Yr Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>MSCI AC World</td>\n",
       "      <td>425.32</td>\n",
       "      <td>-0.00342097</td>\n",
       "      <td>0.0141638</td>\n",
       "      <td>-0.00342097</td>\n",
       "      <td>-0.00561115</td>\n",
       "      <td>0.0196586</td>\n",
       "      <td>442.7</td>\n",
       "      <td>392.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>S&amp;P 500</td>\n",
       "      <td>2098.04</td>\n",
       "      <td>-0.00275686</td>\n",
       "      <td>0.0147028</td>\n",
       "      <td>-0.00275686</td>\n",
       "      <td>0.0150366</td>\n",
       "      <td>0.0190102</td>\n",
       "      <td>2130.82</td>\n",
       "      <td>1862.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Euro Stoxx 50</td>\n",
       "      <td>3635.4</td>\n",
       "      <td>0.00963982</td>\n",
       "      <td>0.0348126</td>\n",
       "      <td>0.00963982</td>\n",
       "      <td>-0.0214978</td>\n",
       "      <td>0.155405</td>\n",
       "      <td>3828.78</td>\n",
       "      <td>2874.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>DAX</td>\n",
       "      <td>11443.7</td>\n",
       "      <td>0.0119135</td>\n",
       "      <td>0.0350313</td>\n",
       "      <td>0.0119135</td>\n",
       "      <td>-0.0437581</td>\n",
       "      <td>0.167066</td>\n",
       "      <td>12374.7</td>\n",
       "      <td>8571.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Nikkei 225</td>\n",
       "      <td>20548.1</td>\n",
       "      <td>-0.00180372</td>\n",
       "      <td>0.00973017</td>\n",
       "      <td>-0.00180372</td>\n",
       "      <td>0.0572691</td>\n",
       "      <td>0.17749</td>\n",
       "      <td>20868</td>\n",
       "      <td>14532.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>상해</td>\n",
       "      <td>3622.91</td>\n",
       "      <td>-0.0111417</td>\n",
       "      <td>-0.0275537</td>\n",
       "      <td>-0.0111417</td>\n",
       "      <td>-0.062378</td>\n",
       "      <td>0.120021</td>\n",
       "      <td>5166.35</td>\n",
       "      <td>2187.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Fixed Income</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Last Price(%)</td>\n",
       "      <td>D-1</td>\n",
       "      <td>D-7</td>\n",
       "      <td>MTD</td>\n",
       "      <td>QTD</td>\n",
       "      <td>YTD</td>\n",
       "      <td>Yr High</td>\n",
       "      <td>Yr Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>UST 10Y</td>\n",
       "      <td>2.148</td>\n",
       "      <td>-3</td>\n",
       "      <td>-6.95</td>\n",
       "      <td>-3.21</td>\n",
       "      <td>30.91</td>\n",
       "      <td>-2.32</td>\n",
       "      <td>2.6198</td>\n",
       "      <td>1.6407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>UST 2Y</td>\n",
       "      <td>0.6646</td>\n",
       "      <td>0</td>\n",
       "      <td>1.51</td>\n",
       "      <td>0.4</td>\n",
       "      <td>18.84</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.7393</td>\n",
       "      <td>0.3076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Spread(10Y-2Y)</td>\n",
       "      <td>1.4834</td>\n",
       "      <td>-3</td>\n",
       "      <td>-8.46</td>\n",
       "      <td>-3.61</td>\n",
       "      <td>12.07</td>\n",
       "      <td>-2.33</td>\n",
       "      <td>2.0562</td>\n",
       "      <td>1.1919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Bund 10Y</td>\n",
       "      <td>0.628</td>\n",
       "      <td>-1</td>\n",
       "      <td>-6.3</td>\n",
       "      <td>-1.6</td>\n",
       "      <td>43.4</td>\n",
       "      <td>8.7</td>\n",
       "      <td>1.167</td>\n",
       "      <td>0.075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Bund 2Y</td>\n",
       "      <td>-0.244</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.8</td>\n",
       "      <td>-0.9</td>\n",
       "      <td>1.3</td>\n",
       "      <td>-13.4</td>\n",
       "      <td>0.027</td>\n",
       "      <td>-0.283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Spread(10Y-2Y)</td>\n",
       "      <td>0.872</td>\n",
       "      <td>-1</td>\n",
       "      <td>-4.5</td>\n",
       "      <td>-0.7</td>\n",
       "      <td>42.1</td>\n",
       "      <td>22.1</td>\n",
       "      <td>1.157</td>\n",
       "      <td>0.342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>FX</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Last Price</td>\n",
       "      <td>D-1</td>\n",
       "      <td>D-7</td>\n",
       "      <td>MTD</td>\n",
       "      <td>QTD</td>\n",
       "      <td>YTD</td>\n",
       "      <td>Yr High</td>\n",
       "      <td>Yr Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Dollar Index</td>\n",
       "      <td>97.494</td>\n",
       "      <td>0.00162324</td>\n",
       "      <td>0.0102796</td>\n",
       "      <td>0.00162324</td>\n",
       "      <td>0.00982961</td>\n",
       "      <td>0.0800386</td>\n",
       "      <td>100.33</td>\n",
       "      <td>81.328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>EUR</td>\n",
       "      <td>1.095</td>\n",
       "      <td>-0.00309541</td>\n",
       "      <td>-0.0124459</td>\n",
       "      <td>-0.00309541</td>\n",
       "      <td>-0.00173215</td>\n",
       "      <td>-0.0948917</td>\n",
       "      <td>1.3422</td>\n",
       "      <td>1.0496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>GBP</td>\n",
       "      <td>1.5587</td>\n",
       "      <td>-0.00224043</td>\n",
       "      <td>0.0017996</td>\n",
       "      <td>-0.00224043</td>\n",
       "      <td>0.0447051</td>\n",
       "      <td>0.000641972</td>\n",
       "      <td>1.6886</td>\n",
       "      <td>1.4632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>JPY</td>\n",
       "      <td>124.03</td>\n",
       "      <td>-0.00112876</td>\n",
       "      <td>-0.0062888</td>\n",
       "      <td>-0.00112876</td>\n",
       "      <td>-0.0407966</td>\n",
       "      <td>-0.0342659</td>\n",
       "      <td>102.03</td>\n",
       "      <td>125.612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>AUD</td>\n",
       "      <td>0.7286</td>\n",
       "      <td>-0.0030104</td>\n",
       "      <td>0.0023387</td>\n",
       "      <td>-0.0030104</td>\n",
       "      <td>-0.0454605</td>\n",
       "      <td>-0.108746</td>\n",
       "      <td>0.9378</td>\n",
       "      <td>0.7269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>* D-1,D-7,MTD,QTD,YTD 변동분은 USD 대비 해당 통화 가치의 변동</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Commodity</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Last Price</td>\n",
       "      <td>D-1</td>\n",
       "      <td>D-7</td>\n",
       "      <td>MTD</td>\n",
       "      <td>QTD</td>\n",
       "      <td>YTD</td>\n",
       "      <td>Yr High</td>\n",
       "      <td>Yr Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>WTI</td>\n",
       "      <td>45.17</td>\n",
       "      <td>-0.0413837</td>\n",
       "      <td>-0.0468453</td>\n",
       "      <td>-0.0413837</td>\n",
       "      <td>-0.154278</td>\n",
       "      <td>-0.158845</td>\n",
       "      <td>93.38</td>\n",
       "      <td>47.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Gold</td>\n",
       "      <td>1089.4</td>\n",
       "      <td>-0.005205</td>\n",
       "      <td>-0.00683745</td>\n",
       "      <td>-0.005205</td>\n",
       "      <td>-0.0947316</td>\n",
       "      <td>-0.0805975</td>\n",
       "      <td>1321</td>\n",
       "      <td>1086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Corn</td>\n",
       "      <td>376.5</td>\n",
       "      <td>-0.012459</td>\n",
       "      <td>-0.0182529</td>\n",
       "      <td>-0.012459</td>\n",
       "      <td>-0.0828258</td>\n",
       "      <td>-0.0720887</td>\n",
       "      <td>451.75</td>\n",
       "      <td>363.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>CDS</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Last Price</td>\n",
       "      <td>D-1</td>\n",
       "      <td>D-7</td>\n",
       "      <td>MTD</td>\n",
       "      <td>QTD</td>\n",
       "      <td>YTD</td>\n",
       "      <td>Yr High</td>\n",
       "      <td>Yr Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>China</td>\n",
       "      <td>101</td>\n",
       "      <td>0.831</td>\n",
       "      <td>-0.501</td>\n",
       "      <td>0.831</td>\n",
       "      <td>8.333</td>\n",
       "      <td>14.013</td>\n",
       "      <td>104.336</td>\n",
       "      <td>66.838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Indonesia</td>\n",
       "      <td>186.757</td>\n",
       "      <td>3.249</td>\n",
       "      <td>5.545</td>\n",
       "      <td>3.249</td>\n",
       "      <td>30.208</td>\n",
       "      <td>26.445</td>\n",
       "      <td>183.748</td>\n",
       "      <td>127.162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Malaysia</td>\n",
       "      <td>152.159</td>\n",
       "      <td>4.173</td>\n",
       "      <td>10.277</td>\n",
       "      <td>4.173</td>\n",
       "      <td>24.706</td>\n",
       "      <td>45.332</td>\n",
       "      <td>151.325</td>\n",
       "      <td>73.677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Thailand</td>\n",
       "      <td>109</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5.5</td>\n",
       "      <td>6</td>\n",
       "      <td>119.511</td>\n",
       "      <td>80.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>Russia</td>\n",
       "      <td>345.773</td>\n",
       "      <td>6.039</td>\n",
       "      <td>3.628</td>\n",
       "      <td>6.039</td>\n",
       "      <td>-27.167</td>\n",
       "      <td>-126.441</td>\n",
       "      <td>628.765</td>\n",
       "      <td>221.835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>South Africa</td>\n",
       "      <td>219.178</td>\n",
       "      <td>-0.717</td>\n",
       "      <td>-0.887</td>\n",
       "      <td>-0.717</td>\n",
       "      <td>11.862</td>\n",
       "      <td>29.295</td>\n",
       "      <td>240.794</td>\n",
       "      <td>159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>Turkey</td>\n",
       "      <td>236.839</td>\n",
       "      <td>-1.569</td>\n",
       "      <td>6.529</td>\n",
       "      <td>-1.569</td>\n",
       "      <td>20.735</td>\n",
       "      <td>60.005</td>\n",
       "      <td>238.408</td>\n",
       "      <td>156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>Brazil</td>\n",
       "      <td>292.362</td>\n",
       "      <td>4.642</td>\n",
       "      <td>-4.678</td>\n",
       "      <td>4.642</td>\n",
       "      <td>27.912</td>\n",
       "      <td>95.564</td>\n",
       "      <td>306.576</td>\n",
       "      <td>123.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>Korea</td>\n",
       "      <td>54.165</td>\n",
       "      <td>0.331</td>\n",
       "      <td>-0.751</td>\n",
       "      <td>0.331</td>\n",
       "      <td>2.08</td>\n",
       "      <td>-0.326</td>\n",
       "      <td>67.609</td>\n",
       "      <td>45.491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Last Price</td>\n",
       "      <td>D-1</td>\n",
       "      <td>D-7</td>\n",
       "      <td>MTD</td>\n",
       "      <td>QTD</td>\n",
       "      <td>YTD</td>\n",
       "      <td>Yr High</td>\n",
       "      <td>Yr Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>Japan</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.713</td>\n",
       "      <td>0</td>\n",
       "      <td>4.511</td>\n",
       "      <td>-27.5</td>\n",
       "      <td>73.833</td>\n",
       "      <td>31.335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>Italy</td>\n",
       "      <td>114.915</td>\n",
       "      <td>-0.611</td>\n",
       "      <td>-0.215</td>\n",
       "      <td>-0.611</td>\n",
       "      <td>6.397</td>\n",
       "      <td>-24.382</td>\n",
       "      <td>160.16</td>\n",
       "      <td>84.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>Spain</td>\n",
       "      <td>94.888</td>\n",
       "      <td>-0.057</td>\n",
       "      <td>2.668</td>\n",
       "      <td>-0.057</td>\n",
       "      <td>9.649</td>\n",
       "      <td>-2.852</td>\n",
       "      <td>130.438</td>\n",
       "      <td>56.493</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 0              1           2  \\\n",
       "0                                           Equity            NaN         NaN   \n",
       "1                                              NaN     Last Price         D-1   \n",
       "2                                    MSCI AC World         425.32 -0.00342097   \n",
       "3                                          S&P 500        2098.04 -0.00275686   \n",
       "4                                    Euro Stoxx 50         3635.4  0.00963982   \n",
       "5                                              DAX        11443.7   0.0119135   \n",
       "6                                       Nikkei 225        20548.1 -0.00180372   \n",
       "7                                               상해        3622.91  -0.0111417   \n",
       "9                                     Fixed Income            NaN         NaN   \n",
       "10                                             NaN  Last Price(%)         D-1   \n",
       "11                                         UST 10Y          2.148          -3   \n",
       "12                                          UST 2Y         0.6646           0   \n",
       "13                                  Spread(10Y-2Y)         1.4834          -3   \n",
       "14                                        Bund 10Y          0.628          -1   \n",
       "15                                         Bund 2Y         -0.244           0   \n",
       "16                                  Spread(10Y-2Y)          0.872          -1   \n",
       "18                                              FX            NaN         NaN   \n",
       "19                                             NaN     Last Price         D-1   \n",
       "20                                    Dollar Index         97.494  0.00162324   \n",
       "21                                             EUR          1.095 -0.00309541   \n",
       "22                                             GBP         1.5587 -0.00224043   \n",
       "23                                             JPY         124.03 -0.00112876   \n",
       "24                                             AUD         0.7286  -0.0030104   \n",
       "25  * D-1,D-7,MTD,QTD,YTD 변동분은 USD 대비 해당 통화 가치의 변동            NaN         NaN   \n",
       "27                                      Commodity             NaN         NaN   \n",
       "28                                             NaN     Last Price         D-1   \n",
       "29                                             WTI          45.17  -0.0413837   \n",
       "30                                            Gold         1089.4   -0.005205   \n",
       "31                                            Corn          376.5   -0.012459   \n",
       "34                                             CDS            NaN         NaN   \n",
       "35                                             NaN     Last Price         D-1   \n",
       "36                                           China            101       0.831   \n",
       "37                                       Indonesia        186.757       3.249   \n",
       "38                                        Malaysia        152.159       4.173   \n",
       "39                                        Thailand            109           2   \n",
       "40                                          Russia        345.773       6.039   \n",
       "41                                    South Africa        219.178      -0.717   \n",
       "42                                          Turkey        236.839      -1.569   \n",
       "43                                          Brazil        292.362       4.642   \n",
       "44                                           Korea         54.165       0.331   \n",
       "46                                             NaN     Last Price         D-1   \n",
       "47                                           Japan             40           0   \n",
       "48                                           Italy        114.915      -0.611   \n",
       "49                                           Spain         94.888      -0.057   \n",
       "\n",
       "             3           4           5            6        7        8  \n",
       "0          NaN         NaN         NaN          NaN      NaN      NaN  \n",
       "1          D-7         MTD         QTD          YTD  Yr High   Yr Low  \n",
       "2    0.0141638 -0.00342097 -0.00561115    0.0196586    442.7   392.13  \n",
       "3    0.0147028 -0.00275686   0.0150366    0.0190102  2130.82  1862.49  \n",
       "4    0.0348126  0.00963982  -0.0214978     0.155405  3828.78  2874.65  \n",
       "5    0.0350313   0.0119135  -0.0437581     0.167066  12374.7  8571.95  \n",
       "6   0.00973017 -0.00180372   0.0572691      0.17749    20868  14532.5  \n",
       "7   -0.0275537  -0.0111417   -0.062378     0.120021  5166.35  2187.67  \n",
       "9          NaN         NaN         NaN          NaN      NaN      NaN  \n",
       "10         D-7         MTD         QTD          YTD  Yr High   Yr Low  \n",
       "11       -6.95       -3.21       30.91        -2.32   2.6198   1.6407  \n",
       "12        1.51         0.4       18.84         0.01   0.7393   0.3076  \n",
       "13       -8.46       -3.61       12.07        -2.33   2.0562   1.1919  \n",
       "14        -6.3        -1.6        43.4          8.7    1.167    0.075  \n",
       "15        -1.8        -0.9         1.3        -13.4    0.027   -0.283  \n",
       "16        -4.5        -0.7        42.1         22.1    1.157    0.342  \n",
       "18         NaN         NaN         NaN          NaN      NaN      NaN  \n",
       "19         D-7         MTD         QTD          YTD  Yr High   Yr Low  \n",
       "20   0.0102796  0.00162324  0.00982961    0.0800386   100.33   81.328  \n",
       "21  -0.0124459 -0.00309541 -0.00173215   -0.0948917   1.3422   1.0496  \n",
       "22   0.0017996 -0.00224043   0.0447051  0.000641972   1.6886   1.4632  \n",
       "23  -0.0062888 -0.00112876  -0.0407966   -0.0342659   102.03  125.612  \n",
       "24   0.0023387  -0.0030104  -0.0454605    -0.108746   0.9378   0.7269  \n",
       "25         NaN         NaN         NaN          NaN      NaN      NaN  \n",
       "27         NaN         NaN         NaN          NaN      NaN      NaN  \n",
       "28         D-7         MTD         QTD          YTD  Yr High   Yr Low  \n",
       "29  -0.0468453  -0.0413837   -0.154278    -0.158845    93.38    47.12  \n",
       "30 -0.00683745   -0.005205  -0.0947316   -0.0805975     1321     1086  \n",
       "31  -0.0182529   -0.012459  -0.0828258   -0.0720887   451.75    363.5  \n",
       "34         NaN         NaN         NaN          NaN      NaN      NaN  \n",
       "35         D-7         MTD         QTD          YTD  Yr High   Yr Low  \n",
       "36      -0.501       0.831       8.333       14.013  104.336   66.838  \n",
       "37       5.545       3.249      30.208       26.445  183.748  127.162  \n",
       "38      10.277       4.173      24.706       45.332  151.325   73.677  \n",
       "39           1           2         5.5            6  119.511    80.84  \n",
       "40       3.628       6.039     -27.167     -126.441  628.765  221.835  \n",
       "41      -0.887      -0.717      11.862       29.295  240.794      159  \n",
       "42       6.529      -1.569      20.735       60.005  238.408      156  \n",
       "43      -4.678       4.642      27.912       95.564  306.576   123.25  \n",
       "44      -0.751       0.331        2.08       -0.326   67.609   45.491  \n",
       "46         D-7         MTD         QTD          YTD  Yr High   Yr Low  \n",
       "47      -0.713           0       4.511        -27.5   73.833   31.335  \n",
       "48      -0.215      -0.611       6.397      -24.382   160.16    84.16  \n",
       "49       2.668      -0.057       9.649       -2.852  130.438   56.493  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "국제0803 = 국제0803.dropna(how='all')\n",
    "국제0803"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Equity</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Last Price</td>\n",
       "      <td>D-1</td>\n",
       "      <td>D-7</td>\n",
       "      <td>MTD</td>\n",
       "      <td>QTD</td>\n",
       "      <td>YTD</td>\n",
       "      <td>Yr High</td>\n",
       "      <td>Yr Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>MSCI AC World</td>\n",
       "      <td>425.32</td>\n",
       "      <td>-0.00342097</td>\n",
       "      <td>0.0141638</td>\n",
       "      <td>-0.00342097</td>\n",
       "      <td>-0.00561115</td>\n",
       "      <td>0.0196586</td>\n",
       "      <td>442.7</td>\n",
       "      <td>392.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>S&amp;P 500</td>\n",
       "      <td>2098.04</td>\n",
       "      <td>-0.00275686</td>\n",
       "      <td>0.0147028</td>\n",
       "      <td>-0.00275686</td>\n",
       "      <td>0.0150366</td>\n",
       "      <td>0.0190102</td>\n",
       "      <td>2130.82</td>\n",
       "      <td>1862.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Euro Stoxx 50</td>\n",
       "      <td>3635.4</td>\n",
       "      <td>0.00963982</td>\n",
       "      <td>0.0348126</td>\n",
       "      <td>0.00963982</td>\n",
       "      <td>-0.0214978</td>\n",
       "      <td>0.155405</td>\n",
       "      <td>3828.78</td>\n",
       "      <td>2874.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>DAX</td>\n",
       "      <td>11443.7</td>\n",
       "      <td>0.0119135</td>\n",
       "      <td>0.0350313</td>\n",
       "      <td>0.0119135</td>\n",
       "      <td>-0.0437581</td>\n",
       "      <td>0.167066</td>\n",
       "      <td>12374.7</td>\n",
       "      <td>8571.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Nikkei 225</td>\n",
       "      <td>20548.1</td>\n",
       "      <td>-0.00180372</td>\n",
       "      <td>0.00973017</td>\n",
       "      <td>-0.00180372</td>\n",
       "      <td>0.0572691</td>\n",
       "      <td>0.17749</td>\n",
       "      <td>20868</td>\n",
       "      <td>14532.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>상해</td>\n",
       "      <td>3622.91</td>\n",
       "      <td>-0.0111417</td>\n",
       "      <td>-0.0275537</td>\n",
       "      <td>-0.0111417</td>\n",
       "      <td>-0.062378</td>\n",
       "      <td>0.120021</td>\n",
       "      <td>5166.35</td>\n",
       "      <td>2187.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Fixed Income</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Last Price(%)</td>\n",
       "      <td>D-1</td>\n",
       "      <td>D-7</td>\n",
       "      <td>MTD</td>\n",
       "      <td>QTD</td>\n",
       "      <td>YTD</td>\n",
       "      <td>Yr High</td>\n",
       "      <td>Yr Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>UST 10Y</td>\n",
       "      <td>2.148</td>\n",
       "      <td>-3</td>\n",
       "      <td>-6.95</td>\n",
       "      <td>-3.21</td>\n",
       "      <td>30.91</td>\n",
       "      <td>-2.32</td>\n",
       "      <td>2.6198</td>\n",
       "      <td>1.6407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>UST 2Y</td>\n",
       "      <td>0.6646</td>\n",
       "      <td>0</td>\n",
       "      <td>1.51</td>\n",
       "      <td>0.4</td>\n",
       "      <td>18.84</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.7393</td>\n",
       "      <td>0.3076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Spread(10Y-2Y)</td>\n",
       "      <td>1.4834</td>\n",
       "      <td>-3</td>\n",
       "      <td>-8.46</td>\n",
       "      <td>-3.61</td>\n",
       "      <td>12.07</td>\n",
       "      <td>-2.33</td>\n",
       "      <td>2.0562</td>\n",
       "      <td>1.1919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Bund 10Y</td>\n",
       "      <td>0.628</td>\n",
       "      <td>-1</td>\n",
       "      <td>-6.3</td>\n",
       "      <td>-1.6</td>\n",
       "      <td>43.4</td>\n",
       "      <td>8.7</td>\n",
       "      <td>1.167</td>\n",
       "      <td>0.075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Bund 2Y</td>\n",
       "      <td>-0.244</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.8</td>\n",
       "      <td>-0.9</td>\n",
       "      <td>1.3</td>\n",
       "      <td>-13.4</td>\n",
       "      <td>0.027</td>\n",
       "      <td>-0.283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Spread(10Y-2Y)</td>\n",
       "      <td>0.872</td>\n",
       "      <td>-1</td>\n",
       "      <td>-4.5</td>\n",
       "      <td>-0.7</td>\n",
       "      <td>42.1</td>\n",
       "      <td>22.1</td>\n",
       "      <td>1.157</td>\n",
       "      <td>0.342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>FX</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Last Price</td>\n",
       "      <td>D-1</td>\n",
       "      <td>D-7</td>\n",
       "      <td>MTD</td>\n",
       "      <td>QTD</td>\n",
       "      <td>YTD</td>\n",
       "      <td>Yr High</td>\n",
       "      <td>Yr Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Dollar Index</td>\n",
       "      <td>97.494</td>\n",
       "      <td>0.00162324</td>\n",
       "      <td>0.0102796</td>\n",
       "      <td>0.00162324</td>\n",
       "      <td>0.00982961</td>\n",
       "      <td>0.0800386</td>\n",
       "      <td>100.33</td>\n",
       "      <td>81.328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>EUR</td>\n",
       "      <td>1.095</td>\n",
       "      <td>-0.00309541</td>\n",
       "      <td>-0.0124459</td>\n",
       "      <td>-0.00309541</td>\n",
       "      <td>-0.00173215</td>\n",
       "      <td>-0.0948917</td>\n",
       "      <td>1.3422</td>\n",
       "      <td>1.0496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>GBP</td>\n",
       "      <td>1.5587</td>\n",
       "      <td>-0.00224043</td>\n",
       "      <td>0.0017996</td>\n",
       "      <td>-0.00224043</td>\n",
       "      <td>0.0447051</td>\n",
       "      <td>0.000641972</td>\n",
       "      <td>1.6886</td>\n",
       "      <td>1.4632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>JPY</td>\n",
       "      <td>124.03</td>\n",
       "      <td>-0.00112876</td>\n",
       "      <td>-0.0062888</td>\n",
       "      <td>-0.00112876</td>\n",
       "      <td>-0.0407966</td>\n",
       "      <td>-0.0342659</td>\n",
       "      <td>102.03</td>\n",
       "      <td>125.612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>AUD</td>\n",
       "      <td>0.7286</td>\n",
       "      <td>-0.0030104</td>\n",
       "      <td>0.0023387</td>\n",
       "      <td>-0.0030104</td>\n",
       "      <td>-0.0454605</td>\n",
       "      <td>-0.108746</td>\n",
       "      <td>0.9378</td>\n",
       "      <td>0.7269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>* D-1,D-7,MTD,QTD,YTD 변동분은 USD 대비 해당 통화 가치의 변동</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Commodity</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Last Price</td>\n",
       "      <td>D-1</td>\n",
       "      <td>D-7</td>\n",
       "      <td>MTD</td>\n",
       "      <td>QTD</td>\n",
       "      <td>YTD</td>\n",
       "      <td>Yr High</td>\n",
       "      <td>Yr Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>WTI</td>\n",
       "      <td>45.17</td>\n",
       "      <td>-0.0413837</td>\n",
       "      <td>-0.0468453</td>\n",
       "      <td>-0.0413837</td>\n",
       "      <td>-0.154278</td>\n",
       "      <td>-0.158845</td>\n",
       "      <td>93.38</td>\n",
       "      <td>47.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Gold</td>\n",
       "      <td>1089.4</td>\n",
       "      <td>-0.005205</td>\n",
       "      <td>-0.00683745</td>\n",
       "      <td>-0.005205</td>\n",
       "      <td>-0.0947316</td>\n",
       "      <td>-0.0805975</td>\n",
       "      <td>1321</td>\n",
       "      <td>1086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Corn</td>\n",
       "      <td>376.5</td>\n",
       "      <td>-0.012459</td>\n",
       "      <td>-0.0182529</td>\n",
       "      <td>-0.012459</td>\n",
       "      <td>-0.0828258</td>\n",
       "      <td>-0.0720887</td>\n",
       "      <td>451.75</td>\n",
       "      <td>363.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>CDS</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Last Price</td>\n",
       "      <td>D-1</td>\n",
       "      <td>D-7</td>\n",
       "      <td>MTD</td>\n",
       "      <td>QTD</td>\n",
       "      <td>YTD</td>\n",
       "      <td>Yr High</td>\n",
       "      <td>Yr Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>China</td>\n",
       "      <td>101</td>\n",
       "      <td>0.831</td>\n",
       "      <td>-0.501</td>\n",
       "      <td>0.831</td>\n",
       "      <td>8.333</td>\n",
       "      <td>14.013</td>\n",
       "      <td>104.336</td>\n",
       "      <td>66.838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Indonesia</td>\n",
       "      <td>186.757</td>\n",
       "      <td>3.249</td>\n",
       "      <td>5.545</td>\n",
       "      <td>3.249</td>\n",
       "      <td>30.208</td>\n",
       "      <td>26.445</td>\n",
       "      <td>183.748</td>\n",
       "      <td>127.162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Malaysia</td>\n",
       "      <td>152.159</td>\n",
       "      <td>4.173</td>\n",
       "      <td>10.277</td>\n",
       "      <td>4.173</td>\n",
       "      <td>24.706</td>\n",
       "      <td>45.332</td>\n",
       "      <td>151.325</td>\n",
       "      <td>73.677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Thailand</td>\n",
       "      <td>109</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5.5</td>\n",
       "      <td>6</td>\n",
       "      <td>119.511</td>\n",
       "      <td>80.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Russia</td>\n",
       "      <td>345.773</td>\n",
       "      <td>6.039</td>\n",
       "      <td>3.628</td>\n",
       "      <td>6.039</td>\n",
       "      <td>-27.167</td>\n",
       "      <td>-126.441</td>\n",
       "      <td>628.765</td>\n",
       "      <td>221.835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>South Africa</td>\n",
       "      <td>219.178</td>\n",
       "      <td>-0.717</td>\n",
       "      <td>-0.887</td>\n",
       "      <td>-0.717</td>\n",
       "      <td>11.862</td>\n",
       "      <td>29.295</td>\n",
       "      <td>240.794</td>\n",
       "      <td>159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Turkey</td>\n",
       "      <td>236.839</td>\n",
       "      <td>-1.569</td>\n",
       "      <td>6.529</td>\n",
       "      <td>-1.569</td>\n",
       "      <td>20.735</td>\n",
       "      <td>60.005</td>\n",
       "      <td>238.408</td>\n",
       "      <td>156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Brazil</td>\n",
       "      <td>292.362</td>\n",
       "      <td>4.642</td>\n",
       "      <td>-4.678</td>\n",
       "      <td>4.642</td>\n",
       "      <td>27.912</td>\n",
       "      <td>95.564</td>\n",
       "      <td>306.576</td>\n",
       "      <td>123.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Korea</td>\n",
       "      <td>54.165</td>\n",
       "      <td>0.331</td>\n",
       "      <td>-0.751</td>\n",
       "      <td>0.331</td>\n",
       "      <td>2.08</td>\n",
       "      <td>-0.326</td>\n",
       "      <td>67.609</td>\n",
       "      <td>45.491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Last Price</td>\n",
       "      <td>D-1</td>\n",
       "      <td>D-7</td>\n",
       "      <td>MTD</td>\n",
       "      <td>QTD</td>\n",
       "      <td>YTD</td>\n",
       "      <td>Yr High</td>\n",
       "      <td>Yr Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>Japan</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.713</td>\n",
       "      <td>0</td>\n",
       "      <td>4.511</td>\n",
       "      <td>-27.5</td>\n",
       "      <td>73.833</td>\n",
       "      <td>31.335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>Italy</td>\n",
       "      <td>114.915</td>\n",
       "      <td>-0.611</td>\n",
       "      <td>-0.215</td>\n",
       "      <td>-0.611</td>\n",
       "      <td>6.397</td>\n",
       "      <td>-24.382</td>\n",
       "      <td>160.16</td>\n",
       "      <td>84.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>Spain</td>\n",
       "      <td>94.888</td>\n",
       "      <td>-0.057</td>\n",
       "      <td>2.668</td>\n",
       "      <td>-0.057</td>\n",
       "      <td>9.649</td>\n",
       "      <td>-2.852</td>\n",
       "      <td>130.438</td>\n",
       "      <td>56.493</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 0              1           2  \\\n",
       "0                                           Equity            NaN         NaN   \n",
       "1                                              NaN     Last Price         D-1   \n",
       "2                                    MSCI AC World         425.32 -0.00342097   \n",
       "3                                          S&P 500        2098.04 -0.00275686   \n",
       "4                                    Euro Stoxx 50         3635.4  0.00963982   \n",
       "5                                              DAX        11443.7   0.0119135   \n",
       "6                                       Nikkei 225        20548.1 -0.00180372   \n",
       "7                                               상해        3622.91  -0.0111417   \n",
       "8                                     Fixed Income            NaN         NaN   \n",
       "9                                              NaN  Last Price(%)         D-1   \n",
       "10                                         UST 10Y          2.148          -3   \n",
       "11                                          UST 2Y         0.6646           0   \n",
       "12                                  Spread(10Y-2Y)         1.4834          -3   \n",
       "13                                        Bund 10Y          0.628          -1   \n",
       "14                                         Bund 2Y         -0.244           0   \n",
       "15                                  Spread(10Y-2Y)          0.872          -1   \n",
       "16                                              FX            NaN         NaN   \n",
       "17                                             NaN     Last Price         D-1   \n",
       "18                                    Dollar Index         97.494  0.00162324   \n",
       "19                                             EUR          1.095 -0.00309541   \n",
       "20                                             GBP         1.5587 -0.00224043   \n",
       "21                                             JPY         124.03 -0.00112876   \n",
       "22                                             AUD         0.7286  -0.0030104   \n",
       "23  * D-1,D-7,MTD,QTD,YTD 변동분은 USD 대비 해당 통화 가치의 변동            NaN         NaN   \n",
       "24                                      Commodity             NaN         NaN   \n",
       "25                                             NaN     Last Price         D-1   \n",
       "26                                             WTI          45.17  -0.0413837   \n",
       "27                                            Gold         1089.4   -0.005205   \n",
       "28                                            Corn          376.5   -0.012459   \n",
       "29                                             CDS            NaN         NaN   \n",
       "30                                             NaN     Last Price         D-1   \n",
       "31                                           China            101       0.831   \n",
       "32                                       Indonesia        186.757       3.249   \n",
       "33                                        Malaysia        152.159       4.173   \n",
       "34                                        Thailand            109           2   \n",
       "35                                          Russia        345.773       6.039   \n",
       "36                                    South Africa        219.178      -0.717   \n",
       "37                                          Turkey        236.839      -1.569   \n",
       "38                                          Brazil        292.362       4.642   \n",
       "39                                           Korea         54.165       0.331   \n",
       "40                                             NaN     Last Price         D-1   \n",
       "41                                           Japan             40           0   \n",
       "42                                           Italy        114.915      -0.611   \n",
       "43                                           Spain         94.888      -0.057   \n",
       "\n",
       "             3           4           5            6        7        8  \n",
       "0          NaN         NaN         NaN          NaN      NaN      NaN  \n",
       "1          D-7         MTD         QTD          YTD  Yr High   Yr Low  \n",
       "2    0.0141638 -0.00342097 -0.00561115    0.0196586    442.7   392.13  \n",
       "3    0.0147028 -0.00275686   0.0150366    0.0190102  2130.82  1862.49  \n",
       "4    0.0348126  0.00963982  -0.0214978     0.155405  3828.78  2874.65  \n",
       "5    0.0350313   0.0119135  -0.0437581     0.167066  12374.7  8571.95  \n",
       "6   0.00973017 -0.00180372   0.0572691      0.17749    20868  14532.5  \n",
       "7   -0.0275537  -0.0111417   -0.062378     0.120021  5166.35  2187.67  \n",
       "8          NaN         NaN         NaN          NaN      NaN      NaN  \n",
       "9          D-7         MTD         QTD          YTD  Yr High   Yr Low  \n",
       "10       -6.95       -3.21       30.91        -2.32   2.6198   1.6407  \n",
       "11        1.51         0.4       18.84         0.01   0.7393   0.3076  \n",
       "12       -8.46       -3.61       12.07        -2.33   2.0562   1.1919  \n",
       "13        -6.3        -1.6        43.4          8.7    1.167    0.075  \n",
       "14        -1.8        -0.9         1.3        -13.4    0.027   -0.283  \n",
       "15        -4.5        -0.7        42.1         22.1    1.157    0.342  \n",
       "16         NaN         NaN         NaN          NaN      NaN      NaN  \n",
       "17         D-7         MTD         QTD          YTD  Yr High   Yr Low  \n",
       "18   0.0102796  0.00162324  0.00982961    0.0800386   100.33   81.328  \n",
       "19  -0.0124459 -0.00309541 -0.00173215   -0.0948917   1.3422   1.0496  \n",
       "20   0.0017996 -0.00224043   0.0447051  0.000641972   1.6886   1.4632  \n",
       "21  -0.0062888 -0.00112876  -0.0407966   -0.0342659   102.03  125.612  \n",
       "22   0.0023387  -0.0030104  -0.0454605    -0.108746   0.9378   0.7269  \n",
       "23         NaN         NaN         NaN          NaN      NaN      NaN  \n",
       "24         NaN         NaN         NaN          NaN      NaN      NaN  \n",
       "25         D-7         MTD         QTD          YTD  Yr High   Yr Low  \n",
       "26  -0.0468453  -0.0413837   -0.154278    -0.158845    93.38    47.12  \n",
       "27 -0.00683745   -0.005205  -0.0947316   -0.0805975     1321     1086  \n",
       "28  -0.0182529   -0.012459  -0.0828258   -0.0720887   451.75    363.5  \n",
       "29         NaN         NaN         NaN          NaN      NaN      NaN  \n",
       "30         D-7         MTD         QTD          YTD  Yr High   Yr Low  \n",
       "31      -0.501       0.831       8.333       14.013  104.336   66.838  \n",
       "32       5.545       3.249      30.208       26.445  183.748  127.162  \n",
       "33      10.277       4.173      24.706       45.332  151.325   73.677  \n",
       "34           1           2         5.5            6  119.511    80.84  \n",
       "35       3.628       6.039     -27.167     -126.441  628.765  221.835  \n",
       "36      -0.887      -0.717      11.862       29.295  240.794      159  \n",
       "37       6.529      -1.569      20.735       60.005  238.408      156  \n",
       "38      -4.678       4.642      27.912       95.564  306.576   123.25  \n",
       "39      -0.751       0.331        2.08       -0.326   67.609   45.491  \n",
       "40         D-7         MTD         QTD          YTD  Yr High   Yr Low  \n",
       "41      -0.713           0       4.511        -27.5   73.833   31.335  \n",
       "42      -0.215      -0.611       6.397      -24.382   160.16    84.16  \n",
       "43       2.668      -0.057       9.649       -2.852  130.438   56.493  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "국제0803 = 국제0803.reset_index(drop=True)\n",
    "국제0803"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</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",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Equity</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Last Price</td>\n",
       "      <td>D-1</td>\n",
       "      <td>D-7</td>\n",
       "      <td>MTD</td>\n",
       "      <td>QTD</td>\n",
       "      <td>YTD</td>\n",
       "      <td>Yr High</td>\n",
       "      <td>Yr Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>MSCI AC World</td>\n",
       "      <td>425.32</td>\n",
       "      <td>-0.00342097</td>\n",
       "      <td>0.0141638</td>\n",
       "      <td>-0.00342097</td>\n",
       "      <td>-0.00561115</td>\n",
       "      <td>0.0196586</td>\n",
       "      <td>442.7</td>\n",
       "      <td>392.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>S&amp;P 500</td>\n",
       "      <td>2098.04</td>\n",
       "      <td>-0.00275686</td>\n",
       "      <td>0.0147028</td>\n",
       "      <td>-0.00275686</td>\n",
       "      <td>0.0150366</td>\n",
       "      <td>0.0190102</td>\n",
       "      <td>2130.82</td>\n",
       "      <td>1862.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Euro Stoxx 50</td>\n",
       "      <td>3635.4</td>\n",
       "      <td>0.00963982</td>\n",
       "      <td>0.0348126</td>\n",
       "      <td>0.00963982</td>\n",
       "      <td>-0.0214978</td>\n",
       "      <td>0.155405</td>\n",
       "      <td>3828.78</td>\n",
       "      <td>2874.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>DAX</td>\n",
       "      <td>11443.7</td>\n",
       "      <td>0.0119135</td>\n",
       "      <td>0.0350313</td>\n",
       "      <td>0.0119135</td>\n",
       "      <td>-0.0437581</td>\n",
       "      <td>0.167066</td>\n",
       "      <td>12374.7</td>\n",
       "      <td>8571.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Nikkei 225</td>\n",
       "      <td>20548.1</td>\n",
       "      <td>-0.00180372</td>\n",
       "      <td>0.00973017</td>\n",
       "      <td>-0.00180372</td>\n",
       "      <td>0.0572691</td>\n",
       "      <td>0.17749</td>\n",
       "      <td>20868</td>\n",
       "      <td>14532.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>상해</td>\n",
       "      <td>3622.91</td>\n",
       "      <td>-0.0111417</td>\n",
       "      <td>-0.0275537</td>\n",
       "      <td>-0.0111417</td>\n",
       "      <td>-0.062378</td>\n",
       "      <td>0.120021</td>\n",
       "      <td>5166.35</td>\n",
       "      <td>2187.67</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               0           1           2           3           4           5  \\\n",
       "0         Equity         NaN         NaN         NaN         NaN         NaN   \n",
       "1            NaN  Last Price         D-1         D-7         MTD         QTD   \n",
       "2  MSCI AC World      425.32 -0.00342097   0.0141638 -0.00342097 -0.00561115   \n",
       "3        S&P 500     2098.04 -0.00275686   0.0147028 -0.00275686   0.0150366   \n",
       "4  Euro Stoxx 50      3635.4  0.00963982   0.0348126  0.00963982  -0.0214978   \n",
       "5            DAX     11443.7   0.0119135   0.0350313   0.0119135  -0.0437581   \n",
       "6     Nikkei 225     20548.1 -0.00180372  0.00973017 -0.00180372   0.0572691   \n",
       "7             상해     3622.91  -0.0111417  -0.0275537  -0.0111417   -0.062378   \n",
       "\n",
       "           6        7        8  \n",
       "0        NaN      NaN      NaN  \n",
       "1        YTD  Yr High   Yr Low  \n",
       "2  0.0196586    442.7   392.13  \n",
       "3  0.0190102  2130.82  1862.49  \n",
       "4   0.155405  3828.78  2874.65  \n",
       "5   0.167066  12374.7  8571.95  \n",
       "6    0.17749    20868  14532.5  \n",
       "7   0.120021  5166.35  2187.67  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "국제0803.iloc[0:8]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "색인범위목록 = {\n",
    "    'equity': (0,8),\n",
    "    'fixed income': (8,16),\n",
    "    'fx': (16,23),\n",
    "    'commodity': (24,29),\n",
    "    'cds': (29,44),\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "국제0803지표 = {}\n",
    "for 제목, 색인범위 in 색인범위목록.items():\n",
    "    시작, 종료 = 색인범위\n",
    "    프레임 = 국제0803.iloc[시작:종료]\n",
    "    프레임 = 프레임.reset_index(drop=True)\n",
    "    국제0803지표[제목] = 프레임"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CDS</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Last Price</td>\n",
       "      <td>D-1</td>\n",
       "      <td>D-7</td>\n",
       "      <td>MTD</td>\n",
       "      <td>QTD</td>\n",
       "      <td>YTD</td>\n",
       "      <td>Yr High</td>\n",
       "      <td>Yr Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>China</td>\n",
       "      <td>101</td>\n",
       "      <td>0.831</td>\n",
       "      <td>-0.501</td>\n",
       "      <td>0.831</td>\n",
       "      <td>8.333</td>\n",
       "      <td>14.013</td>\n",
       "      <td>104.336</td>\n",
       "      <td>66.838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Indonesia</td>\n",
       "      <td>186.757</td>\n",
       "      <td>3.249</td>\n",
       "      <td>5.545</td>\n",
       "      <td>3.249</td>\n",
       "      <td>30.208</td>\n",
       "      <td>26.445</td>\n",
       "      <td>183.748</td>\n",
       "      <td>127.162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Malaysia</td>\n",
       "      <td>152.159</td>\n",
       "      <td>4.173</td>\n",
       "      <td>10.277</td>\n",
       "      <td>4.173</td>\n",
       "      <td>24.706</td>\n",
       "      <td>45.332</td>\n",
       "      <td>151.325</td>\n",
       "      <td>73.677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Thailand</td>\n",
       "      <td>109</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5.5</td>\n",
       "      <td>6</td>\n",
       "      <td>119.511</td>\n",
       "      <td>80.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Russia</td>\n",
       "      <td>345.773</td>\n",
       "      <td>6.039</td>\n",
       "      <td>3.628</td>\n",
       "      <td>6.039</td>\n",
       "      <td>-27.167</td>\n",
       "      <td>-126.441</td>\n",
       "      <td>628.765</td>\n",
       "      <td>221.835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>South Africa</td>\n",
       "      <td>219.178</td>\n",
       "      <td>-0.717</td>\n",
       "      <td>-0.887</td>\n",
       "      <td>-0.717</td>\n",
       "      <td>11.862</td>\n",
       "      <td>29.295</td>\n",
       "      <td>240.794</td>\n",
       "      <td>159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Turkey</td>\n",
       "      <td>236.839</td>\n",
       "      <td>-1.569</td>\n",
       "      <td>6.529</td>\n",
       "      <td>-1.569</td>\n",
       "      <td>20.735</td>\n",
       "      <td>60.005</td>\n",
       "      <td>238.408</td>\n",
       "      <td>156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Brazil</td>\n",
       "      <td>292.362</td>\n",
       "      <td>4.642</td>\n",
       "      <td>-4.678</td>\n",
       "      <td>4.642</td>\n",
       "      <td>27.912</td>\n",
       "      <td>95.564</td>\n",
       "      <td>306.576</td>\n",
       "      <td>123.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Korea</td>\n",
       "      <td>54.165</td>\n",
       "      <td>0.331</td>\n",
       "      <td>-0.751</td>\n",
       "      <td>0.331</td>\n",
       "      <td>2.08</td>\n",
       "      <td>-0.326</td>\n",
       "      <td>67.609</td>\n",
       "      <td>45.491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Last Price</td>\n",
       "      <td>D-1</td>\n",
       "      <td>D-7</td>\n",
       "      <td>MTD</td>\n",
       "      <td>QTD</td>\n",
       "      <td>YTD</td>\n",
       "      <td>Yr High</td>\n",
       "      <td>Yr Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Japan</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.713</td>\n",
       "      <td>0</td>\n",
       "      <td>4.511</td>\n",
       "      <td>-27.5</td>\n",
       "      <td>73.833</td>\n",
       "      <td>31.335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Italy</td>\n",
       "      <td>114.915</td>\n",
       "      <td>-0.611</td>\n",
       "      <td>-0.215</td>\n",
       "      <td>-0.611</td>\n",
       "      <td>6.397</td>\n",
       "      <td>-24.382</td>\n",
       "      <td>160.16</td>\n",
       "      <td>84.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Spain</td>\n",
       "      <td>94.888</td>\n",
       "      <td>-0.057</td>\n",
       "      <td>2.668</td>\n",
       "      <td>-0.057</td>\n",
       "      <td>9.649</td>\n",
       "      <td>-2.852</td>\n",
       "      <td>130.438</td>\n",
       "      <td>56.493</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               0           1      2       3      4       5        6        7  \\\n",
       "0            CDS         NaN    NaN     NaN    NaN     NaN      NaN      NaN   \n",
       "1            NaN  Last Price    D-1     D-7    MTD     QTD      YTD  Yr High   \n",
       "2          China         101  0.831  -0.501  0.831   8.333   14.013  104.336   \n",
       "3      Indonesia     186.757  3.249   5.545  3.249  30.208   26.445  183.748   \n",
       "4       Malaysia     152.159  4.173  10.277  4.173  24.706   45.332  151.325   \n",
       "5       Thailand         109      2       1      2     5.5        6  119.511   \n",
       "6         Russia     345.773  6.039   3.628  6.039 -27.167 -126.441  628.765   \n",
       "7   South Africa     219.178 -0.717  -0.887 -0.717  11.862   29.295  240.794   \n",
       "8         Turkey     236.839 -1.569   6.529 -1.569  20.735   60.005  238.408   \n",
       "9         Brazil     292.362  4.642  -4.678  4.642  27.912   95.564  306.576   \n",
       "10         Korea      54.165  0.331  -0.751  0.331    2.08   -0.326   67.609   \n",
       "11           NaN  Last Price    D-1     D-7    MTD     QTD      YTD  Yr High   \n",
       "12         Japan          40      0  -0.713      0   4.511    -27.5   73.833   \n",
       "13         Italy     114.915 -0.611  -0.215 -0.611   6.397  -24.382   160.16   \n",
       "14         Spain      94.888 -0.057   2.668 -0.057   9.649   -2.852  130.438   \n",
       "\n",
       "          8  \n",
       "0       NaN  \n",
       "1    Yr Low  \n",
       "2    66.838  \n",
       "3   127.162  \n",
       "4    73.677  \n",
       "5     80.84  \n",
       "6   221.835  \n",
       "7       159  \n",
       "8       156  \n",
       "9    123.25  \n",
       "10   45.491  \n",
       "11   Yr Low  \n",
       "12   31.335  \n",
       "13    84.16  \n",
       "14   56.493  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "국제0803지표['cds']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>True</td>\n",
       "      <td>True</td>\n",
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       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
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       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
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       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
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       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        0      1      2      3      4      5      6      7      8\n",
       "0    True  False  False  False  False  False  False  False  False\n",
       "1   False   True   True   True   True   True   True   True   True\n",
       "2    True   True   True   True   True   True   True   True   True\n",
       "3    True   True   True   True   True   True   True   True   True\n",
       "4    True   True   True   True   True   True   True   True   True\n",
       "5    True   True   True   True   True   True   True   True   True\n",
       "6    True   True   True   True   True   True   True   True   True\n",
       "7    True   True   True   True   True   True   True   True   True\n",
       "8    True  False  False  False  False  False  False  False  False\n",
       "9   False   True   True   True   True   True   True   True   True\n",
       "10   True   True   True   True   True   True   True   True   True\n",
       "11   True   True   True   True   True   True   True   True   True\n",
       "12   True   True   True   True   True   True   True   True   True\n",
       "13   True   True   True   True   True   True   True   True   True\n",
       "14   True   True   True   True   True   True   True   True   True\n",
       "15   True   True   True   True   True   True   True   True   True\n",
       "16   True  False  False  False  False  False  False  False  False\n",
       "17  False   True   True   True   True   True   True   True   True\n",
       "18   True   True   True   True   True   True   True   True   True\n",
       "19   True   True   True   True   True   True   True   True   True\n",
       "20   True   True   True   True   True   True   True   True   True\n",
       "21   True   True   True   True   True   True   True   True   True\n",
       "22   True   True   True   True   True   True   True   True   True\n",
       "23   True  False  False  False  False  False  False  False  False\n",
       "24   True  False  False  False  False  False  False  False  False\n",
       "25  False   True   True   True   True   True   True   True   True\n",
       "26   True   True   True   True   True   True   True   True   True\n",
       "27   True   True   True   True   True   True   True   True   True\n",
       "28   True   True   True   True   True   True   True   True   True\n",
       "29   True  False  False  False  False  False  False  False  False\n",
       "30  False   True   True   True   True   True   True   True   True\n",
       "31   True   True   True   True   True   True   True   True   True\n",
       "32   True   True   True   True   True   True   True   True   True\n",
       "33   True   True   True   True   True   True   True   True   True\n",
       "34   True   True   True   True   True   True   True   True   True\n",
       "35   True   True   True   True   True   True   True   True   True\n",
       "36   True   True   True   True   True   True   True   True   True\n",
       "37   True   True   True   True   True   True   True   True   True\n",
       "38   True   True   True   True   True   True   True   True   True\n",
       "39   True   True   True   True   True   True   True   True   True\n",
       "40  False   True   True   True   True   True   True   True   True\n",
       "41   True   True   True   True   True   True   True   True   True\n",
       "42   True   True   True   True   True   True   True   True   True\n",
       "43   True   True   True   True   True   True   True   True   True"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "국제0803.notnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      True\n",
       "1     False\n",
       "2     False\n",
       "3     False\n",
       "4     False\n",
       "5     False\n",
       "6     False\n",
       "7     False\n",
       "8      True\n",
       "9     False\n",
       "10    False\n",
       "11    False\n",
       "12    False\n",
       "13    False\n",
       "14    False\n",
       "15    False\n",
       "16     True\n",
       "17    False\n",
       "18    False\n",
       "19    False\n",
       "20    False\n",
       "21    False\n",
       "22    False\n",
       "23     True\n",
       "24     True\n",
       "25    False\n",
       "26    False\n",
       "27    False\n",
       "28    False\n",
       "29     True\n",
       "30    False\n",
       "31    False\n",
       "32    False\n",
       "33    False\n",
       "34    False\n",
       "35    False\n",
       "36    False\n",
       "37    False\n",
       "38    False\n",
       "39    False\n",
       "40    False\n",
       "41    False\n",
       "42    False\n",
       "43    False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "국제0803.notnull().sum(1) == 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     0\n",
       "1     8\n",
       "2    16\n",
       "3    23\n",
       "4    24\n",
       "5    29\n",
       "dtype: int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "색인시작목록 = pd.Series(국제0803.index[국제0803.notnull().sum(1) == 1])\n",
    "색인시작목록"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1     8\n",
       "2    16\n",
       "3    23\n",
       "4    24\n",
       "5    29\n",
       "0    44\n",
       "dtype: int64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "색인종료목록 = 색인시작목록[1:]\n",
    "색인종료목록 = 색인종료목록.append(pd.Series(len(국제0803)))\n",
    "색인종료목록"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0, 8), (8, 16), (16, 23), (23, 24), (24, 29), (29, 44)]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "색인범위목록 = list(zip(색인시작목록, 색인종료목록))\n",
    "색인범위목록"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "프레임목록 = []\n",
    "for 범위 in 색인범위목록:\n",
    "    시작, 종료 = 범위\n",
    "    프레임 = 국제0803.iloc[시작:종료]\n",
    "    프레임 = 프레임.reset_index(drop=True)\n",
    "    프레임목록.append(프레임)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Equity</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Last Price</td>\n",
       "      <td>D-1</td>\n",
       "      <td>D-7</td>\n",
       "      <td>MTD</td>\n",
       "      <td>QTD</td>\n",
       "      <td>YTD</td>\n",
       "      <td>Yr High</td>\n",
       "      <td>Yr Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>MSCI AC World</td>\n",
       "      <td>425.32</td>\n",
       "      <td>-0.00342097</td>\n",
       "      <td>0.0141638</td>\n",
       "      <td>-0.00342097</td>\n",
       "      <td>-0.00561115</td>\n",
       "      <td>0.0196586</td>\n",
       "      <td>442.7</td>\n",
       "      <td>392.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>S&amp;P 500</td>\n",
       "      <td>2098.04</td>\n",
       "      <td>-0.00275686</td>\n",
       "      <td>0.0147028</td>\n",
       "      <td>-0.00275686</td>\n",
       "      <td>0.0150366</td>\n",
       "      <td>0.0190102</td>\n",
       "      <td>2130.82</td>\n",
       "      <td>1862.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Euro Stoxx 50</td>\n",
       "      <td>3635.4</td>\n",
       "      <td>0.00963982</td>\n",
       "      <td>0.0348126</td>\n",
       "      <td>0.00963982</td>\n",
       "      <td>-0.0214978</td>\n",
       "      <td>0.155405</td>\n",
       "      <td>3828.78</td>\n",
       "      <td>2874.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>DAX</td>\n",
       "      <td>11443.7</td>\n",
       "      <td>0.0119135</td>\n",
       "      <td>0.0350313</td>\n",
       "      <td>0.0119135</td>\n",
       "      <td>-0.0437581</td>\n",
       "      <td>0.167066</td>\n",
       "      <td>12374.7</td>\n",
       "      <td>8571.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Nikkei 225</td>\n",
       "      <td>20548.1</td>\n",
       "      <td>-0.00180372</td>\n",
       "      <td>0.00973017</td>\n",
       "      <td>-0.00180372</td>\n",
       "      <td>0.0572691</td>\n",
       "      <td>0.17749</td>\n",
       "      <td>20868</td>\n",
       "      <td>14532.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>상해</td>\n",
       "      <td>3622.91</td>\n",
       "      <td>-0.0111417</td>\n",
       "      <td>-0.0275537</td>\n",
       "      <td>-0.0111417</td>\n",
       "      <td>-0.062378</td>\n",
       "      <td>0.120021</td>\n",
       "      <td>5166.35</td>\n",
       "      <td>2187.67</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               0           1           2           3           4           5  \\\n",
       "0         Equity         NaN         NaN         NaN         NaN         NaN   \n",
       "1            NaN  Last Price         D-1         D-7         MTD         QTD   \n",
       "2  MSCI AC World      425.32 -0.00342097   0.0141638 -0.00342097 -0.00561115   \n",
       "3        S&P 500     2098.04 -0.00275686   0.0147028 -0.00275686   0.0150366   \n",
       "4  Euro Stoxx 50      3635.4  0.00963982   0.0348126  0.00963982  -0.0214978   \n",
       "5            DAX     11443.7   0.0119135   0.0350313   0.0119135  -0.0437581   \n",
       "6     Nikkei 225     20548.1 -0.00180372  0.00973017 -0.00180372   0.0572691   \n",
       "7             상해     3622.91  -0.0111417  -0.0275537  -0.0111417   -0.062378   \n",
       "\n",
       "           6        7        8  \n",
       "0        NaN      NaN      NaN  \n",
       "1        YTD  Yr High   Yr Low  \n",
       "2  0.0196586    442.7   392.13  \n",
       "3  0.0190102  2130.82  1862.49  \n",
       "4   0.155405  3828.78  2874.65  \n",
       "5   0.167066  12374.7  8571.95  \n",
       "6    0.17749    20868  14532.5  \n",
       "7   0.120021  5166.35  2187.67  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "프레임목록[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CDS</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Last Price</td>\n",
       "      <td>D-1</td>\n",
       "      <td>D-7</td>\n",
       "      <td>MTD</td>\n",
       "      <td>QTD</td>\n",
       "      <td>YTD</td>\n",
       "      <td>Yr High</td>\n",
       "      <td>Yr Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>China</td>\n",
       "      <td>101</td>\n",
       "      <td>0.831</td>\n",
       "      <td>-0.501</td>\n",
       "      <td>0.831</td>\n",
       "      <td>8.333</td>\n",
       "      <td>14.013</td>\n",
       "      <td>104.336</td>\n",
       "      <td>66.838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Indonesia</td>\n",
       "      <td>186.757</td>\n",
       "      <td>3.249</td>\n",
       "      <td>5.545</td>\n",
       "      <td>3.249</td>\n",
       "      <td>30.208</td>\n",
       "      <td>26.445</td>\n",
       "      <td>183.748</td>\n",
       "      <td>127.162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Malaysia</td>\n",
       "      <td>152.159</td>\n",
       "      <td>4.173</td>\n",
       "      <td>10.277</td>\n",
       "      <td>4.173</td>\n",
       "      <td>24.706</td>\n",
       "      <td>45.332</td>\n",
       "      <td>151.325</td>\n",
       "      <td>73.677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Thailand</td>\n",
       "      <td>109</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5.5</td>\n",
       "      <td>6</td>\n",
       "      <td>119.511</td>\n",
       "      <td>80.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Russia</td>\n",
       "      <td>345.773</td>\n",
       "      <td>6.039</td>\n",
       "      <td>3.628</td>\n",
       "      <td>6.039</td>\n",
       "      <td>-27.167</td>\n",
       "      <td>-126.441</td>\n",
       "      <td>628.765</td>\n",
       "      <td>221.835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>South Africa</td>\n",
       "      <td>219.178</td>\n",
       "      <td>-0.717</td>\n",
       "      <td>-0.887</td>\n",
       "      <td>-0.717</td>\n",
       "      <td>11.862</td>\n",
       "      <td>29.295</td>\n",
       "      <td>240.794</td>\n",
       "      <td>159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Turkey</td>\n",
       "      <td>236.839</td>\n",
       "      <td>-1.569</td>\n",
       "      <td>6.529</td>\n",
       "      <td>-1.569</td>\n",
       "      <td>20.735</td>\n",
       "      <td>60.005</td>\n",
       "      <td>238.408</td>\n",
       "      <td>156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Brazil</td>\n",
       "      <td>292.362</td>\n",
       "      <td>4.642</td>\n",
       "      <td>-4.678</td>\n",
       "      <td>4.642</td>\n",
       "      <td>27.912</td>\n",
       "      <td>95.564</td>\n",
       "      <td>306.576</td>\n",
       "      <td>123.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Korea</td>\n",
       "      <td>54.165</td>\n",
       "      <td>0.331</td>\n",
       "      <td>-0.751</td>\n",
       "      <td>0.331</td>\n",
       "      <td>2.08</td>\n",
       "      <td>-0.326</td>\n",
       "      <td>67.609</td>\n",
       "      <td>45.491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Last Price</td>\n",
       "      <td>D-1</td>\n",
       "      <td>D-7</td>\n",
       "      <td>MTD</td>\n",
       "      <td>QTD</td>\n",
       "      <td>YTD</td>\n",
       "      <td>Yr High</td>\n",
       "      <td>Yr Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Japan</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.713</td>\n",
       "      <td>0</td>\n",
       "      <td>4.511</td>\n",
       "      <td>-27.5</td>\n",
       "      <td>73.833</td>\n",
       "      <td>31.335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Italy</td>\n",
       "      <td>114.915</td>\n",
       "      <td>-0.611</td>\n",
       "      <td>-0.215</td>\n",
       "      <td>-0.611</td>\n",
       "      <td>6.397</td>\n",
       "      <td>-24.382</td>\n",
       "      <td>160.16</td>\n",
       "      <td>84.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Spain</td>\n",
       "      <td>94.888</td>\n",
       "      <td>-0.057</td>\n",
       "      <td>2.668</td>\n",
       "      <td>-0.057</td>\n",
       "      <td>9.649</td>\n",
       "      <td>-2.852</td>\n",
       "      <td>130.438</td>\n",
       "      <td>56.493</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               0           1      2       3      4       5        6        7  \\\n",
       "0            CDS         NaN    NaN     NaN    NaN     NaN      NaN      NaN   \n",
       "1            NaN  Last Price    D-1     D-7    MTD     QTD      YTD  Yr High   \n",
       "2          China         101  0.831  -0.501  0.831   8.333   14.013  104.336   \n",
       "3      Indonesia     186.757  3.249   5.545  3.249  30.208   26.445  183.748   \n",
       "4       Malaysia     152.159  4.173  10.277  4.173  24.706   45.332  151.325   \n",
       "5       Thailand         109      2       1      2     5.5        6  119.511   \n",
       "6         Russia     345.773  6.039   3.628  6.039 -27.167 -126.441  628.765   \n",
       "7   South Africa     219.178 -0.717  -0.887 -0.717  11.862   29.295  240.794   \n",
       "8         Turkey     236.839 -1.569   6.529 -1.569  20.735   60.005  238.408   \n",
       "9         Brazil     292.362  4.642  -4.678  4.642  27.912   95.564  306.576   \n",
       "10         Korea      54.165  0.331  -0.751  0.331    2.08   -0.326   67.609   \n",
       "11           NaN  Last Price    D-1     D-7    MTD     QTD      YTD  Yr High   \n",
       "12         Japan          40      0  -0.713      0   4.511    -27.5   73.833   \n",
       "13         Italy     114.915 -0.611  -0.215 -0.611   6.397  -24.382   160.16   \n",
       "14         Spain      94.888 -0.057   2.668 -0.057   9.649   -2.852  130.438   \n",
       "\n",
       "          8  \n",
       "0       NaN  \n",
       "1    Yr Low  \n",
       "2    66.838  \n",
       "3   127.162  \n",
       "4    73.677  \n",
       "5     80.84  \n",
       "6   221.835  \n",
       "7       159  \n",
       "8       156  \n",
       "9    123.25  \n",
       "10   45.491  \n",
       "11   Yr Low  \n",
       "12   31.335  \n",
       "13    84.16  \n",
       "14   56.493  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "프레임목록[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "def 데이터프레임추출(시트프레임):\n",
    "    색인시작목록 = pd.Series(시트프레임.index[시트프레임.notnull().sum(1) == 1])\n",
    "    색인종료목록 = 색인시작목록[1:]\n",
    "    색인종료목록 = 색인종료목록.append(pd.Series(len(시트프레임)))\n",
    "    색인범위목록 = list(zip(색인시작목록, 색인종료목록))\n",
    "    \n",
    "    프레임목록 = []\n",
    "    for 범위 in 색인범위목록:\n",
    "        # 범위가 2보다 작으면 건너뛴다\n",
    "        if 범위[1] - 범위[0] < 2:\n",
    "            continue\n",
    "            \n",
    "        시작, 종료 = 범위\n",
    "        프레임 = 시트프레임.iloc[시작:종료]\n",
    "        프레임 = 프레임.reset_index(drop=True)\n",
    "        프레임목록.append(프레임)\n",
    "        \n",
    "    return 프레임목록"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "               0           1           2           3           4           5  \\\n",
      "0         Equity         NaN         NaN         NaN         NaN         NaN   \n",
      "1            NaN  Last Price         D-1         D-7         MTD         QTD   \n",
      "2  MSCI AC World      425.32 -0.00342097   0.0141638 -0.00342097 -0.00561115   \n",
      "3        S&P 500     2098.04 -0.00275686   0.0147028 -0.00275686   0.0150366   \n",
      "4  Euro Stoxx 50      3635.4  0.00963982   0.0348126  0.00963982  -0.0214978   \n",
      "5            DAX     11443.7   0.0119135   0.0350313   0.0119135  -0.0437581   \n",
      "6     Nikkei 225     20548.1 -0.00180372  0.00973017 -0.00180372   0.0572691   \n",
      "7             상해     3622.91  -0.0111417  -0.0275537  -0.0111417   -0.062378   \n",
      "\n",
      "           6        7        8  \n",
      "0        NaN      NaN      NaN  \n",
      "1        YTD  Yr High   Yr Low  \n",
      "2  0.0196586    442.7   392.13  \n",
      "3  0.0190102  2130.82  1862.49  \n",
      "4   0.155405  3828.78  2874.65  \n",
      "5   0.167066  12374.7  8571.95  \n",
      "6    0.17749    20868  14532.5  \n",
      "7   0.120021  5166.35  2187.67  \n",
      "                0              1    2     3     4      5     6        7  \\\n",
      "0    Fixed Income            NaN  NaN   NaN   NaN    NaN   NaN      NaN   \n",
      "1             NaN  Last Price(%)  D-1   D-7   MTD    QTD   YTD  Yr High   \n",
      "2         UST 10Y          2.148   -3 -6.95 -3.21  30.91 -2.32   2.6198   \n",
      "3          UST 2Y         0.6646    0  1.51   0.4  18.84  0.01   0.7393   \n",
      "4  Spread(10Y-2Y)         1.4834   -3 -8.46 -3.61  12.07 -2.33   2.0562   \n",
      "5        Bund 10Y          0.628   -1  -6.3  -1.6   43.4   8.7    1.167   \n",
      "6         Bund 2Y         -0.244    0  -1.8  -0.9    1.3 -13.4    0.027   \n",
      "7  Spread(10Y-2Y)          0.872   -1  -4.5  -0.7   42.1  22.1    1.157   \n",
      "\n",
      "        8  \n",
      "0     NaN  \n",
      "1  Yr Low  \n",
      "2  1.6407  \n",
      "3  0.3076  \n",
      "4  1.1919  \n",
      "5   0.075  \n",
      "6  -0.283  \n",
      "7   0.342  \n",
      "              0           1           2          3           4           5  \\\n",
      "0            FX         NaN         NaN        NaN         NaN         NaN   \n",
      "1           NaN  Last Price         D-1        D-7         MTD         QTD   \n",
      "2  Dollar Index      97.494  0.00162324  0.0102796  0.00162324  0.00982961   \n",
      "3           EUR       1.095 -0.00309541 -0.0124459 -0.00309541 -0.00173215   \n",
      "4           GBP      1.5587 -0.00224043  0.0017996 -0.00224043   0.0447051   \n",
      "5           JPY      124.03 -0.00112876 -0.0062888 -0.00112876  -0.0407966   \n",
      "6           AUD      0.7286  -0.0030104  0.0023387  -0.0030104  -0.0454605   \n",
      "\n",
      "             6        7        8  \n",
      "0          NaN      NaN      NaN  \n",
      "1          YTD  Yr High   Yr Low  \n",
      "2    0.0800386   100.33   81.328  \n",
      "3   -0.0948917   1.3422   1.0496  \n",
      "4  0.000641972   1.6886   1.4632  \n",
      "5   -0.0342659   102.03  125.612  \n",
      "6    -0.108746   0.9378   0.7269  \n",
      "            0           1          2           3          4          5  \\\n",
      "0  Commodity          NaN        NaN         NaN        NaN        NaN   \n",
      "1         NaN  Last Price        D-1         D-7        MTD        QTD   \n",
      "2         WTI       45.17 -0.0413837  -0.0468453 -0.0413837  -0.154278   \n",
      "3        Gold      1089.4  -0.005205 -0.00683745  -0.005205 -0.0947316   \n",
      "4        Corn       376.5  -0.012459  -0.0182529  -0.012459 -0.0828258   \n",
      "\n",
      "           6        7       8  \n",
      "0        NaN      NaN     NaN  \n",
      "1        YTD  Yr High  Yr Low  \n",
      "2  -0.158845    93.38   47.12  \n",
      "3 -0.0805975     1321    1086  \n",
      "4 -0.0720887   451.75   363.5  \n",
      "               0           1      2       3      4       5        6        7  \\\n",
      "0            CDS         NaN    NaN     NaN    NaN     NaN      NaN      NaN   \n",
      "1            NaN  Last Price    D-1     D-7    MTD     QTD      YTD  Yr High   \n",
      "2          China         101  0.831  -0.501  0.831   8.333   14.013  104.336   \n",
      "3      Indonesia     186.757  3.249   5.545  3.249  30.208   26.445  183.748   \n",
      "4       Malaysia     152.159  4.173  10.277  4.173  24.706   45.332  151.325   \n",
      "5       Thailand         109      2       1      2     5.5        6  119.511   \n",
      "6         Russia     345.773  6.039   3.628  6.039 -27.167 -126.441  628.765   \n",
      "7   South Africa     219.178 -0.717  -0.887 -0.717  11.862   29.295  240.794   \n",
      "8         Turkey     236.839 -1.569   6.529 -1.569  20.735   60.005  238.408   \n",
      "9         Brazil     292.362  4.642  -4.678  4.642  27.912   95.564  306.576   \n",
      "10         Korea      54.165  0.331  -0.751  0.331    2.08   -0.326   67.609   \n",
      "11           NaN  Last Price    D-1     D-7    MTD     QTD      YTD  Yr High   \n",
      "12         Japan          40      0  -0.713      0   4.511    -27.5   73.833   \n",
      "13         Italy     114.915 -0.611  -0.215 -0.611   6.397  -24.382   160.16   \n",
      "14         Spain      94.888 -0.057   2.668 -0.057   9.649   -2.852  130.438   \n",
      "\n",
      "          8  \n",
      "0       NaN  \n",
      "1    Yr Low  \n",
      "2    66.838  \n",
      "3   127.162  \n",
      "4    73.677  \n",
      "5     80.84  \n",
      "6   221.835  \n",
      "7       159  \n",
      "8       156  \n",
      "9    123.25  \n",
      "10   45.491  \n",
      "11   Yr Low  \n",
      "12   31.335  \n",
      "13    84.16  \n",
      "14   56.493  \n"
     ]
    }
   ],
   "source": [
    "프레임목록 = 데이터프레임추출(국제0803)\n",
    "for 표 in 프레임목록:\n",
    "    print(표)"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}