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beomjunshin-ben/pandas_from_A_to_C.ipynb

Created April 26, 2016 01:39
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pandas_from_A_to_C.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# python data analysis with pandas"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"“pandas allows us to focus more on research and less on programming. We have found pandas easy to learn, easy to use, and easy to maintain. The bottom line is that it has increased our productivity.”\n",
"\n",
"- Roni Israelov, PhD Portfolio Manager"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"http://bayes.shasta.kr:19999/#system_ram"
]
},
{
"cell_type": "code",
"execution_count": 93,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"from matplotlib import rc, rcParams\n",
"import seaborn as sns\n",
"import pandas as pd\n",
"import numpy as np\n",
"\n",
"import pydata.wisefn as qw # My Packages\n",
"\n",
"sns.set_style(\"whitegrid\")\n",
"rcParams.update({\n",
" 'savefig.dpi': 100,\n",
" 'savefig.bbox': 'tight',\n",
" 'figure.figsize': (8, 6),\n",
" 'font.family': font_name\n",
"}) # \",\".join(rcParams.keys())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Data Loading"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Read csv(tsv) file"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"df_kospi200 = pd.read_csv(\"data/1990_kospi200.tsv\", delimiter=\"\\t\", encoding=\"utf8\")"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>dt</th>\n",
" <th>stk_cd</th>\n",
" <th>open_prc</th>\n",
" <th>high_prc</th>\n",
" <th>low_prc</th>\n",
" <th>close_prc</th>\n",
" <th>trd_amt</th>\n",
" <th>marketvalue</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>19900103</td>\n",
" <td>10</td>\n",
" <td>45664.160236</td>\n",
" <td>49176.787946</td>\n",
" <td>45664.160236</td>\n",
" <td>49176.787946</td>\n",
" <td>2641758000</td>\n",
" <td>1540000000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>19900104</td>\n",
" <td>10</td>\n",
" <td>49176.787946</td>\n",
" <td>51284.364573</td>\n",
" <td>48474.262404</td>\n",
" <td>51284.364573</td>\n",
" <td>5963383000</td>\n",
" <td>1606000000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>19900105</td>\n",
" <td>10</td>\n",
" <td>51284.364573</td>\n",
" <td>51986.890115</td>\n",
" <td>50230.576259</td>\n",
" <td>50581.839030</td>\n",
" <td>4896930000</td>\n",
" <td>1584000000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" dt stk_cd open_prc high_prc low_prc close_prc \\\n",
"0 19900103 10 45664.160236 49176.787946 45664.160236 49176.787946 \n",
"1 19900104 10 49176.787946 51284.364573 48474.262404 51284.364573 \n",
"2 19900105 10 51284.364573 51986.890115 50230.576259 50581.839030 \n",
"\n",
" trd_amt marketvalue \n",
"0 2641758000 1540000000000 \n",
"1 5963383000 1606000000000 \n",
"2 4896930000 1584000000000 "
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_kospi200.head(3)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Read excel file"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"df_wics = qw.read_quantiwise(\"data/wics.xlsx\", sheetname=\"meta\", is_first_date_index=False, row_colname=13, row_data_start=14)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Code</th>\n",
" <th>Name</th>\n",
" <th>결산월</th>\n",
" <th>주식코드</th>\n",
" <th>WICS업종명(대)</th>\n",
" <th>WICS업종코드(대)</th>\n",
" <th>WICS업종명(중)</th>\n",
" <th>WICS업종코드(중)</th>\n",
" <th>WICS업종명(소)</th>\n",
" <th>WICS업종코드(소)</th>\n",
" <th>WI26업종명(대)</th>\n",
" <th>WI26업종코드(대)</th>\n",
" <th>WI26업종명(중)</th>\n",
" <th>KOSPI200 구성종목여부(1:해당, 0:미해당)</th>\n",
" <th>WI26업종코드(중)</th>\n",
" <th>WMI500 시가총액규모별(1:대형주,2:중형주,3:소형주,0:WMI500미해당)</th>\n",
" <th>시가총액</th>\n",
" <th>시가총액(전체)</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>A000020</td>\n",
" <td>동화약품</td>\n",
" <td>12</td>\n",
" <td>A000020</td>\n",
" <td>건강관리</td>\n",
" <td>G35</td>\n",
" <td>제약과생물공학</td>\n",
" <td>G3520</td>\n",
" <td>제약</td>\n",
" <td>G352020</td>\n",
" <td>건강관리</td>\n",
" <td>WI410</td>\n",
" <td>제약</td>\n",
" <td>0</td>\n",
" <td>WI41010</td>\n",
" <td>0</td>\n",
" <td>226524221700</td>\n",
" <td>226524221700</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>A000030</td>\n",
" <td>우리은행</td>\n",
" <td>12</td>\n",
" <td>A000030</td>\n",
" <td>금융</td>\n",
" <td>G40</td>\n",
" <td>은행</td>\n",
" <td>G4010</td>\n",
" <td>은행</td>\n",
" <td>G401010</td>\n",
" <td>은행</td>\n",
" <td>WI500</td>\n",
" <td>은행</td>\n",
" <td>1</td>\n",
" <td>WI50010</td>\n",
" <td>1</td>\n",
" <td>6394960000000</td>\n",
" <td>6394960000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Code Name 결산월 주식코드 WICS업종명(대) WICS업종코드(대) WICS업종명(중) WICS업종코드(중) \\\n",
"0 A000020 동화약품 12 A000020 건강관리 G35 제약과생물공학 G3520 \n",
"1 A000030 우리은행 12 A000030 금융 G40 은행 G4010 \n",
"\n",
" WICS업종명(소) WICS업종코드(소) WI26업종명(대) WI26업종코드(대) WI26업종명(중) \\\n",
"0 제약 G352020 건강관리 WI410 제약 \n",
"1 은행 G401010 은행 WI500 은행 \n",
"\n",
" KOSPI200 구성종목여부(1:해당, 0:미해당) WI26업종코드(중) \\\n",
"0 0 WI41010 \n",
"1 1 WI50010 \n",
"\n",
" WMI500 시가총액규모별(1:대형주,2:중형주,3:소형주,0:WMI500미해당) 시가총액 시가총액(전체) \n",
"0 0 226524221700 226524221700 \n",
"1 1 6394960000000 6394960000000 "
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_wics.head(2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Read from database"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import pymssql\n",
"import os\n",
"# Put Server information to enviroment variables.\n",
"conn = pymssql.connect(\n",
" server=os.environ[\"LILIAC\"], \n",
" user=os.environ[\"USER\"], \n",
" password=os.environ[\"PASSWORD\"], \n",
" database=os.environ[\"DATABASE\"], \n",
" port=os.environ[\"PORT\"], \n",
" charset=os.environ[\"CHARSET\"]\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"df_ts_stk_data = pd.read_sql(\"select top 100 * from wise..ts_stk_data\", conn)\n",
"conn.close()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>TRD_DT</th>\n",
" <th>STK_CD</th>\n",
" <th>RIGHTOFF_TYP</th>\n",
" <th>PRE_CLOSE_PRC</th>\n",
" <th>BASE_PRC</th>\n",
" <th>SUBST_PRC</th>\n",
" <th>FACE_VAL</th>\n",
" <th>EVAL_PRC</th>\n",
" <th>UL_PRC</th>\n",
" <th>LL_PRC</th>\n",
" <th>...</th>\n",
" <th>MKT_VAL</th>\n",
" <th>MKT_SCH_VAL</th>\n",
" <th>TRD_QTY_TO</th>\n",
" <th>TRD_AMT_TO</th>\n",
" <th>MOD_TM</th>\n",
" <th>MOD_LOC</th>\n",
" <th>FRGN_STK_CNT_LMT</th>\n",
" <th>FRGN_MKT_VAL</th>\n",
" <th>FRGN_STK_CNT_LMT_RT</th>\n",
" <th>BASE_PRC_RPT</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>95</th>\n",
" <td>1990-04-30</td>\n",
" <td>000010</td>\n",
" <td>0</td>\n",
" <td>10900.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>5000.0</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>...</td>\n",
" <td>1.339000e+12</td>\n",
" <td>0.0</td>\n",
" <td>0.00314</td>\n",
" <td>0.00319</td>\n",
" <td>2010-03-25 10:55:45.370</td>\n",
" <td>ENGPR74</td>\n",
" <td>None</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>None</td>\n",
" </tr>\n",
" <tr>\n",
" <th>96</th>\n",
" <td>1990-05-01</td>\n",
" <td>000010</td>\n",
" <td>0</td>\n",
" <td>10300.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>5000.0</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>...</td>\n",
" <td>1.417000e+12</td>\n",
" <td>0.0</td>\n",
" <td>0.00245</td>\n",
" <td>0.00245</td>\n",
" <td>2010-03-25 10:55:45.370</td>\n",
" <td>ENGPR74</td>\n",
" <td>None</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>None</td>\n",
" </tr>\n",
" <tr>\n",
" <th>97</th>\n",
" <td>1990-05-03</td>\n",
" <td>000010</td>\n",
" <td>0</td>\n",
" <td>10900.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>5000.0</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>...</td>\n",
" <td>1.495000e+12</td>\n",
" <td>0.0</td>\n",
" <td>0.00139</td>\n",
" <td>0.00139</td>\n",
" <td>2010-03-25 10:55:45.370</td>\n",
" <td>ENGPR74</td>\n",
" <td>None</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>None</td>\n",
" </tr>\n",
" <tr>\n",
" <th>98</th>\n",
" <td>1990-05-04</td>\n",
" <td>000010</td>\n",
" <td>0</td>\n",
" <td>11500.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>5000.0</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>...</td>\n",
" <td>1.573000e+12</td>\n",
" <td>0.0</td>\n",
" <td>0.00978</td>\n",
" <td>0.00978</td>\n",
" <td>2010-03-25 10:55:45.370</td>\n",
" <td>ENGPR74</td>\n",
" <td>None</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>None</td>\n",
" </tr>\n",
" <tr>\n",
" <th>99</th>\n",
" <td>1990-05-07</td>\n",
" <td>000010</td>\n",
" <td>0</td>\n",
" <td>12100.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>5000.0</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>...</td>\n",
" <td>1.625000e+12</td>\n",
" <td>0.0</td>\n",
" <td>0.01150</td>\n",
" <td>0.01156</td>\n",
" <td>2010-03-25 10:55:45.370</td>\n",
" <td>ENGPR74</td>\n",
" <td>None</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>None</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 36 columns</p>\n",
"</div>"
],
"text/plain": [
" TRD_DT STK_CD RIGHTOFF_TYP PRE_CLOSE_PRC BASE_PRC SUBST_PRC \\\n",
"95 1990-04-30 000010 0 10900.0 0.0 0.0 \n",
"96 1990-05-01 000010 0 10300.0 0.0 0.0 \n",
"97 1990-05-03 000010 0 10900.0 0.0 0.0 \n",
"98 1990-05-04 000010 0 11500.0 0.0 0.0 \n",
"99 1990-05-07 000010 0 12100.0 0.0 0.0 \n",
"\n",
" FACE_VAL EVAL_PRC UL_PRC LL_PRC ... MKT_VAL MKT_SCH_VAL \\\n",
"95 5000.0 None None None ... 1.339000e+12 0.0 \n",
"96 5000.0 None None None ... 1.417000e+12 0.0 \n",
"97 5000.0 None None None ... 1.495000e+12 0.0 \n",
"98 5000.0 None None None ... 1.573000e+12 0.0 \n",
"99 5000.0 None None None ... 1.625000e+12 0.0 \n",
"\n",
" TRD_QTY_TO TRD_AMT_TO MOD_TM MOD_LOC FRGN_STK_CNT_LMT \\\n",
"95 0.00314 0.00319 2010-03-25 10:55:45.370 ENGPR74 None \n",
"96 0.00245 0.00245 2010-03-25 10:55:45.370 ENGPR74 None \n",
"97 0.00139 0.00139 2010-03-25 10:55:45.370 ENGPR74 None \n",
"98 0.00978 0.00978 2010-03-25 10:55:45.370 ENGPR74 None \n",
"99 0.01150 0.01156 2010-03-25 10:55:45.370 ENGPR74 None \n",
"\n",
" FRGN_MKT_VAL FRGN_STK_CNT_LMT_RT BASE_PRC_RPT \n",
"95 0.0 0.0 None \n",
"96 0.0 0.0 None \n",
"97 0.0 0.0 None \n",
"98 0.0 0.0 None \n",
"99 0.0 0.0 None \n",
"\n",
"[5 rows x 36 columns]"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_ts_stk_data.tail()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Read from serialized file (fast!)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<class 'pandas.core.panel.Panel'>\n",
"Dimensions: 157 (items) x 183 (major_axis) x 435 (minor_axis)\n",
"Items axis: err_fy1 to 거래정지여부\n",
"Major_axis axis: 20010131 to 20160331\n",
"Minor_axis axis: A000010 to A192820"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_factors = pd.read_pickle(\"data/kospi200_factors.pkl\")\n",
"df_factors"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Read from HTMl file (it may be difficult)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>기업명</th>\n",
" <th>기관명/작성자</th>\n",
" <th>투자의견</th>\n",
" <th>이전의견</th>\n",
" <th>목표주가</th>\n",
" <th>이전목표</th>\n",
" <th>전일수정주가</th>\n",
" <th>요약</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>하이트진로(000080)</td>\n",
" <td>신영증권 [김윤오]</td>\n",
" <td>중립</td>\n",
" <td>중립</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>27700.0</td>\n",
" <td>▶ 최근 동향 점검 ▶ 오비맥주의 가격 인상 수혜 기대 ▶ 하이트진로의 모기업, 재...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>대림산업(000210)</td>\n",
" <td>한국투자증권 [이경자,강승균]</td>\n",
" <td>매수</td>\n",
" <td>매수</td>\n",
" <td>130000.0</td>\n",
" <td>130000.0</td>\n",
" <td>88600.0</td>\n",
" <td>▶ 해외 현장 리스크 지고 있음은 분명 ▶ 주택과 유화는 이미 연간 타겟의 절반 달...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>현대증권(003450)</td>\n",
" <td>이베스트투자증권 [전배승]</td>\n",
" <td>매수</td>\n",
" <td>NaN</td>\n",
" <td>10000.0</td>\n",
" <td>NaN</td>\n",
" <td>7170.0</td>\n",
" <td>▶ 투자의견 매수, 목표주가 10,000원 제시 ▶ 환골탈태 가능성에 무게를 둔다 ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>SK(003600)</td>\n",
" <td>미래에셋증권 [이진우]</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>▶ 1Q16의 경우, SK이노베이션 호실적에 따른 실적 선전이 기대 ▶ SK E&amp;S...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>성신양회(004980)</td>\n",
" <td>IBK투자증권 [우창희]</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>12200.0</td>\n",
" <td>▶ 49년 업력의 시멘트, 레미콘 제조 및 판매하는 국내 2위권 업체 ▶ 1분기는 ...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 기업명 기관명/작성자 투자의견 이전의견 목표주가 이전목표 전일수정주가 \\\n",
"0 하이트진로(000080) 신영증권 [김윤오] 중립 중립 NaN NaN 27700.0 \n",
"1 대림산업(000210) 한국투자증권 [이경자,강승균] 매수 매수 130000.0 130000.0 88600.0 \n",
"2 현대증권(003450) 이베스트투자증권 [전배승] 매수 NaN 10000.0 NaN 7170.0 \n",
"3 SK(003600) 미래에셋증권 [이진우] NaN NaN NaN NaN NaN \n",
"4 성신양회(004980) IBK투자증권 [우창희] NaN NaN NaN NaN 12200.0 \n",
"\n",
" 요약 \n",
"0 ▶ 최근 동향 점검 ▶ 오비맥주의 가격 인상 수혜 기대 ▶ 하이트진로의 모기업, 재... \n",
"1 ▶ 해외 현장 리스크 지고 있음은 분명 ▶ 주택과 유화는 이미 연간 타겟의 절반 달... \n",
"2 ▶ 투자의견 매수, 목표주가 10,000원 제시 ▶ 환골탈태 가능성에 무게를 둔다 ... \n",
"3 ▶ 1Q16의 경우, SK이노베이션 호실적에 따른 실적 선전이 기대 ▶ SK E&S... \n",
"4 ▶ 49년 업력의 시멘트, 레미콘 제조 및 판매하는 국내 2위권 업체 ▶ 1분기는 ... "
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_wisereport = pd.read_html(\"http://comp.wisereport.co.kr/wiseReport/summary/ReportSummary.aspx\", attrs={\"style\": \"table-layout:fixed\"}, encoding=\"utf8\")\n",
"df_wisereport[0].head()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/bjshin/.virtualenvs/py3/lib/python3.4/site-packages/pandas/io/data.py:35: FutureWarning: \n",
"The pandas.io.data module is moved to a separate package (pandas-datareader) and will be removed from pandas in a future version.\n",
"After installing the pandas-datareader package (https://github.com/pydata/pandas-datareader), you can change the import ``from pandas.io import data, wb`` to ``from pandas_datareader import data, wb``.\n",
" FutureWarning)\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Open</th>\n",
" <th>High</th>\n",
" <th>Low</th>\n",
" <th>Close</th>\n",
" <th>Volume</th>\n",
" <th>Adj Close</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Date</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2001-01-02</th>\n",
" <td>503.309998</td>\n",
" <td>521.340027</td>\n",
" <td>500.970001</td>\n",
" <td>520.950012</td>\n",
" <td>23101400</td>\n",
" <td>520.950012</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2001-01-03</th>\n",
" <td>512.739990</td>\n",
" <td>524.580017</td>\n",
" <td>510.700012</td>\n",
" <td>521.429993</td>\n",
" <td>32458400</td>\n",
" <td>521.429993</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2001-01-04</th>\n",
" <td>551.530029</td>\n",
" <td>567.159973</td>\n",
" <td>550.909973</td>\n",
" <td>558.020020</td>\n",
" <td>44454000</td>\n",
" <td>558.020020</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2001-01-05</th>\n",
" <td>559.539978</td>\n",
" <td>581.409973</td>\n",
" <td>555.400024</td>\n",
" <td>580.849976</td>\n",
" <td>57828600</td>\n",
" <td>580.849976</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2001-01-08</th>\n",
" <td>573.719971</td>\n",
" <td>587.909973</td>\n",
" <td>572.479980</td>\n",
" <td>586.650024</td>\n",
" <td>55864500</td>\n",
" <td>586.650024</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Open High Low Close Volume \\\n",
"Date \n",
"2001-01-02 503.309998 521.340027 500.970001 520.950012 23101400 \n",
"2001-01-03 512.739990 524.580017 510.700012 521.429993 32458400 \n",
"2001-01-04 551.530029 567.159973 550.909973 558.020020 44454000 \n",
"2001-01-05 559.539978 581.409973 555.400024 580.849976 57828600 \n",
"2001-01-08 573.719971 587.909973 572.479980 586.650024 55864500 \n",
"\n",
" Adj Close \n",
"Date \n",
"2001-01-02 520.950012 \n",
"2001-01-03 521.429993 \n",
"2001-01-04 558.020020 \n",
"2001-01-05 580.849976 \n",
"2001-01-08 586.650024 "
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from datetime import datetime\n",
"import pandas.io.data as web\n",
"df_yahoo_kospi = web.DataReader(\"^KS11\", \"yahoo\", datetime(2001, 1, 1), datetime.now())\n",
"df_yahoo_kospi.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Overview"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>dt</th>\n",
" <th>stk_cd</th>\n",
" <th>open_prc</th>\n",
" <th>high_prc</th>\n",
" <th>low_prc</th>\n",
" <th>close_prc</th>\n",
" <th>trd_amt</th>\n",
" <th>marketvalue</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>2.735658e+06</td>\n",
" <td>2.735658e+06</td>\n",
" <td>2.735658e+06</td>\n",
" <td>2.735658e+06</td>\n",
" <td>2.735658e+06</td>\n",
" <td>2.735658e+06</td>\n",
" <td>2.735658e+06</td>\n",
" <td>2.735658e+06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>2.002390e+07</td>\n",
" <td>1.225102e+04</td>\n",
" <td>3.126131e+05</td>\n",
" <td>3.184123e+05</td>\n",
" <td>3.073840e+05</td>\n",
" <td>3.123359e+05</td>\n",
" <td>6.058353e+09</td>\n",
" <td>1.105210e+12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>7.471268e+04</td>\n",
" <td>1.904395e+04</td>\n",
" <td>5.795347e+06</td>\n",
" <td>5.894102e+06</td>\n",
" <td>5.704002e+06</td>\n",
" <td>5.785683e+06</td>\n",
" <td>2.376512e+10</td>\n",
" <td>5.520591e+12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>1.990010e+07</td>\n",
" <td>1.000000e+01</td>\n",
" <td>5.000000e+00</td>\n",
" <td>5.000000e+00</td>\n",
" <td>5.000000e+00</td>\n",
" <td>5.000000e+00</td>\n",
" <td>0.000000e+00</td>\n",
" <td>5.000000e+06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>1.996062e+07</td>\n",
" <td>2.900000e+03</td>\n",
" <td>3.963505e+03</td>\n",
" <td>4.059340e+03</td>\n",
" <td>3.881960e+03</td>\n",
" <td>3.965000e+03</td>\n",
" <td>1.151895e+08</td>\n",
" <td>4.650000e+10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>2.002072e+07</td>\n",
" <td>6.200000e+03</td>\n",
" <td>1.158593e+04</td>\n",
" <td>1.181702e+04</td>\n",
" <td>1.135625e+04</td>\n",
" <td>1.159251e+04</td>\n",
" <td>5.388719e+08</td>\n",
" <td>1.237045e+11</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>2.009051e+07</td>\n",
" <td>1.233000e+04</td>\n",
" <td>3.600437e+04</td>\n",
" <td>3.675000e+04</td>\n",
" <td>3.537036e+04</td>\n",
" <td>3.602597e+04</td>\n",
" <td>2.702480e+09</td>\n",
" <td>4.257200e+11</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>2.016012e+07</td>\n",
" <td>1.928200e+05</td>\n",
" <td>3.090909e+08</td>\n",
" <td>3.181818e+08</td>\n",
" <td>3.000000e+08</td>\n",
" <td>3.068182e+08</td>\n",
" <td>2.265011e+12</td>\n",
" <td>2.321438e+14</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" dt stk_cd open_prc high_prc low_prc \\\n",
"count 2.735658e+06 2.735658e+06 2.735658e+06 2.735658e+06 2.735658e+06 \n",
"mean 2.002390e+07 1.225102e+04 3.126131e+05 3.184123e+05 3.073840e+05 \n",
"std 7.471268e+04 1.904395e+04 5.795347e+06 5.894102e+06 5.704002e+06 \n",
"min 1.990010e+07 1.000000e+01 5.000000e+00 5.000000e+00 5.000000e+00 \n",
"25% 1.996062e+07 2.900000e+03 3.963505e+03 4.059340e+03 3.881960e+03 \n",
"50% 2.002072e+07 6.200000e+03 1.158593e+04 1.181702e+04 1.135625e+04 \n",
"75% 2.009051e+07 1.233000e+04 3.600437e+04 3.675000e+04 3.537036e+04 \n",
"max 2.016012e+07 1.928200e+05 3.090909e+08 3.181818e+08 3.000000e+08 \n",
"\n",
" close_prc trd_amt marketvalue \n",
"count 2.735658e+06 2.735658e+06 2.735658e+06 \n",
"mean 3.123359e+05 6.058353e+09 1.105210e+12 \n",
"std 5.785683e+06 2.376512e+10 5.520591e+12 \n",
"min 5.000000e+00 0.000000e+00 5.000000e+06 \n",
"25% 3.965000e+03 1.151895e+08 4.650000e+10 \n",
"50% 1.159251e+04 5.388719e+08 1.237045e+11 \n",
"75% 3.602597e+04 2.702480e+09 4.257200e+11 \n",
"max 3.068182e+08 2.265011e+12 2.321438e+14 "
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"_df_kospi200_describe = df_kospi200.describe()\n",
"_df_kospi200_describe"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"sample_series = pd.Series([1,2,3], index=[\"a\", \"c\", \"b\"])"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Index(['a', 'c', 'b'], dtype='object')"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sample_series.index"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([1, 2, 3])"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sample_series.values"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Index(['dt', 'stk_cd', 'open_prc', 'high_prc', 'low_prc', 'close_prc',\n",
" 'trd_amt', 'marketvalue'],\n",
" dtype='object')"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"_df_kospi200_describe.columns # columns 도 index 입니다."
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"count 2.735658e+06\n",
"mean 1.225102e+04\n",
"std 1.904395e+04\n",
"min 1.000000e+01\n",
"25% 2.900000e+03\n",
"50% 6.200000e+03\n",
"75% 1.233000e+04\n",
"max 1.928200e+05\n",
"Name: stk_cd, dtype: float64"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_kospi200.describe()[\"stk_cd\"]"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>stk_cd</th>\n",
" <th>open_prc</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>2.735658e+06</td>\n",
" <td>2.735658e+06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>1.225102e+04</td>\n",
" <td>3.126131e+05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>1.904395e+04</td>\n",
" <td>5.795347e+06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>1.000000e+01</td>\n",
" <td>5.000000e+00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>2.900000e+03</td>\n",
" <td>3.963505e+03</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>6.200000e+03</td>\n",
" <td>1.158593e+04</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>1.233000e+04</td>\n",
" <td>3.600437e+04</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>1.928200e+05</td>\n",
" <td>3.090909e+08</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" stk_cd open_prc\n",
"count 2.735658e+06 2.735658e+06\n",
"mean 1.225102e+04 3.126131e+05\n",
"std 1.904395e+04 5.795347e+06\n",
"min 1.000000e+01 5.000000e+00\n",
"25% 2.900000e+03 3.963505e+03\n",
"50% 6.200000e+03 1.158593e+04\n",
"75% 1.233000e+04 3.600437e+04\n",
"max 1.928200e+05 3.090909e+08"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_kospi200.describe()[[\"stk_cd\", \"open_prc\"]] # [] 에 list를 넘기면 dataframe 을 return 합니다."
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"count 2.735658e+06\n",
"mean 3.126131e+05\n",
"std 5.795347e+06\n",
"min 5.000000e+00\n",
"25% 3.963505e+03\n",
"50% 1.158593e+04\n",
"75% 3.600437e+04\n",
"max 3.090909e+08\n",
"Name: open_prc, dtype: float64"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_kospi200.describe()[\"open_prc\"] # [] 에 str을 넘기면 그 str에 해당하는 열(series)을 return 합니다."
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"IT 546\n",
"경기관련소비재 358\n",
"산업재 335\n",
"소재 226\n",
"건강관리 169\n",
"필수소비재 81\n",
"금융 76\n",
"에너지 23\n",
"유틸리티 20\n",
"전기통신서비스 6\n",
"Name: WICS업종명(대), dtype: int64"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_wics[\"WICS업종명(대)\"].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"pandas.core.series.Series"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(df_wics[\"WICS업종명(대)\"].value_counts())"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"pandas.core.panel.Panel"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.Series # 1-D\n",
"pd.DataFrame # 2-D\n",
"pd.Panel # 3-D"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"IT 0.296739\n",
"경기관련소비재 0.194565\n",
"산업재 0.182065\n",
"소재 0.122826\n",
"건강관리 0.091848\n",
"필수소비재 0.044022\n",
"금융 0.041304\n",
"에너지 0.012500\n",
"유틸리티 0.010870\n",
"전기통신서비스 0.003261\n",
"Name: WICS업종명(대), dtype: float64"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_wics[\"WICS업종명(대)\"].value_counts(normalize=True) # Histogram"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
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" width:100%;\n",
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" max-width: 5em;\n",
" font-weight: 400;\n",
" text-align:right;\n",
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" color: #a94442;\n",
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" width: 0;\n",
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" background-color: #337ab7;\n",
" border-radius: 3px;\n",
" margin-right: 4px;\n",
" }\n",
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" background-color: #999;\n",
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" .missing .bar{\n",
" background-color: #a94442;\n",
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" .tooltip-inner {\n",
" width: 100%;\n",
" white-space: nowrap;\n",
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" }\n",
"\n",
" .extrapadding{\n",
" padding: 2em;\n",
" }\n",
"\n",
"\n",
"\n",
" </style>\n",
"\n",
"<div class=\"container pandas-profiling\">\n",
" <div class=\"row headerrow highlight\">\n",
" <h1>Overview</h1>\n",
" </div>\n",
"\n",
" \n",
" <div class=\"row variablerow\">\n",
" <div class=\"col-md-6 namecol\">\n",
" <p class=\"h4\">Dataset info</p>\n",
" <table class=\"stats\" style=\"margin-left: 1em;\" >\n",
" <tbody><tr><th>Number of variables</th>\n",
" <td>18</td></tr>\n",
" <tr><th>Number of observations</th>\n",
" <td>1840</td></tr>\n",
" <tr><th>Total Missing (%)</th>\n",
" <td>0.0%</td></tr>\n",
" <tr><th>Total size in memory</th>\n",
" <td>258.8 KiB</td></tr>\n",
" <tr><th>Average record size in memory</th>\n",
" <td>144.0 B</td></tr>\n",
" </tbody></table>\n",
" </div>\n",
" <div class=\"col-md-6 namecol\">\n",
" <p class=\"h4\">Variables types</p>\n",
" <table class=\"stats\" style=\"margin-left: 1em;\">\n",
" <tbody><tr><th>Numeric</th>\n",
" <td>4</td></tr>\n",
" <tr><th>Categorical</th>\n",
" <td>10</td></tr>\n",
" <tr><th>Date</th>\n",
" <td>0</td></tr>\n",
" <tr><th>Text (Unique)</th>\n",
" <td>3</td></tr>\n",
" <tr><th>Rejected</th>\n",
" <td>1</td></tr>\n",
" </tbody></table>\n",
" </div>\n",
" <div class=\"col-md-12\" style=\"padding-left: 1em;\">\n",
" <p class=\"h4\">Warnings</p>\n",
" <ul class=\"list-unstyled\"><li><code>KOSPI200 구성종목여부(1:해당, 0:미해당)</code> has 1640 / 89.1% zeros</l><li><code>WICS업종명(소)</code> has a high cardinality: 80 distinct values <span class=\"label label-warning\">Warning</span></l><li><code>WICS업종코드(소)</code> has a high cardinality: 80 distinct values <span class=\"label label-warning\">Warning</span></l><li><code>WMI500 시가총액규모별(1:대형주,2:중형주,3:소형주,0:WMI500미해당)</code> has 1341 / 72.9% zeros</l><li><code>시가총액</code> is highly skewed (γ1 = 29.414)</l><li><code>시가총액(전체)</code> is highly correlated with <code>시가총액</code> (ρ = 0.99894) <span class=\"label label-primary\">Rejected</span></l></ul>\n",
" </div>\n",
" </div>\n",
"\n",
"\n",
" <div class=\"row headerrow highlight\">\n",
" <h1>Variables</h1>\n",
" </div>\n",
"\n",
" <div class=\"row variablerow\">\n",
" <div class=\"col-md-3 namecol\">\n",
" <p class=\"h4\">Code<br/><small>Categorical, Unique</small></p>\n",
" </div>\n",
"\n",
" <div class=\"col-md-3 collapse in\" id=\"minivalues5825950221424080767\"><table border=\"1\" class=\"dataframe example_values\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>First 3 values</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>A097780</td>\n",
" </tr>\n",
" <tr>\n",
" <td>A033660</td>\n",
" </tr>\n",
" <tr>\n",
" <td>A049800</td>\n",
" </tr>\n",
" </tbody>\n",
"</table></div>\n",
" <div class=\"col-md-6 collapse in\" id=\"minivalues5825950221424080767\"><table border=\"1\" class=\"dataframe example_values\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>Last 3 values</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>A001250</td>\n",
" </tr>\n",
" <tr>\n",
" <td>A192530</td>\n",
" </tr>\n",
" <tr>\n",
" <td>A001740</td>\n",
" </tr>\n",
" </tbody>\n",
"</table></div>\n",
" <div class=\"col-md-12 text-right\">\n",
" <a role=\"button\" data-toggle=\"collapse\" data-target=\"#values5825950221424080767,#minivalues5825950221424080767\" aria-expanded=\"false\" aria-controls=\"collapseExample\">\n",
" Toggle details\n",
" </a>\n",
" </div>\n",
" <div class=\"col-md-12 collapse\" id=\"values5825950221424080767\">\n",
" <p class=\"h4\">First 20 values</p>\n",
" <table border=\"1\" class=\"dataframe sample table table-hover\">\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>A097780</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>A033660</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>A049800</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>A113810</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>A104480</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>A017510</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>A008470</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>A126700</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>A004490</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>A111770</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>A058610</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>A028050</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>A019540</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>A000030</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>A094480</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>A026960</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>A035620</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>A096350</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>A068760</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>A008560</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
" <p class=\"h4\">Last 20 values</p>\n",
" <table border=\"1\" class=\"dataframe sample table table-hover\">\n",
" <tbody>\n",
" <tr>\n",
" <th>1821</th>\n",
" <td>A145990</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1822</th>\n",
" <td>A196700</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1823</th>\n",
" <td>A101170</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1824</th>\n",
" <td>A085620</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1825</th>\n",
" <td>A090430</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1826</th>\n",
" <td>A059120</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1827</th>\n",
" <td>A086960</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1828</th>\n",
" <td>A011930</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1829</th>\n",
" <td>A003560</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1830</th>\n",
" <td>A900050</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1831</th>\n",
" <td>A001720</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1832</th>\n",
" <td>A031390</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1833</th>\n",
" <td>A029960</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1834</th>\n",
" <td>A045100</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1835</th>\n",
" <td>A011810</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1836</th>\n",
" <td>A078890</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1837</th>\n",
" <td>A025860</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1838</th>\n",
" <td>A001250</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1839</th>\n",
" <td>A192530</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1840</th>\n",
" <td>A001740</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
" </div>\n",
"\n",
" </div><div class=\"row variablerow\">\n",
" <div class=\"col-md-3 namecol\">\n",
" <p class=\"h4\">KOSPI200 구성종목여부(1:해당, 0:미해당)<br/><small>Numeric</small></p>\n",
" </div>\n",
"\n",
" <div class=\"col-md-6\">\n",
" <div class=\"row\">\n",
" <div class=\"col-sm-6\">\n",
" <table class=\"stats \">\n",
" <tr><th>Distinct count</th>\n",
" <td>2</td></tr>\n",
" <tr><th>Unique (%)</th>\n",
" <td>0.1%</td></tr>\n",
" <tr class=\"ignore\"><th>Missing (%)</th>\n",
" <td>0.0%</td></tr>\n",
" <tr class=\"ignore\"><th>Missing (n)</th>\n",
" <td>0</td></tr>\n",
" </table>\n",
"\n",
" </div>\n",
" <div class=\"col-sm-6\">\n",
" <table class=\"stats \">\n",
"\n",
" <tr><th>Mean</th>\n",
" <td>0.1087</td></tr>\n",
" <tr><th>Minimum</th>\n",
" <td>0</td></tr>\n",
" <tr><th>Maximum</th>\n",
" <td>1</td></tr>\n",
" <tr class=\"alert\"><th>Zeros (%)</th>\n",
" <td>89.1%</td></tr>\n",
" </table>\n",
" </div>\n",
" </div>\n",
" </div>\n",
" <div class=\"col-md-3 collapse in\" id=\"minihistogram5590839722509363679\">\n",
" <img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAMgAAABLCAYAAAA1fMjoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAA4NJREFUeJzt3b1LY1kYx/FfTIwv0wiThKDsMoJsY%2BEuDIIvjRBw13ogMOB/IBaKgo3YuH3ASlAQjJJtRAwWIqIibiM4WC7IDLiFuWFQhEHEhLvVhg0zPpBhb47B76cLD4d7ivPVe0NCQr7v%2B6ozz/OUy%2BWUTqeVSCTqfXk0GJfnJeQiEKBRNLneAPCcEQhgiLi6cP7so748lGta86olrN9%2B%2BVHhcDigXQHVnAWy8eff%2Buv6S01rfkq%2B0q8//xDQjoCvcYsFGAgEMBAIYCAQwEAggIFAAAOBAAYCAQwEAhgIBDAQCGAgEMBAIICBQAADgQAGAgEMBAIYCAQwEAhgIBDAQCCAgUAAA4EABgIBDAQCGAgEMBAIYCAQwEAggIFAAAOBAAYCAQwEAhgIBDAQCGAgEMDg7Ec88fL4vq9yubZfNv5XJOLmqBII6qZcLmv%2Bjw/69Pm%2BpnVvXrfp9/dvA9qVjUBQV58%2B39f8898u8QwCGAgEMBAIYHD2DPLubadu7x9rWtPR1vzd74LAvXK5rDev22pe9z1r/i8h3/f9el/U8zzlcjml02klEol6Xx4NxuV5cXKLVSwWtbS0pGKx6OLyaDAuzwvPIICBQAADgQAGAgEMTgKJx%2BOamJhQPB53cXk0GJfnxcnbvECj4BYLMBAIYCAQwEAggIFAAAOBAAYCAQyBfx9keXlZe3t7ikQi6uvr09zcXNV8a2tL6%2Bvram1tVVdXlxYXF9Xc3Bz0tvBM3d3daX5%2BXmdnZzo5OflqXu/zEuh/kIuLC%2B3s7GhjY0Obm5u6vLzU/v5%2BZV4oFJTJZLSysqJsNquWlhZls9kgt4Rnbnp6WsPDw9%2BcuTgvgQZyfHysVCqlaDSqUCik0dFRHR4eVuanp6fq7%2B9XR0eHJGlsbExHR0dBbgnPXCaT0cDAwDdnLs5LoIF4nqdYLFZ5HY/HdX19/eQ8FotVzfHytLe3PzlzcV54SAcMgQaSTCbleV7lted56uzsrJoXCoUn58B/uTgvgQYyMjKig4MDPTw8qFQqKZ/PK5VKVeZDQ0M6Pz/Xzc2NJGl7e7tqjpfpqQ%2BYuzgvgX/cfW1tTfl8XuFwWIODg5qcnNTU1JRmZ2eVTCa1u7ur1dVVRaNR9fT0aGFhQU1N3Pm9RLe3txofH1epVNLV1ZW6u7vV29urx8dHzczMODkvfB8EMPCnGjAQCGD4B0SJKVR0abCGAAAAAElFTkSuQmCC\">\n",
"\n",
" </div>\n",
" <div class=\"col-md-12 text-right\">\n",
" <a role=\"button\" data-toggle=\"collapse\" data-target=\"#descriptives5590839722509363679,#minihistogram5590839722509363679\" aria-expanded=\"false\" aria-controls=\"collapseExample\">\n",
" Toggle details\n",
" </a>\n",
" </div>\n",
" <div class=\"row collapse col-md-12\" id=\"descriptives5590839722509363679\">\n",
" <div class=\"col-sm-4\">\n",
" <p class=\"h4\">Quantile statistics</p>\n",
" <table class=\"stats indent\">\n",
" <tr><th>Minimum</th>\n",
" <td>0</td></tr>\n",
" <tr><th>5-th percentile</th>\n",
" <td>0</td></tr>\n",
" <tr><th>Q1</th>\n",
" <td>0</td></tr>\n",
" <tr><th>Median</th>\n",
" <td>0</td></tr>\n",
" <tr><th>Q3</th>\n",
" <td>0</td></tr>\n",
" <tr><th>95-th percentile</th>\n",
" <td>1</td></tr>\n",
" <tr><th>Maximum</th>\n",
" <td>1</td></tr>\n",
" <tr><th>Range</th>\n",
" <td>1</td></tr>\n",
" <tr><th>Interquartile range</th>\n",
" <td>0</td></tr>\n",
" </table>\n",
" <p class=\"h4\">Descriptive statistics</p>\n",
" <table class=\"stats indent\">\n",
" <tr><th>Standard deviation</th>\n",
" <td>0.31134</td></tr>\n",
" <tr><th>Coef of variation</th>\n",
" <td>2.8643</td></tr>\n",
" <tr><th>Kurtosis</th>\n",
" <td>4.337</td></tr>\n",
" <tr><th>Mean</th>\n",
" <td>0.1087</td></tr>\n",
" <tr><th>MAD</th>\n",
" <td>0.19376</td></tr>\n",
" <tr class=\"\"><th>Skewness</th>\n",
" <td>2.5164</td></tr>\n",
" <tr><th>Sum</th>\n",
" <td>200</td></tr>\n",
" <tr><th>Variance</th>\n",
" <td>0.096934</td></tr>\n",
" <tr><th>Memory size</th>\n",
" <td>14.5 KiB</td></tr>\n",
" </table>\n",
" </div>\n",
" <div class=\"col-sm-8 histogram\">\n",
" <img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAlgAAAGQCAYAAAByNR6YAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAIABJREFUeJzt3X1YVOed//HPDGSAKoOOBXyI26pRQUGbiFGrBh1Ta40i2hVE0u0mNmms6I9ArK3GaKJJk2JtVS5RQhvy1Eak0RhjstbIqlH2ap7awIhxE5NakwikMFHjGGGY3x/dsCHoBvTMHGd8v64rF%2BW%2BD3N/72%2BB8/HMYcbi8/l8AgAAgGGsZhcAAAAQaghYAAAABiNgAQAAGIyABQAAYDACFgAAgMEIWAAAAAYjYAEAABiMgAUAAGAwAhYAAIDBCFgAAAAGI2ABAAAYjIAFAABgMAIWAACAwQhYAAAABiNgAQAAGIyABQAAYDACFgAAgMEIWAAAAAYjYAEAABiMgAUAAGAwAhYAAIDBCFgAAAAGI2ABAAAYjIAFAABgMAIWAACAwQhYAAAABiNgAQAAGIyABQAAYDACFgAAgMEIWAAAAAYjYAEAABiMgAUAAGAwAhYAAIDBgjZg7d%2B/X2PHjlV%2Bfn67uV27diktLU3XX3%2B9Jk2apHXr1rWZLy0t1ZQpU5SSkqLs7GxVVVW1zp0/f17Lly9XamqqxowZo0WLFqmhoeGyaq2rq9OGDRtUV1d3WY%2BDS0P/zUX/zUX/zUX/zWVm/4MyYBUXF6ugoED9%2B/dvN3f06FH99Kc/VW5url5//XVt3rxZ5eXl%2Bv3vfy9J2rNnj4qKilRQUKBDhw7J6XRq/vz58ng8kqQ1a9boyJEjKisr0%2B7du2WxWLR06dLLqre%2Bvl6FhYWqr6%2B/rMfBpaH/5qL/5qL/5qL/5jKz/0EZsLp166atW7fq2muvbTd35MgRdevWTU6nU1arVdddd51GjhypmpoaSVJ5eblmzZql5ORk2Ww2zZs3T1arVRUVFfJ6vdq2bZsWLFig%2BPh4RUdHKzc3V/v27eOHAwAAdFhQBqyMjAxFRkZecG7UqFE6d%2B6cdu3apaamJh09elSvvfaaJkyYIEmqrq7WkCFD2nxNQkKCqqqqdPz4cZ0%2BfVqJiYmtc/369VNkZKRcLpff9gMAAEJLUAas/0t8fLzWrFmjZcuWadiwYZoxY4ZmzpypSZMmSZLcbrfsdnubr4mJiZHb7Zbb7ZbFYlFMTEybebvdrsbGxoDtAQAABLdwswsw2rvvvqvFixfrkUce0YQJE/T%2B%2B%2B9r4cKFiouLU3Z2docew%2BfzXfL6dXV17Z5OfPfddy/58QAAwOW50Hk4NjZWcXFxflsz5ALWs88%2Bq2HDhmny5MmSpEGDBik7O1tlZWXKzs6Ww%2BFodzXK7XZr0KBBcjgc8vl8crvdioqKap3/5JNP5HA4OrT%2Bli1bVFhY2G58wYIFGjp06GXsDJdq6NChevvtt80u46pF/81F/81F/801dOhQjRw5UosXL243l5OTo4ULF/pt7ZALWC0tLWppaWkz1tzc3Pq/k5KS5HK5lJ6e3nr84cOHlZGRob59%2ByomJkYul0u9evWS9M%2B/SmxqalJycnKH1s/MzJTT6Ww3Hhsbq1OnPPJ6Wy7wVfCnsDCr7PYo%2Bm8S%2Bm8u%2Bm8u%2Bm%2BusDCr1q5de8E/VIuNjfXr2kEZsGpra%2BXz%2BeTxeNTU1KTa2lpJ/7z/auLEiXryySe1d%2B9e3XTTTTp%2B/LjKysp0yy23SJKysrKUn5%2BvadOmafDgwSopKVFERIRSU1NltVqVkZGhoqIiJSUlKSIiQmvXrtXkyZM7fAUrLi7uopccGxs/VXMzP2Bm8Xpb6L%2BJ6L%2B56L%2B56L95/q/zsj9ZfJdzw5FJEhISZLFYWj/3%2BXyyWCytL8Wwa9cubdq0SSdOnJDD4dAtt9yinJwcXXPNNZKkZ555Rps3b1ZDQ4OSk5O1cuVKXXfddZKkpqYmPfzww9q5c6e8Xq8mTpyoFStWqGvXrpddNwHLHOHhVnXv3oX%2Bm4T%2Bm4v%2Bm4v%2Bm%2Bvz/pshKANWsOIHzBz8gjMX/TcX/TcX/TeXmQEr5F6mAQAAwGwELAAAAIMRsAAAAAxGwAIAADAYAQsAAMBgBCwAAACDEbAAAAAMRsACAAAwGAELAADAYAQsAAAAgxGwAAAADEbAAgAAMBgBCwAAwGAELAAAAIMRsAAAAAxGwAIAADAYAQsAAMBgBCwAAACDEbAAAAAMRsACAAAwGAELAADAYAQsAAAAgxGwAAAADBZudgFXi5ffOq5PPc3y%2BcyupPMir7Hqhn9xSLKYXQoAAEGBgBUgv933Nx09%2BanZZVySQT27/E/AAgAAHRG0TxHu379fY8eOVX5%2Bfru5M2fOaMmSJRoxYoRGjRql5cuX6/z5863zpaWlmjJlilJSUpSdna2qqqrWufPnz2v58uVKTU3VmDFjtGjRIjU0NARkTwAAIDQEZcAqLi5WQUGB%2Bvfvf8H5pUuXyuPxqKKiQjt27NAHH3ygl156SZK0Z88eFRUVqaCgQIcOHZLT6dT8%2BfPl8XgkSWvWrNGRI0dUVlam3bt3y2KxaOnSpQHbGwAACH5BGbC6deumrVu36tprr2039%2BGHH6qiokIrVqyQ3W5XfHy8fve73yktLU2SVF5erlmzZik5OVk2m03z5s2T1WpVRUWFvF6vtm3bpgULFig%2BPl7R0dHKzc3Vvn37VF9fH%2BhtAgCAIBWUASsjI0ORkZEXnHv99dfVu3dv7dixQ%2BPHj1dqaqrWrl0r3//cXV5dXa0hQ4a0%2BZqEhARVVVXp%2BPHjOn36tBITE1vn%2BvXrp8jISLlcLv9tCAAAhJSQu8n95MmTqq2t1UcffaTdu3frv//7v3XXXXcpLi5Ot956q9xut%2Bx2e5uviYmJkdvtltvtlsViUUxMTJt5u92uxsbGQG4DAAAEsZALWD6fT83NzfrpT3%2Bq8PBwDRs2TLNnz9YLL7ygW2%2B9tcOPcanq6upC8unE8HCLgvSCp8LCrG0%2BIrDov7nov7nov7nCwqwXPS/HxsYqLi7Ob2uHXMCKjY1VZGSkwsP/d2u9e/fWxx9/LElyOBztrka53W4NGjRIDodDPp9PbrdbUVFRrfOffPKJHI6OvUzBli1bVFhY2G58RE7JpWznihEdHdWmp8HIbo/66oPgN/TfXPTfXPTfPI8/XnLB83JOTo4WLlzot3WD%2B4x5Adddd50%2B/fRTnThxovUm%2BBMnTqh3796SpKSkJLlcLqWnp0uSWlpadPjwYWVkZKhv376KiYmRy%2BVSr169JElHjx5VU1OTkpOTO7R%2BZmamnE5nu/EH9wb3Sz2cPu1RMF/BstujdOqUR15vi9nlXHXov7nov7nov7nCwqwXPS/Hxsb6de2gDFi1tbXy%2BXzyeDxqampSbW2tJCk%2BPl7JyckaNmyYHnroIT3yyCM6ceKE/vjHP%2BpnP/uZJCkrK0v5%2BfmaNm2aBg8erJKSEkVERCg1NVVWq1UZGRkqKipSUlKSIiIitHbtWk2ePLnDV7Di4uIufMlx7wHD9m%2BG5mafpOD%2B5eD1tqi5Obj3EMzov7nov7nov3kuel72s6AMWKmpqbJY/vdtW15%2B%2BWVZLBbV1NRIktavX6/77rtPN910k7p06aIf/ehHrS/TMH78eOXl5Sk3N1cNDQ1KTk5WcXGxbDabJGnRokU6e/asZsyYIa/Xq4kTJ2rFihWB3yQAAAhaFt/l3NGNDpu74UBQv1XOujnDFazvRRgeblX37l3U2Pgp/4I0Af03F/03F/031%2Bf9N0Nw3lQDAABwBSNgAQAAGIyABQAAYDACFgAAgMEIWAAAAAYjYAEAABiMgAUAAGAwAhYAAIDBCFgAAAAGI2ABAAAYjIAFAABgMAIWAACAwQhYAAAABiNgAQAAGIyABQAAYDACFgAAgMEIWAAAAAYjYAEAABiMgAUAAGAwAhYAAIDBCFgAAAAGI2ABAAAYjIAFAABgMAIWAACAwYI2YO3fv19jx45Vfn7%2BRY/x%2BXyaNWuW/u3f/q3NeGlpqaZMmaKUlBRlZ2erqqqqde78%2BfNavny5UlNTNWbMGC1atEgNDQ1%2B2wcAAAg9QRmwiouLVVBQoP79%2B/%2Bfxz311FM6fvx4m7E9e/aoqKhIBQUFOnTokJxOp%2BbPny%2BPxyNJWrNmjY4cOaKysjLt3r1bFotFS5cu9dteAABA6AnKgNWtWzdt3bpV11577UWPqaur06ZNm9pdvSovL9esWbOUnJwsm82mefPmyWq1qqKiQl6vV9u2bdOCBQsUHx%2Bv6Oho5ebmat%2B%2Bfaqvr/f3tgAAQIgIyoCVkZGhyMjI//OYX/ziF8rOzlbfvn3bjFdXV2vIkCFtxhISElRVVaXjx4/r9OnTSkxMbJ3r16%2BfIiMj5XK5jNsAAAAIaUEZsL7KgQMH9Pbbb%2BuOO%2B5oN%2Bd2u2W329uMxcTEyO12y%2B12y2KxKCYmps283W5XY2OjX2sGAAChI9zsAox2/vx5rV69WqtWrdI111xzSY/h8/kuef26urqQfDoxPNyiYM3jYWHWNh8RWPTfXPTfXPTfXGFh1ouel2NjYxUXF%2Be3tUMuYG3cuFHXX3%2B9brzxRkntw5LD4Wh3NcrtdmvQoEFyOBzy%2BXxyu92Kiopqnf/kk0/kcDg6tP6WLVtUWFjYbnxETklnt3JFiY6OUnh4cH%2B72O1RX30Q/Ib%2Bm4v%2Bm4v%2Bm%2Bfxx0sueF7OycnRwoUL/bZucJ8xL%2BD555/XqVOnNHr0aEn/vKJ1/vx5jRkzRtu3b1dSUpJcLpfS09MlSS0tLTp8%2BLAyMjLUt29fxcTEyOVyqVevXpKko0ePqqmpScnJyR1aPzMzU06ns934g3uD%2B6UeTp/2KJivYNntUTp1yiOvt8Xscq469N9c9N9c9N9cYWHWi56XY2Nj/bp2UAas2tpa%2BXw%2BeTweNTU1qba2VpIUHx%2BvsrIyNTc3tx774osv6qWXXtL69esVGxurrKws5efna9q0aRo8eLBKSkoUERGh1NRUWa1WZWRkqKioSElJSYqIiNDatWs1efLkDl/BiouLu/Alx70HDNm7WZqbfZKC%2B5eD19ui5ubg3kMwo//mov/mov/mueh52c%2BCMmClpqbKYrG0fv7yyy/LYrGopqZGPXr0aHNsTEyMbDZba3PHjx%2BvvLw85ebmqqGhQcnJySouLpbNZpMkLVq0SGfPntWMGTPk9Xo1ceJErVixInCbAwAAQc/iu5w7utFhczcc0NGTn5pdxiUZ1LOL1s0ZLsnylcdeicLDrerevYsaGz/lX5AmoP/mov/mov/m%2Brz/ZgjOm2oAAACuYAQsAAAAgxGwAAAADEbAAgAAMBgBCwAAwGAELAAAAIMRsAAAAAxGwAIAADAYAQsAAMBgBCwAAACDEbAAAAAMRsACAAAwGAELAADAYAQsAAAAgxGwAAAADEbAAgAAMBgBCwAAwGAELAAAAIMRsAAAAAxGwAIAADAYAQsAAMBgBCwAAACDEbAAAAAMRsACAAAwGAELAADAYEEbsPbv36%2BxY8cqPz%2B/3dyf//xnzZkzRzfccIOcTqc2btzYZr60tFRTpkxRSkqKsrOzVVVV1Tp3/vx5LV%2B%2BXKmpqRozZowWLVqkhoYGv%2B8HAACEjqAMWMXFxSooKFD//v3bzdXW1uquu%2B7SzJkz9dprr6mwsFCPPfaYnn/%2BeUnSnj17VFRUpIKCAh06dEhOp1Pz58%2BXx%2BORJK1Zs0ZHjhxRWVmZdu/eLYvFoqVLlwZ0fwAAILgFZcDq1q2btm7dqmuvvbbdXH19vWbPnq3MzExZrVYNGTJE3/72t/Xqq69KksrLyzVr1iwlJyfLZrNp3rx5slqtqqiokNfr1bZt27RgwQLFx8crOjpaubm52rdvn%2Brr6wO9TQAAEKSCMmBlZGQoMjLygnNJSUn6%2Bc9/3mbsww8/VM%2BePSVJ1dXVGjJkSJv5hIQEVVVV6fjx4zp9%2BrQSExNb5/r166fIyEi5XC6DdwEAAEJVUAasznjyySd14sQJzZkzR5Lkdrtlt9vbHBMTEyO32y232y2LxaKYmJg283a7XY2NjQGrGQAABLdwswvwp6eeekobNmxQcXGxHA5Hh7/O5/Nd8pp1dXUh%2BXRieLhFwZrHw8KsbT4isOi/uei/uei/ucLCrBc9L8fGxiouLs5va4dswPr1r3%2Btbdu26YknnlBCQkLruMPhaHc1yu12a9CgQXI4HPL5fHK73YqKimqd/%2BSTTzoc0LZs2aLCwsJ24yNySi5xJ1eG6OgohYcH97eL3R711QfBb%2Bi/uei/uei/eR5/vOSC5%2BWcnBwtXLjQb%2BsG9xnzIh577DHt2rVLZWVlrfdefS4pKUkul0vp6emSpJaWFh0%2BfFgZGRnq27evYmJi5HK51KtXL0nS0aNH1dTUpOTk5A6tnZmZKafT2W78wb3B/VIPp097FMxXsOz2KJ065ZHX22J2OVcd%2Bm8u%2Bm8u%2Bm%2BusDDrRc/LsbGxfl07KANWbW2tfD6fPB6PmpqaVFtbK0mKj4/X3//%2Bd23YsOGC4UqSsrKylJ%2Bfr2nTpmnw4MEqKSlRRESEUlNTZbValZGRoaKiIiUlJSkiIkJr167V5MmTO3wFKy4u7sKXHPceuKw9m6252ScpuH85eL0tam4O7j0EM/pvLvpvLvpvnouel/0sKANWamqqLBZL6%2Bcvv/yyLBaLampq9Pzzz%2BvcuXP6/ve/3zrv8/nUp08fvfjiixo/frzy8vKUm5urhoYGJScnq7i4WDabTZK0aNEinT17VjNmzJDX69XEiRO1YsWKgO8RAAAEL4vvcu7oRofN3XBAR09%2BanYZl2RQzy5aN2e4JMtXHnslCg%2B3qnv3Lmps/JR/QZqA/puL/puL/pvr8/6bIThvqgEAALiCEbAAAAAMRsACAAAwGAELAADAYAQsAAAAgxGwAAAADEbAAgAAMBgBCwAAwGAELAAAAIMRsAAAAAxGwAIAADAYAQsAAMBgBCwAAACDEbAAAAAMFrCAdfr06UAtBQAAYKqABaxx48ZpyZIleu211wK1JAAAgCkCFrDuv/9%2BNTQ06N///d/1ve99T4899pgaGhoCtTwAAEDABCxgpaen69FHH9X%2B/ft166236k9/%2BpMmTJigu%2B%2B%2BW5WVlYEqAwAAwO8CfpO7w%2BFQdna2fv/736ugoECHDh3S7bffrqlTp2r37t2BLgcAAMBw4YFesKGhQdu2bdOzzz6r999/X%2BPHj1dmZqY%2B%2BOAD3Xvvvfrggw902223BbosAAAAwwQsYO3fv1/l5eWqqKhQTEyM/vVf/1WZmZnq1atX6zHXXXedFi9eTMACAABBLWAB68c//rFGjx6tNWvW6Oabb1ZYWFi7Y0aMGKGoqKhAlQQAAOAXAQtYL730kr7xjW/o/PnzreHqzJkz6tq1a%2Bsx11xzDfdhAQCAoBewm9ytVqumTp2qvXv3to6VlZVp6tSp%2Bvvf/x6oMgAAAPwuYAHrwQcf1ODBgzVixIjWsbS0NA0bNky/%2BMUvOv14%2B/fv19ixY5Wfn99u7uDBg5o9e7ZGjBih6dOna/v27W3mS0tLNWXKFKWkpCg7O1tVVVWtc%2BfPn9fy5cuVmpqqMWPGaNGiRbxeFwAA6JSABaw333xTDz30kGJjY1vHvv71r%2Bu%2B%2B%2B7T66%2B/3qnHKi4uVkFBgfr3799urq6uTjk5OZo7d64qKyu1bNkyrVy5UtXV1ZKkPXv2qKioqPUlIpxOp%2BbPny%2BPxyNJWrNmjY4cOaKysjLt3r1bFotFS5cuvYydAwCAq03AApbP59P58%2BfbjZ85c0Zer7dTj9WtWzdt3bpV1157bbu5nTt3ql%2B/fpo5c6ZsNptGjx6tSZMmqby8XJJUXl6uWbNmKTk5WTabTfPmzZPValVFRYW8Xq%2B2bdumBQsWKD4%2BXtHR0crNzdW%2BfftUX19/aRsHAABXnYAFrPHjx2vJkiU6cuSIzpw5o1OnTumNN95QXl6eJkyY0KnHysjIUGRk5AXnXC6Xhg4d2mYsMTGx9WnA6upqDRkypM18QkKCqqqqdPz4cZ0%2BfVqJiYmtc/369VNkZKRcLlenagQAAFevgP0V4dKlS7VgwQKlp6fLYrG0jqekpGjFihWGreN2u9WzZ882YzExMWpsbGydt9vt7ebdbrfcbrcsFotiYmLazNvt9tavBwAA%2BCoBC1g9evTQM888oyNHjuhvf/ubwsLC9M1vflPXXXed4Wv5fD7Tvr6uri4kn04MD7fIhHdWMkRYmLXNRwQW/TcX/TcX/TdXWJj1oufl2NhYxcXF%2BW3tgL9VTkJCghISEvz2%2BN27d5fb7W4z5na71aNHD0n/fC/EL1%2BNcrvdGjRokBwOh3w%2Bn9xud5sXPP3kk0/kcDg6tP6WLVtUWFjYbnxETklnt3JFiY6OUnh4wL9dDGW38yK2ZqL/5qL/5qL/5nn88ZILnpdzcnK0cOFCv60bsDNmVVWVVq9eraNHj%2BrcuXPt5mtqagxZJykpSdu2bWu39vDhw1vnXS6X0tPTJUktLS06fPiwMjIy1LdvX8XExMjlcrW%2Bhc/Ro0fV1NSk5OTkDq2fmZkpp9PZbvzBvcH9Ug%2BnT3sUzFew7PYonTrlkdfbYnY5Vx36by76by76b66wMOtFz8tffFUDfwhYwFq%2BfLm%2B9rWvaeHChfra1752WY9VW1srn88nj8ejpqYm1dbWSpLi4%2BOVlpamwsJClZeXKy0tTZWVlTpw4IDKysokSVlZWcrPz9e0adM0ePBglZSUKCIiQqmpqbJarcrIyFBRUZGSkpIUERGhtWvXavLkyR2%2BghUXF3fhS457D1zWns3W3OyTFNy/HLzeFjU3B/ceghn9Nxf9Nxf9N89Fz8t%2BFrCA9f777%2BuVV15p89Y4lyo1NbXNjfIvv/yyLBaLampq5HA4tGnTJq1evVoPPPCA%2BvTpo4KCAg0cOFDSP/%2BaMS8vT7m5uWpoaFBycrKKi4tls9kkSYsWLdLZs2c1Y8YMeb1eTZw40dCb8AEAQOiz%2BC73jvAOmjp1qsrKygwJWMFo7oYDOnryU7PLuCSDenbRujnDJVm%2B8tgrUXi4Vd27d1Fj46f8C9IE9N9c9N9c9N9cn/ffDAG7qebuu%2B/WQw89pDNnzgRqSQAAAFME7CnCoqIinThxQtu3b1e3bt1ktbbNdq%2B88kqgSgEAAPCrgAWsC93BDwAAEIoCFrBycnICtRQAAICpAvrCRgcPHtTixYv1gx/8QNI/X4Nq165dgSwBAADA7wIWsF544QXdeeedOn36tP7yl79Ikk6ePKkVK1Zo69atgSoDAADA7wIWsDZv3qw1a9Zo06ZNra9h1bt3b61fv16/%2B93vAlUGAACA3wUsYB0/flyTJ0%2BWpDYvEjpq1CidOHEiUGUAAAD4XcACVvfu3dXQ0P79%2BN577z116WLOi4ABAAD4Q8AC1ujRo7Vs2TK98847kiS3261XXnlFubm5mjhxYqDKAAAA8LuABawlS5bI4/Fo2rRp%2BuyzzzRmzBj96Ec/Up8%2BffSzn/0sUGUAAAD4XcBeB6tbt2568skndeTIER07dkyRkZHq16%2Bf%2BvXrF6gSAAAAAiJgAetzCQkJSkhICPSyAAAAAROwgJWQkNDmrwe/rKamJlClAAAA%2BFXAAtaKFSvaBCyv16v33ntP%2B/fv1/z58wNVBgAAgN8FLGBlZWVdcPzPf/6ztmzZopkzZwaqFAAAAL8K6HsRXsjIkSO1b98%2Bs8sAAAAwjOkBa%2B/evQoPD/i99gAAAH4TsGQzbty4dmPnzp3Tp59%2BetGnDwEAAIJRwAJWZmZmu78ijIiI0IABA%2BR0OgNVBgAAgN8FLGAtXLgwUEsBAACYKmABq7CwsEPHWSwWLViwwM/VAAAA%2BE/AAtb27dtVX1%2Bvzz77TDExMWppadHp06cVGRmprl27tjmWgAUAAIJZwAJWXl6eKioq9LOf/Uw9evSQJJ08eVIPP/ywvvOd7%2BiWW24JVCkAAAB%2BFbCXaVi3bp3uvffe1nAlST179tR9992nX//614EqAwAAwO8CFrA%2B%2BugjhYWFtRu/5ppr9I9//MPQtWpqavTDH/5QI0eO1Lhx47R48WI1NjZKkg4ePKjZs2drxIgRmj59urZv397ma0tLSzVlyhSlpKQoOztbVVVVhtYGAABCX8AC1sCBA7VkyRK5XC6dOnVKZ86c0ZEjR7R06VINHjzYsHW8Xq/uvPNODR8%2BXAcPHtSOHTv08ccf6/7771ddXZ1ycnI0d%2B5cVVZWatmyZVq5cqWqq6slSXv27FFRUZEKCgp06NAhOZ1OzZ8/Xx6Px7D6AABA6AtYwFq1apWOHTum73//%2Bxo1apRGjhyp9PR0uVwurVy50rB1Pv74Y9XX1ys9PV02m00Oh0Pf/e53VVNTo507d6pfv36aOXOmbDabRo8erUmTJqm8vFySVF5erlmzZik5OVk2m03z5s2T1WpVRUWFYfUBAIDQF7Cb3IcMGaIXX3xR1dXV%2Buijj%2BTz%2BdSzZ08lJSXJajUu58XHx2vo0KHasmWL/t//%2B3/yeDz6j//4D02YMEEul0tDhw5tc3xiYqJefPFFSVJ1dXW7m%2B0TEhJUVVWlqVOnGlYjAAAIbQF/L0K73a6uXbtq8uTJGjZsmKHh6nO/%2Bc1vtHfvXo0YMULjxo2Tz%2BdTfn6%2B3G637HZ7m2NjYmJa78%2B62Lzb7Ta8RgAAELoCdgWroaFBP/nJT/SXv/xF4eHhqq6uVn19vW677TY9%2Buij6tWrlyHrnD9/XvPnz9f3vvc9/fjHP9bZs2d1//3365577pEk%2BXw%2BQ9a5mLq6OtXX1/t1DTOEh1t0Bbw3%2BCUJC7O2%2BYjAov/mov/mov/mCguzXvS8HBsbq7i4OL%2BtHbCA9fDDDysyMlJbt25Vdna2JCk6OlpDhgzRI488ot/85jeGrFNZWakPPvhAeXl5kqQuXbooJydH6enpuummm9pdjXK73a0vHeFwOFqvZn1xftCgQR1ef8uWLRd81foROSWd3coVJTo6SuHhAft28Qu7PcrsEq5q9N9c9N9c9N88jz9ecsHzck5Ojl/fxi9gZ8z9%2B/frueeeU3x8fOubPkdGRurnP/%2B5vvOd7xi2TktLS%2Bt/nz%2BWjkW1AAAY2ElEQVT92NzcLIvFom9/%2B9t69tln2xxfVVWl4cOHS5KSkpLkcrmUnp7e%2BliHDx/W7NmzO7x%2BZmbmBd%2B8%2BsG9DZe6pSvC6dMeBfMVLLs9SqdOeeT1tphdzlWH/puL/puL/psrLMx60fNybGysX9cOWMBqamq64KW4yMhINTU1GbbO9ddfr6997Wtav3697rrrLnk8Hm3evFkpKSlKS0tTYWGhysvLlZaWpsrKSh04cEBlZWWSpKysLOXn52vatGkaPHiwSkpKFBERoQkTJnR4/bi4uAtfctx7wKAdmqO52ScpuH85eL0tam4O7j0EM/pvLvpvLvpvnouel/0sYJckBgwYoN27d7cb37Jli/r372/YOt26ddNvf/tbvfHGG0pNTdX06dNls9m0du1aORwObdq0SU899ZRSUlL08MMPq6CgQAMHDpQkjR8/Xnl5ecrNzdWoUaP0X//1XyouLpbNZjOsPgAAEPoCdgXr9ttv1%2BLFi7Vr1y55vV6tWrVKLpdLb731lmH3X31uyJAheuKJJy44l5KS0u7V279ozpw5mjNnjqH1AACAq0vArmBNmTJFmzdvltfr1b/8y7/ozTffVJ8%2BffTMM89o8uTJgSoDAADA7wJyBaulpUXV1dUaM2aMxowZE4glAQAATBOQK1hWq1U//OEP5fV6A7EcAACAqQL2FOGMGTNUWlrq9xf6BAAAMFvAbnI/efKk9uzZo0cffVS9e/du95d5zzzzTKBKAQAA8KuABazu3btr/PjxgVoOAADANH4PWIsWLdL69ev1i1/8onVs/fr1WrRokb%2BXBgAAMIXf78Hat29fu7Hf/va3/l4WAADANKa8uRw3ugMAgFBmSsD6/M2eAQAAQpEpAQsAACCUEbAAAAAM5ve/ImxqalJ%2Bfv5Xjv3qV7/ydykAAAAB4feANWLECNXV1X3lGAAAQKjwe8B68skn/b0EAADAFYV7sAAAAAxGwAIAADAYAQsAAMBgBCwAAACDEbAAAAAMRsACAAAwGAELAADAYAQsAAAAgxGwAAAADBayAauoqEjjxo3T9ddfr9tvv10nTpyQJB08eFCzZ8/WiBEjNH36dG3fvr3N15WWlmrKlClKSUlRdna2qqqqzCgfAAAEsZAMWE8//bR27Nihp556SgcOHFD//v1VWlqquro65eTkaO7cuaqsrNSyZcu0cuVKVVdXS5L27NmjoqIiFRQU6NChQ3I6nZo/f748Ho/JOwIAAMEkJAPWY489pry8PH3zm99U165dde%2B99%2Bree%2B/Vzp071a9fP82cOVM2m02jR4/WpEmTVF5eLkkqLy/XrFmzlJycLJvNpnnz5slqtaqiosLkHQEAgGAScgGrtrZWJ06c0KlTp3TLLbdo1KhRys3NVWNjo1wul4YOHdrm%2BMTExNanAaurqzVkyJA28wkJCTxNCAAAOiUkA5YkvfTSS3r88ce1Y8cOnTx5Uvfdd5/cbrfsdnub42NiYtTY2ChJF513u92BKR4AAISEcLMLMJrP55Mk3XHHHfr6178uSVq4cKHuuOMOjRw5snXeX%2Brq6lRfX%2B/XNcwQHm5RsObxsDBrm48ILPpvLvpvLvpvrrAw60XPy7GxsYqLi/Pb2iEXsD4PVdHR0a1jvXv3VktLiyS1uxrldrvVo0cPSZLD4Wi9mvXF%2BUGDBnV4/S1btqiwsLDd%2BIickg4/xpUoOjpK4eHB/e1it0eZXcJVjf6bi/6bi/6b5/HHSy54Xs7JydHChQv9tm5wnzEvoGfPnoqOjlZNTY0SExMlSSdOnNA111yj1NTUdi/LUFVVpeHDh0uSkpKS5HK5lJ6eLklqaWnR4cOHNXv27A6vn5mZKafT2W78wb0Nl7qlK8Lp0x4F8xUsuz1Kp0555PW2mF3OVYf%2Bm4v%2Bm4v%2BmysszHrR83JsbKxf1w65gBUWFqaMjAxt2rRJKSkp6tKlizZu3KgZM2YoPT1dGzduVHl5udLS0lRZWakDBw6orKxMkpSVlaX8/HxNmzZNgwcPVklJiSIiIjRhwoQOrx8XF3fhS457Dxi0Q3M0N/skBfcvB6%2B3Rc3Nwb2HYEb/zUX/zUX/zXPR87KfhVzAkqTc3FydO3dOs2fPVnNzs7773e9q2bJlioqK0qZNm7R69Wo98MAD6tOnjwoKCjRw4EBJ0vjx45WXl6fc3Fw1NDQoOTlZxcXFstlsJu8IAAAEE4vP33d9Q5I0d8MBHT35qdllXJJBPbto3Zzhkixml3JJwsOt6t69ixobP%2BVfkCag/%2Bai/%2Bai/%2Bb6vP9mCM6bagAAAK5gBCwAAACDEbAAAAAMRsACAAAwGAELAADAYAQsAAAAgxGwAAAADEbAAgAAMBgBCwAAwGAELAAAAIMRsAAAAAxGwAIAADAYAQsAAMBgBCwAAACDEbAAAAAMRsACAAAwGAELAADAYAQsAAAAgxGwAAAADEbAAgAAMBgBCwAAwGAELAAAAIMRsAAAAAxGwAIAADAYAQsAAMBgIR%2BwHnroISUkJLR%2BfvDgQc2ePVsjRozQ9OnTtX379jbHl5aWasqUKUpJSVF2draqqqoCXTIAAAhyIR2wampq9Nxzz8lisUiSamtrlZOTo7lz56qyslLLli3TypUrVV1dLUnas2ePioqKVFBQoEOHDsnpdGr%2B/PnyeDxmbgMAAASZkA1YPp9PK1eu1O233946tnPnTvXr108zZ86UzWbT6NGjNWnSJJWXl0uSysvLNWvWLCUnJ8tms2nevHmyWq2qqKgwaxsAACAIhWzA%2BsMf/qCoqChNmzatdezw4cMaOnRom%2BMSExNbnwasrq7WkCFD2swnJCTwNCEAAOiUcLML8IePP/5YGzdu1NNPP91m3O12q2fPnm3GYmJi1NjY2Dpvt9vbzbvdbv8WDAAAQkpIBqyHH35Yc%2BbM0Te%2B8Q198MEHbeZ8Pp9f166rq1N9fb1f1zBDeLhFwXrBMyzM2uYjAov%2Bm4v%2Bm4v%2BmysszHrR83JsbKzi4uL8tnbIBazKykpVV1froYcektQ2UHXv3r3d1Si3260ePXpIkhwOR%2BvVrC/ODxo0qMPrb9myRYWFhe3GR%2BSUdPgxrkTR0VEKDw/ubxe7PcrsEq5q9N9c9N9c9N88jz9ecsHzck5OjhYuXOi3dYP7jHkBO3bsUG1trW666SZJ/wxYPp9PY8aM0W233aadO3e2Ob6qqkrDhw%2BXJCUlJcnlcik9PV2S1NLSosOHD2v27NkdXj8zM1NOp7Pd%2BIN7Gy51S1eE06c9CuYrWHZ7lE6d8sjrbTG7nKsO/TcX/TcX/TdXWJj1oufl2NhYv64dcgFr6dKlys3Nbf385MmTyszM1HPPPSev16vi4mKVl5crLS1NlZWVOnDggMrKyiRJWVlZys/P17Rp0zR48GCVlJQoIiJCEyZM6PD6cXFxF77kuPfA5W7NVM3NPknB/cvB621Rc3Nw7yGY0X9z0X9z0X/zXPS87GchF7Cio6MVHR3d%2Bnlzc7MsFktrczdt2qTVq1frgQceUJ8%2BfVRQUKCBAwdKksaPH6%2B8vDzl5uaqoaFBycnJKi4uls1mM2UvAAAgOIVcwPqyPn36qKampvXzlJSUdq/e/kVz5szRnDlzAlEaAAAIUcF5Uw0AAMAVjIAFAABgMAIWAACAwQhYAAAABiNgAQAAGIyABQAAYDACFgAAgMEIWAAAAAYjYAEAABiMgAUAAGAwAhYAAIDBCFgAAAAGI2ABAAAYjIAFAABgMAIWAACAwQhYAAAABiNgAQAAGIyABQAAYDACFgAAgMEIWAAAAAYjYAEAABiMgAUAAGAwAhYAAIDBCFgAAAAGC8mA9eGHHyonJ0ejRo3St7/9bS1ZskRnzpyRJB08eFCzZ8/WiBEjNH36dG3fvr3N15aWlmrKlClKSUlRdna2qqqqzNgCAAAIYiEZsH7yk5/Ibrdr3759ev7553Xs2DE98sgjqqurU05OjubOnavKykotW7ZMK1euVHV1tSRpz549KioqUkFBgQ4dOiSn06n58%2BfL4/GYvCMAABBMQi5gnTlzRkOHDtU999yjyMhI9ejRQ%2Bnp6Xr11Ve1c%2BdO9evXTzNnzpTNZtPo0aM1adIklZeXS5LKy8s1a9YsJScny2azad68ebJaraqoqDB5VwAAIJiEXMDq2rWrHnzwQTkcjtaxDz/8UPHx8XK5XBo6dGib4xMTE1ufBqyurtaQIUPazCckJPA0IQAA6JSQC1hfVlVVpd///ve666675Ha7Zbfb28zHxMSosbFRki4673a7A1YvAAAIfuFmF%2BBPr7/%2Bun7yk5/onnvu0ZgxY1RSUiKfz%2BfXNevq6lRfX%2B/XNcwQHm5RsObxsDBrm48ILPpvLvpvruDvv%2B9//gtOVuvFz8uxsbGKi4vz29ohG7D27t2rn/70p7rvvvuUlpYmSerevXu7q1Fut1s9evSQJDkcjtarWV%2BcHzRoUIfX3bJliwoLC9uNj8gp6ewWrijR0VEKDw/ubxe7PcrsEq5q9N9c9N9cwdr/5uZm3Vf2F73/j%2BD8Y69v9ohSr39UXvC8nJOTo4ULF/pt7eA%2BY17EG2%2B8oZ///OfasGGDxowZ0zqelJSkbdu2tTm2qqpKw4cPb513uVxKT0%2BXJLW0tOjw4cOaPXt2h9fOzMyU0%2BlsN/7g3oZL2coV4/Rpj4L5CpbdHqVTpzzyelvMLueqQ//NRf/NFfz9b9H7//Do6MlPzS7kkuVe5LwcGxvr13VDLmB5vV4tX7689WnBL0pLS1NhYaHKy8uVlpamyspKHThwQGVlZZKkrKws5efna9q0aRo8eLBKSkoUERGhCRMmdHj9uLi4C19y3HvgcrZluuZmn6Rg/OXwv7zeFjU3B/ceghn9Nxf9N1fw9j94nx783EXPy34WcgHrzTff1LFjx7R69WqtWrVKFotFPp9PFotFL730kjZt2qTVq1frgQceUJ8%2BfVRQUKCBAwdKksaPH6%2B8vDzl5uaqoaFBycnJKi4uls1mM3lXAAAgmIRcwEpJSVFNTc1F53v16tXu1du/aM6cOZozZ44/SgMAAFeJ4LypBgAA4ApGwAIAADAYAQsAAMBgBCwAAACDEbAAAAAMRsACAAAwGAELAADAYAQsAAAAgxGwAAAADEbAAgAAMBgBCwAAwGAELAAAAIMRsAAAAAxGwAIAADAYAQsAAMBgBCwAAACDEbAAAAAMRsACAAAwGAELAADAYAQsAAAAgxGwAAAADEbAAgAAMBgBCwAAwGAELAAAAIMRsAAAAAxGwPqSEydO6M4779SoUaPkdDr1y1/%2BUj6fz%2ByyAABAECFgfcmiRYvUq1cv7d27V48//rj27t2r0tJSs8sCAABBhID1BVVVVTp69KgWL16sLl26qG/fvrrtttu0detWs0sDAABBhID1BYcPH1afPn3UtWvX1rHExES99957Onv2rImVAQCAYELA%2BgK32y273d5mrFu3bpKkxsZGM0oCAABBKNzsAq40l3tDe11dnerr69uNf7NH1GU9rpm%2B2SNK4UH8nWK1Ss3NzbJaFdT7CFb031z031yh0P9gP39d7LwcGxuruLg4v60dpP93%2B4fD4ZDb7W4z5na7ZbFY5HA4OvQYW7ZsUWFhYbvxkSNH6jdr1/r1/0xcWF1dnZ544nfKzMyk/yag/%2Bai/%2BYKhf4/NDfF7BIuWV1dnfLy8vTqq6%2B2m8vJydHChQv9tjYB6wuSkpL00Ucfye12tz41%2BNZbb2nAgAGKiupYgs/MzJTT6Wwz9u6772rx4sWqr68P2h%2BwYFZfX6/CwkI5nU76bwL6by76by76b676%2Bnq9%2BuqrKigo0IABA9rMxcbG%2BnVtAtYXJCYmKjk5Wb/61a%2B0ZMkS1dbWqrS0VPPmzevwY8TFxfFDBADAFWTAgAEaOnRoQNfkJvcvWbdunWprazVu3Dj98Ic/1MyZM5WVlWV2WQAAIIhwBetL4uPjVVxcbHYZAAAgiHEFCwAAwGBhK1euXGl2EVeDLl266MYbb1SXLl3MLuWqRP/NRf/NRf/NRf/NZVb/LT7eyRgAAMBQPEUIAABgMAIWAACAwQhYAAAABiNgAQAAGIyABQAAYDACFgAAgMEIWAAAAAYjYAEAABiMgGWQEydO6M4779SoUaPkdDr1y1/%2BUhd7DdfS0lJNmTJFKSkpys7OVlVVVYCrDT2d6f8f/vAHTZkyRTfccIPS0tK0Z8%2BeAFcbejrT/8/V1tbqhhtuUGFhYYCqDF2d6f%2BxY8f0gx/8QN/61rc0ceJElZaWBrbYENTR/vt8Pq1fv15Op1M33HCDZsyYoV27dplQcWjZv3%2B/xo4dq/z8/K88NqDnXx8MMXPmTN99993nO3PmjO/48eO%2B7373u77f/e537Y7705/%2B5Lvxxht9b731lu%2Bzzz7zlZSU%2BMaOHes7e/asCVWHjs70PyUlxffmm2/6vF6vb9u2bb6kpCTf3//%2BdxOqDh0d7f8X5eTk%2BFJSUnwbNmwIUJWhq6P9P3funG/ixIm%2B4uJi32effeb761//6ps2bZrv2LFjJlQdOjra/6eeeso3fvx43zvvvOPzer2%2BP/3pT76hQ4f63n77bROqDg2bN2/2TZs2zXfrrbf68vLy/s9jA33%2B5QqWAaqqqnT06FEtXrxYXbp0Ud%2B%2BfXXbbbdp69at7Y4tLy/XrFmzlJycLJvNpnnz5slqtaqiosKEykNDZ/rv8XiUl5enb33rW7JarUpPT1fXrl3117/%2B1YTKQ0Nn%2Bv%2B5ffv26b333tPEiRMDWGlo6kz/X3zxRXXt2lV33HGHbDabhg0bpueff179%2BvUzofLQ0Jn%2B19TUKCUlRQMGDJDVatXNN9%2Bsbt266e233zah8tDQrVs3bd26Vddee%2B1XHhvo8y8BywCHDx9Wnz591LVr19axxMREvffeezp79mybY6urqzVkyJA2YwkJCTxNeBk60//p06crKyur9fNTp07pzJkzio%2BPD1i9oaYz/Zekzz77TKtWrdLKlSsVFhYWyFJDUmf6//rrr2vQoEFaunSpRo4cqalTp%2BqFF14IdMkhpTP9nzBhgv785z%2BrpqZGTU1N2rNnj86dO6cbb7wx0GWHjIyMDEVGRnbo2ECffwlYBnC73bLb7W3GunXrJklqbGz8ymNjYmLkdrv9W2QI60z/v%2Bzee%2B/Vt771LaWkpPitvlDX2f4XFhbqxhtvpOcG6Uz/T548qZdfflnjxo3TK6%2B8oh//%2BMdavHixjhw5ErB6Q01n%2Bn/zzTcrMzNTM2fO1LBhw7R48WI9/PDD/AMvQAJ9/g33y6NehXxfcUMv/Kuz/W9ubtaSJUt07NgxPfHEE36q6urR0f6/88472r59u3bu3Onniq4uHe2/z%2BdTUlKSpk6dKkmaMWOG/vCHP%2BjFF19UQkKCP0sMaR3t//bt27Vt2zb98Y9/1MCBA1VZWal77rlHvXv3bndlBcGPK1gGcDgc7RKw2%2B2WxWKRw%2BFod%2ByFrmp9%2BTh0XGf6L/3zKao777xTJ0%2Be1NNPP03vL1Nn%2Bn///ffr7rvvVkxMTCBLDGmd6X9sbKyio6PbjPXp00cff/yx3%2BsMVZ3p/9NPP605c%2BZo6NChstlsSk1N1ahRo7Rt27ZAlnzVCvT5l4BlgKSkJH300UdtfsjeeustDRgwQFFRUe2OdblcrZ%2B3tLTo8OHDGj58eMDqDTWd6b8k3X333bLZbCotLeVEb4CO9v/DDz/Ua6%2B9poKCAo0ePVqjR4/WCy%2B8oJKSEs2aNcuM0kNCZ77/r7vuunY3VH/wwQfq3bt3QGoNRZ3pv9frldfrbTPW3NwckDoR%2BPMvAcsAiYmJSk5O1q9%2B9SudOXNG7777rkpLSzV37lxJ0pQpU/TGG29IkrKysvTcc8/pr3/9q86dO6eNGzcqIiJCEyZMMHEHwa0z/d%2BxY4feeecdrVu3Ttdcc42ZZYeMjva/V69e%2Bs///E9t375dzz33nJ577jk5nU5lZWXp0UcfNXkXwasz3/8zZszQJ598os2bN%2Buzzz7Tzp075XK5lJaWZuYWglpn%2Bu90OrV161a9/fbb8nq9OnjwoCorK3XzzTebuYWgVltbq5MnT8rj8ejcuXOqra1VbW1t6/z3vvc9086/3INlkHXr1mn58uUaN26cunbtqqysrNa/Vvvb3/7W%2Btck48ePV15ennJzc9XQ0KDk5GQVFxfLZrOZWX7Q%2B6r%2BezweSdKzzz6rDz/8sPWvdnw%2BnywWi2bMmKEHHnjAtPqDXUe%2B/y0WS7ubeaOiotSlSxf16NHDjLJDRkd//3z961/X5s2btWrVKm3cuFG9evVSUVGR%2Bvbta2b5Qa%2Bj/b/rrrvU0tKiBQsWqKGhQX369NGqVas0atQoM8sPaqmpqbJYLK2fv/zyy7JYLKqpqZEkvf/%2B%2B6adfy0%2B7s4GAAAwFE8RAgAAGIyABQAAYDACFgAAgMEIWAAAAAYjYAEAABiMgAUAAGAwAhYAAIDBCFgAAAAGI2ABAAAYjIAFAABgMAIWAACAwQhYAAAABiNgAQAAGOz/AxBwfjSH1j7qAAAAAElFTkSuQmCC\">\n",
" </div>\n",
" </div>\n",
" </div><div class=\"row variablerow\">\n",
" <div class=\"col-md-3 namecol\">\n",
" <p class=\"h4\">Name<br/><small>Categorical, Unique</small></p>\n",
" </div>\n",
"\n",
" <div class=\"col-md-3 collapse in\" id=\"minivalues-8965338523238783170\"><table border=\"1\" class=\"dataframe example_values\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>First 3 values</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>SIMPAC</td>\n",
" </tr>\n",
" <tr>\n",
" <td>제로투세븐</td>\n",
" </tr>\n",
" <tr>\n",
" <td>에스티오</td>\n",
" </tr>\n",
" </tbody>\n",
"</table></div>\n",
" <div class=\"col-md-6 collapse in\" id=\"minivalues-8965338523238783170\"><table border=\"1\" class=\"dataframe example_values\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>Last 3 values</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>일진다이아</td>\n",
" </tr>\n",
" <tr>\n",
" <td>LG생활건강</td>\n",
" </tr>\n",
" <tr>\n",
" <td>동방아그로</td>\n",
" </tr>\n",
" </tbody>\n",
"</table></div>\n",
" <div class=\"col-md-12 text-right\">\n",
" <a role=\"button\" data-toggle=\"collapse\" data-target=\"#values-8965338523238783170,#minivalues-8965338523238783170\" aria-expanded=\"false\" aria-controls=\"collapseExample\">\n",
" Toggle details\n",
" </a>\n",
" </div>\n",
" <div class=\"col-md-12 collapse\" id=\"values-8965338523238783170\">\n",
" <p class=\"h4\">First 20 values</p>\n",
" <table border=\"1\" class=\"dataframe sample table table-hover\">\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>SIMPAC</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>제로투세븐</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>에스티오</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>매커스</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>인포바인</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>디엔에프</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>스카이라이프</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>가비아</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>경방</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>광전자</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>씨에스윈드</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>아세아시멘트</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>JYP Ent.</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>액토즈소프트</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>와이솔</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>씨케이에이치</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>코스맥스비티아이</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>코프라</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>무림페이퍼</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>크리스탈신소재</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
" <p class=\"h4\">Last 20 values</p>\n",
" <table border=\"1\" class=\"dataframe sample table table-hover\">\n",
" <tbody>\n",
" <tr>\n",
" <th>1821</th>\n",
" <td>무학</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1822</th>\n",
" <td>아바코</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1823</th>\n",
" <td>피엘에이</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1824</th>\n",
" <td>YW</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1825</th>\n",
" <td>서화정보통신</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1826</th>\n",
" <td>대웅</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1827</th>\n",
" <td>HMC투자증권</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1828</th>\n",
" <td>테고사이언스</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1829</th>\n",
" <td>덕산네오룩스</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1830</th>\n",
" <td>청호컴넷</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1831</th>\n",
" <td>쎄니트</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1832</th>\n",
" <td>동성제약</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1833</th>\n",
" <td>월덱스</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1834</th>\n",
" <td>알루코</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1835</th>\n",
" <td>SK케미칼</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1836</th>\n",
" <td>화일약품</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1837</th>\n",
" <td>삼화콘덴서</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1838</th>\n",
" <td>일진다이아</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1839</th>\n",
" <td>LG생활건강</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1840</th>\n",
" <td>동방아그로</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
" </div>\n",
"\n",
" </div><div class=\"row variablerow\">\n",
" <div class=\"col-md-3 namecol\">\n",
" <p class=\"h4\">WI26업종명(대)<br/><small>Categorical</small></p>\n",
" </div>\n",
"\n",
" <div class=\"col-md-3\">\n",
"\n",
" <table class=\"stats \">\n",
" <tr class=\"\"><th>Distinct count</th>\n",
" <td>26</td></tr>\n",
" <tr><th>Unique (%)</th>\n",
" <td>1.4%</td></tr>\n",
" <tr class=\"ignore\"><th>Missing (%)</th>\n",
" <td>0.0%</td></tr>\n",
" <tr class=\"ignore\"><th>Missing (n)</th>\n",
" <td>0</td></tr>\n",
" </table>\n",
"\n",
"\n",
"\n",
" </div>\n",
" \n",
" <div class=\"col-md-6 collapse in\" id=\"minifreqtable8643816330725510381\">\n",
" <table class=\"mini freq\">\n",
" <tr class=\"\">\n",
" <th>IT하드웨어</th>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:15%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\" data-delay=500 title=\"Percentage: 10.9%\">\n",
"&nbsp;\n",
" </div>200\n",
" </td>\n",
" </tr>\n",
"<tr class=\"\">\n",
" <th>건강관리</th>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:13%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\" data-delay=500 title=\"Percentage: 9.2%\">\n",
"&nbsp;\n",
" </div>169\n",
" </td>\n",
" </tr>\n",
"<tr class=\"\">\n",
" <th>자동차</th>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:10%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\" data-delay=500 title=\"Percentage: 7.3%\">\n",
"&nbsp;\n",
" </div>134\n",
" </td>\n",
" </tr>\n",
"<tr class=\"other\">\n",
" <th>Other values (23)</th>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:100%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\" data-delay=500 title=\"Percentage: 72.7%\">\n",
"1337\n",
" </div>\n",
" </td>\n",
" </tr>\n",
"\n",
" </table>\n",
" </div>\n",
"\n",
"\n",
" <div class=\"col-md-12 text-right\">\n",
" <a role=\"button\" data-toggle=\"collapse\" data-target=\"#freqtable8643816330725510381, #minifreqtable8643816330725510381\" aria-expanded=\"true\" aria-controls=\"collapseExample\">\n",
" Toggle details\n",
" </a>\n",
" </div>\n",
" \n",
"\n",
" <div class=\"col-md-12 collapse extrapadding\" id=\"freqtable8643816330725510381\">\n",
" <table class=\"freq table table-hover\">\n",
" <thead><tr>\n",
" <td class=\"fillremaining\">Value</td>\n",
" <td class=\"number\">Count</td>\n",
" <td class=\"number\">Frequency (%)</td>\n",
" <td style=\"min-width:200px\">&nbsp;</td>\n",
" </tr></thead>\n",
"\n",
" \n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">IT하드웨어</td>\n",
" <td class=\"number\">200</td>\n",
" <td class=\"number\">10.9%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">건강관리</td>\n",
" <td class=\"number\">169</td>\n",
" <td class=\"number\">9.2%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:84%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">자동차</td>\n",
" <td class=\"number\">134</td>\n",
" <td class=\"number\">7.3%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:67%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">소프트웨어</td>\n",
" <td class=\"number\">123</td>\n",
" <td class=\"number\">6.7%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:61%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">건설</td>\n",
" <td class=\"number\">117</td>\n",
" <td class=\"number\">6.4%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:58%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">기계</td>\n",
" <td class=\"number\">116</td>\n",
" <td class=\"number\">6.3%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:58%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">화장품,의류</td>\n",
" <td class=\"number\">107</td>\n",
" <td class=\"number\">5.8%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:53%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">화학</td>\n",
" <td class=\"number\">100</td>\n",
" <td class=\"number\">5.4%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:50%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">반도체</td>\n",
" <td class=\"number\">94</td>\n",
" <td class=\"number\">5.1%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:47%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">디스플레이</td>\n",
" <td class=\"number\">84</td>\n",
" <td class=\"number\">4.6%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:42%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">필수소비재</td>\n",
" <td class=\"number\">81</td>\n",
" <td class=\"number\">4.4%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:41%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">비철금속</td>\n",
" <td class=\"number\">79</td>\n",
" <td class=\"number\">4.3%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:40%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">미디어,교육</td>\n",
" <td class=\"number\">70</td>\n",
" <td class=\"number\">3.8%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:35%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">철강</td>\n",
" <td class=\"number\">47</td>\n",
" <td class=\"number\">2.6%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:24%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">IT가전</td>\n",
" <td class=\"number\">45</td>\n",
" <td class=\"number\">2.4%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:23%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">상사,자본재</td>\n",
" <td class=\"number\">42</td>\n",
" <td class=\"number\">2.3%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:21%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">은행</td>\n",
" <td class=\"number\">39</td>\n",
" <td class=\"number\">2.1%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:20%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">소매(유통)</td>\n",
" <td class=\"number\">33</td>\n",
" <td class=\"number\">1.8%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:17%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">운송</td>\n",
" <td class=\"number\">33</td>\n",
" <td class=\"number\">1.8%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:17%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">조선</td>\n",
" <td class=\"number\">27</td>\n",
" <td class=\"number\">1.5%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:14%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"other\">\n",
" <td class=\"fillremaining\">Other values (6)</td>\n",
" <td class=\"number\">100</td>\n",
" <td class=\"number\">5.4%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:50%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
" </table>\n",
"\n",
" </div>\n",
"\n",
"\n",
" </div><div class=\"row variablerow\">\n",
" <div class=\"col-md-3 namecol\">\n",
" <p class=\"h4\">WI26업종명(중)<br/><small>Categorical</small></p>\n",
" </div>\n",
"\n",
" <div class=\"col-md-3\">\n",
"\n",
" <table class=\"stats \">\n",
" <tr class=\"\"><th>Distinct count</th>\n",
" <td>48</td></tr>\n",
" <tr><th>Unique (%)</th>\n",
" <td>2.6%</td></tr>\n",
" <tr class=\"ignore\"><th>Missing (%)</th>\n",
" <td>0.0%</td></tr>\n",
" <tr class=\"ignore\"><th>Missing (n)</th>\n",
" <td>0</td></tr>\n",
" </table>\n",
"\n",
"\n",
"\n",
" </div>\n",
" \n",
" <div class=\"col-md-6 collapse in\" id=\"minifreqtable7549355893811414888\">\n",
" <table class=\"mini freq\">\n",
" <tr class=\"\">\n",
" <th>자동차부품</th>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:9%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\" data-delay=500 title=\"Percentage: 7.0%\">\n",
"&nbsp;\n",
" </div>128\n",
" </td>\n",
" </tr>\n",
"<tr class=\"\">\n",
" <th>건설,건축제품,건축자재</th>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:8%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\" data-delay=500 title=\"Percentage: 5.8%\">\n",
"&nbsp;\n",
" </div>107\n",
" </td>\n",
" </tr>\n",
"<tr class=\"\">\n",
" <th>화학</th>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:7%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\" data-delay=500 title=\"Percentage: 5.4%\">\n",
"&nbsp;\n",
" </div>100\n",
" </td>\n",
" </tr>\n",
"<tr class=\"other\">\n",
" <th>Other values (45)</th>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:100%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\" data-delay=500 title=\"Percentage: 81.8%\">\n",
"1505\n",
" </div>\n",
" </td>\n",
" </tr>\n",
"\n",
" </table>\n",
" </div>\n",
"\n",
"\n",
" <div class=\"col-md-12 text-right\">\n",
" <a role=\"button\" data-toggle=\"collapse\" data-target=\"#freqtable7549355893811414888, #minifreqtable7549355893811414888\" aria-expanded=\"true\" aria-controls=\"collapseExample\">\n",
" Toggle details\n",
" </a>\n",
" </div>\n",
" \n",
"\n",
" <div class=\"col-md-12 collapse extrapadding\" id=\"freqtable7549355893811414888\">\n",
" <table class=\"freq table table-hover\">\n",
" <thead><tr>\n",
" <td class=\"fillremaining\">Value</td>\n",
" <td class=\"number\">Count</td>\n",
" <td class=\"number\">Frequency (%)</td>\n",
" <td style=\"min-width:200px\">&nbsp;</td>\n",
" </tr></thead>\n",
"\n",
" \n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">자동차부품</td>\n",
" <td class=\"number\">128</td>\n",
" <td class=\"number\">7.0%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:30%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">건설,건축제품,건축자재</td>\n",
" <td class=\"number\">107</td>\n",
" <td class=\"number\">5.8%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:25%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">화학</td>\n",
" <td class=\"number\">100</td>\n",
" <td class=\"number\">5.4%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:23%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">소프트웨어</td>\n",
" <td class=\"number\">95</td>\n",
" <td class=\"number\">5.2%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:22%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">반도체</td>\n",
" <td class=\"number\">94</td>\n",
" <td class=\"number\">5.1%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:22%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">기계</td>\n",
" <td class=\"number\">94</td>\n",
" <td class=\"number\">5.1%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:22%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">제약</td>\n",
" <td class=\"number\">92</td>\n",
" <td class=\"number\">5.0%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:21%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">디스플레이장비</td>\n",
" <td class=\"number\">82</td>\n",
" <td class=\"number\">4.5%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:19%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">전자장비,사무용전자제품</td>\n",
" <td class=\"number\">75</td>\n",
" <td class=\"number\">4.1%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:18%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">식품,음료</td>\n",
" <td class=\"number\">73</td>\n",
" <td class=\"number\">4.0%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:17%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">핸드셋</td>\n",
" <td class=\"number\">67</td>\n",
" <td class=\"number\">3.6%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:16%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">의류</td>\n",
" <td class=\"number\">64</td>\n",
" <td class=\"number\">3.5%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:15%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">미디어</td>\n",
" <td class=\"number\">55</td>\n",
" <td class=\"number\">3.0%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:13%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">철강</td>\n",
" <td class=\"number\">47</td>\n",
" <td class=\"number\">2.6%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:11%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">생명과학</td>\n",
" <td class=\"number\">44</td>\n",
" <td class=\"number\">2.4%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:11%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">통신장비</td>\n",
" <td class=\"number\">44</td>\n",
" <td class=\"number\">2.4%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:11%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">포장재,종이와목재</td>\n",
" <td class=\"number\">42</td>\n",
" <td class=\"number\">2.3%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:10%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">비철금속</td>\n",
" <td class=\"number\">37</td>\n",
" <td class=\"number\">2.0%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:9%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">건강관리장비,서비스</td>\n",
" <td class=\"number\">33</td>\n",
" <td class=\"number\">1.8%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:8%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">운송</td>\n",
" <td class=\"number\">33</td>\n",
" <td class=\"number\">1.8%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:8%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"other\">\n",
" <td class=\"fillremaining\">Other values (28)</td>\n",
" <td class=\"number\">434</td>\n",
" <td class=\"number\">23.6%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
" </table>\n",
"\n",
" </div>\n",
"\n",
"\n",
" </div><div class=\"row variablerow\">\n",
" <div class=\"col-md-3 namecol\">\n",
" <p class=\"h4\">WI26업종코드(대)<br/><small>Categorical</small></p>\n",
" </div>\n",
"\n",
" <div class=\"col-md-3\">\n",
"\n",
" <table class=\"stats \">\n",
" <tr class=\"\"><th>Distinct count</th>\n",
" <td>26</td></tr>\n",
" <tr><th>Unique (%)</th>\n",
" <td>1.4%</td></tr>\n",
" <tr class=\"ignore\"><th>Missing (%)</th>\n",
" <td>0.0%</td></tr>\n",
" <tr class=\"ignore\"><th>Missing (n)</th>\n",
" <td>0</td></tr>\n",
" </table>\n",
"\n",
"\n",
"\n",
" </div>\n",
" \n",
" <div class=\"col-md-6 collapse in\" id=\"minifreqtable-7327970454173299153\">\n",
" <table class=\"mini freq\">\n",
" <tr class=\"\">\n",
" <th>WI610</th>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:15%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\" data-delay=500 title=\"Percentage: 10.9%\">\n",
"&nbsp;\n",
" </div>200\n",
" </td>\n",
" </tr>\n",
"<tr class=\"\">\n",
" <th>WI410</th>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:13%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\" data-delay=500 title=\"Percentage: 9.2%\">\n",
"&nbsp;\n",
" </div>169\n",
" </td>\n",
" </tr>\n",
"<tr class=\"\">\n",
" <th>WI300</th>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:10%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\" data-delay=500 title=\"Percentage: 7.3%\">\n",
"&nbsp;\n",
" </div>134\n",
" </td>\n",
" </tr>\n",
"<tr class=\"other\">\n",
" <th>Other values (23)</th>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:100%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\" data-delay=500 title=\"Percentage: 72.7%\">\n",
"1337\n",
" </div>\n",
" </td>\n",
" </tr>\n",
"\n",
" </table>\n",
" </div>\n",
"\n",
"\n",
" <div class=\"col-md-12 text-right\">\n",
" <a role=\"button\" data-toggle=\"collapse\" data-target=\"#freqtable-7327970454173299153, #minifreqtable-7327970454173299153\" aria-expanded=\"true\" aria-controls=\"collapseExample\">\n",
" Toggle details\n",
" </a>\n",
" </div>\n",
" \n",
"\n",
" <div class=\"col-md-12 collapse extrapadding\" id=\"freqtable-7327970454173299153\">\n",
" <table class=\"freq table table-hover\">\n",
" <thead><tr>\n",
" <td class=\"fillremaining\">Value</td>\n",
" <td class=\"number\">Count</td>\n",
" <td class=\"number\">Frequency (%)</td>\n",
" <td style=\"min-width:200px\">&nbsp;</td>\n",
" </tr></thead>\n",
"\n",
" \n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">WI610</td>\n",
" <td class=\"number\">200</td>\n",
" <td class=\"number\">10.9%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">WI410</td>\n",
" <td class=\"number\">169</td>\n",
" <td class=\"number\">9.2%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:84%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">WI300</td>\n",
" <td class=\"number\">134</td>\n",
" <td class=\"number\">7.3%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:67%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">WI600</td>\n",
" <td class=\"number\">123</td>\n",
" <td class=\"number\">6.7%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:61%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">WI220</td>\n",
" <td class=\"number\">117</td>\n",
" <td class=\"number\">6.4%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:58%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">WI230</td>\n",
" <td class=\"number\">116</td>\n",
" <td class=\"number\">6.3%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:58%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">WI310</td>\n",
" <td class=\"number\">107</td>\n",
" <td class=\"number\">5.8%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:53%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">WI110</td>\n",
" <td class=\"number\">100</td>\n",
" <td class=\"number\">5.4%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:50%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">WI620</td>\n",
" <td class=\"number\">94</td>\n",
" <td class=\"number\">5.1%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:47%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">WI640</td>\n",
" <td class=\"number\">84</td>\n",
" <td class=\"number\">4.6%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:42%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">WI400</td>\n",
" <td class=\"number\">81</td>\n",
" <td class=\"number\">4.4%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:41%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">WI200</td>\n",
" <td class=\"number\">79</td>\n",
" <td class=\"number\">4.3%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:40%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">WI330</td>\n",
" <td class=\"number\">70</td>\n",
" <td class=\"number\">3.8%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:35%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">WI210</td>\n",
" <td class=\"number\">47</td>\n",
" <td class=\"number\">2.6%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:24%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">WI630</td>\n",
" <td class=\"number\">45</td>\n",
" <td class=\"number\">2.4%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:23%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">WI250</td>\n",
" <td class=\"number\">42</td>\n",
" <td class=\"number\">2.3%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:21%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">WI500</td>\n",
" <td class=\"number\">39</td>\n",
" <td class=\"number\">2.1%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:20%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">WI260</td>\n",
" <td class=\"number\">33</td>\n",
" <td class=\"number\">1.8%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:17%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">WI340</td>\n",
" <td class=\"number\">33</td>\n",
" <td class=\"number\">1.8%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:17%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">WI240</td>\n",
" <td class=\"number\">27</td>\n",
" <td class=\"number\">1.5%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:14%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"other\">\n",
" <td class=\"fillremaining\">Other values (6)</td>\n",
" <td class=\"number\">100</td>\n",
" <td class=\"number\">5.4%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:50%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
" </table>\n",
"\n",
" </div>\n",
"\n",
"\n",
" </div><div class=\"row variablerow\">\n",
" <div class=\"col-md-3 namecol\">\n",
" <p class=\"h4\">WI26업종코드(중)<br/><small>Categorical</small></p>\n",
" </div>\n",
"\n",
" <div class=\"col-md-3\">\n",
"\n",
" <table class=\"stats \">\n",
" <tr class=\"\"><th>Distinct count</th>\n",
" <td>48</td></tr>\n",
" <tr><th>Unique (%)</th>\n",
" <td>2.6%</td></tr>\n",
" <tr class=\"ignore\"><th>Missing (%)</th>\n",
" <td>0.0%</td></tr>\n",
" <tr class=\"ignore\"><th>Missing (n)</th>\n",
" <td>0</td></tr>\n",
" </table>\n",
"\n",
"\n",
"\n",
" </div>\n",
" \n",
" <div class=\"col-md-6 collapse in\" id=\"minifreqtable6844030006187629340\">\n",
" <table class=\"mini freq\">\n",
" <tr class=\"\">\n",
" <th>WI30020</th>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:9%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\" data-delay=500 title=\"Percentage: 7.0%\">\n",
"&nbsp;\n",
" </div>128\n",
" </td>\n",
" </tr>\n",
"<tr class=\"\">\n",
" <th>WI22010</th>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:8%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\" data-delay=500 title=\"Percentage: 5.8%\">\n",
"&nbsp;\n",
" </div>107\n",
" </td>\n",
" </tr>\n",
"<tr class=\"\">\n",
" <th>WI11010</th>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:7%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\" data-delay=500 title=\"Percentage: 5.4%\">\n",
"&nbsp;\n",
" </div>100\n",
" </td>\n",
" </tr>\n",
"<tr class=\"other\">\n",
" <th>Other values (45)</th>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:100%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\" data-delay=500 title=\"Percentage: 81.8%\">\n",
"1505\n",
" </div>\n",
" </td>\n",
" </tr>\n",
"\n",
" </table>\n",
" </div>\n",
"\n",
"\n",
" <div class=\"col-md-12 text-right\">\n",
" <a role=\"button\" data-toggle=\"collapse\" data-target=\"#freqtable6844030006187629340, #minifreqtable6844030006187629340\" aria-expanded=\"true\" aria-controls=\"collapseExample\">\n",
" Toggle details\n",
" </a>\n",
" </div>\n",
" \n",
"\n",
" <div class=\"col-md-12 collapse extrapadding\" id=\"freqtable6844030006187629340\">\n",
" <table class=\"freq table table-hover\">\n",
" <thead><tr>\n",
" <td class=\"fillremaining\">Value</td>\n",
" <td class=\"number\">Count</td>\n",
" <td class=\"number\">Frequency (%)</td>\n",
" <td style=\"min-width:200px\">&nbsp;</td>\n",
" </tr></thead>\n",
"\n",
" \n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">WI30020</td>\n",
" <td class=\"number\">128</td>\n",
" <td class=\"number\">7.0%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:30%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">WI22010</td>\n",
" <td class=\"number\">107</td>\n",
" <td class=\"number\">5.8%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:25%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">WI11010</td>\n",
" <td class=\"number\">100</td>\n",
" <td class=\"number\">5.4%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:23%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">WI60010</td>\n",
" <td class=\"number\">95</td>\n",
" <td class=\"number\">5.2%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:22%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">WI23010</td>\n",
" <td class=\"number\">94</td>\n",
" <td class=\"number\">5.1%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:22%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">WI62010</td>\n",
" <td class=\"number\">94</td>\n",
" <td class=\"number\">5.1%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:22%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">WI41010</td>\n",
" <td class=\"number\">92</td>\n",
" <td class=\"number\">5.0%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:21%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">WI64020</td>\n",
" <td class=\"number\">82</td>\n",
" <td class=\"number\">4.5%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:19%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">WI61040</td>\n",
" <td class=\"number\">75</td>\n",
" <td class=\"number\">4.1%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:18%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">WI40010</td>\n",
" <td class=\"number\">73</td>\n",
" <td class=\"number\">4.0%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:17%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">WI61020</td>\n",
" <td class=\"number\">67</td>\n",
" <td class=\"number\">3.6%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:16%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">WI31020</td>\n",
" <td class=\"number\">64</td>\n",
" <td class=\"number\">3.5%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:15%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">WI33010</td>\n",
" <td class=\"number\">55</td>\n",
" <td class=\"number\">3.0%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:13%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">WI21010</td>\n",
" <td class=\"number\">47</td>\n",
" <td class=\"number\">2.6%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:11%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">WI41020</td>\n",
" <td class=\"number\">44</td>\n",
" <td class=\"number\">2.4%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:11%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">WI61010</td>\n",
" <td class=\"number\">44</td>\n",
" <td class=\"number\">2.4%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:11%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">WI20010</td>\n",
" <td class=\"number\">42</td>\n",
" <td class=\"number\">2.3%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:10%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">WI20020</td>\n",
" <td class=\"number\">37</td>\n",
" <td class=\"number\">2.0%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:9%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">WI26010</td>\n",
" <td class=\"number\">33</td>\n",
" <td class=\"number\">1.8%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:8%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">WI41030</td>\n",
" <td class=\"number\">33</td>\n",
" <td class=\"number\">1.8%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:8%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"other\">\n",
" <td class=\"fillremaining\">Other values (28)</td>\n",
" <td class=\"number\">434</td>\n",
" <td class=\"number\">23.6%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
" </table>\n",
"\n",
" </div>\n",
"\n",
"\n",
" </div><div class=\"row variablerow\">\n",
" <div class=\"col-md-3 namecol\">\n",
" <p class=\"h4\">WICS업종명(대)<br/><small>Categorical</small></p>\n",
" </div>\n",
"\n",
" <div class=\"col-md-3\">\n",
"\n",
" <table class=\"stats \">\n",
" <tr class=\"\"><th>Distinct count</th>\n",
" <td>10</td></tr>\n",
" <tr><th>Unique (%)</th>\n",
" <td>0.5%</td></tr>\n",
" <tr class=\"ignore\"><th>Missing (%)</th>\n",
" <td>0.0%</td></tr>\n",
" <tr class=\"ignore\"><th>Missing (n)</th>\n",
" <td>0</td></tr>\n",
" </table>\n",
"\n",
"\n",
"\n",
" </div>\n",
" \n",
" <div class=\"col-md-6 collapse in\" id=\"minifreqtable9211283895050768164\">\n",
" <table class=\"mini freq\">\n",
" <tr class=\"\">\n",
" <th>IT</th>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:90%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\" data-delay=500 title=\"Percentage: 29.7%\">\n",
"546\n",
" </div>\n",
" </td>\n",
" </tr>\n",
"<tr class=\"\">\n",
" <th>경기관련소비재</th>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:59%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\" data-delay=500 title=\"Percentage: 19.5%\">\n",
"358\n",
" </div>\n",
" </td>\n",
" </tr>\n",
"<tr class=\"\">\n",
" <th>산업재</th>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:56%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\" data-delay=500 title=\"Percentage: 18.2%\">\n",
"335\n",
" </div>\n",
" </td>\n",
" </tr>\n",
"<tr class=\"other\">\n",
" <th>Other values (7)</th>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:100%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\" data-delay=500 title=\"Percentage: 32.7%\">\n",
"601\n",
" </div>\n",
" </td>\n",
" </tr>\n",
"\n",
" </table>\n",
" </div>\n",
"\n",
"\n",
" <div class=\"col-md-12 text-right\">\n",
" <a role=\"button\" data-toggle=\"collapse\" data-target=\"#freqtable9211283895050768164, #minifreqtable9211283895050768164\" aria-expanded=\"true\" aria-controls=\"collapseExample\">\n",
" Toggle details\n",
" </a>\n",
" </div>\n",
" \n",
"\n",
" <div class=\"col-md-12 collapse extrapadding\" id=\"freqtable9211283895050768164\">\n",
" <table class=\"freq table table-hover\">\n",
" <thead><tr>\n",
" <td class=\"fillremaining\">Value</td>\n",
" <td class=\"number\">Count</td>\n",
" <td class=\"number\">Frequency (%)</td>\n",
" <td style=\"min-width:200px\">&nbsp;</td>\n",
" </tr></thead>\n",
"\n",
" \n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">IT</td>\n",
" <td class=\"number\">546</td>\n",
" <td class=\"number\">29.7%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">경기관련소비재</td>\n",
" <td class=\"number\">358</td>\n",
" <td class=\"number\">19.5%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:65%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">산업재</td>\n",
" <td class=\"number\">335</td>\n",
" <td class=\"number\">18.2%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:61%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">소재</td>\n",
" <td class=\"number\">226</td>\n",
" <td class=\"number\">12.3%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:41%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">건강관리</td>\n",
" <td class=\"number\">169</td>\n",
" <td class=\"number\">9.2%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:31%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">필수소비재</td>\n",
" <td class=\"number\">81</td>\n",
" <td class=\"number\">4.4%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:15%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">금융</td>\n",
" <td class=\"number\">76</td>\n",
" <td class=\"number\">4.1%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:14%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">에너지</td>\n",
" <td class=\"number\">23</td>\n",
" <td class=\"number\">1.2%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:5%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">유틸리티</td>\n",
" <td class=\"number\">20</td>\n",
" <td class=\"number\">1.1%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:4%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">전기통신서비스</td>\n",
" <td class=\"number\">6</td>\n",
" <td class=\"number\">0.3%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:2%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
" </table>\n",
"\n",
" </div>\n",
"\n",
"\n",
" </div><div class=\"row variablerow\">\n",
" <div class=\"col-md-3 namecol\">\n",
" <p class=\"h4\">WICS업종명(소)<br/><small>Categorical</small></p>\n",
" </div>\n",
"\n",
" <div class=\"col-md-3\">\n",
"\n",
" <table class=\"stats \">\n",
" <tr class=\"alert\"><th>Distinct count</th>\n",
" <td>80</td></tr>\n",
" <tr><th>Unique (%)</th>\n",
" <td>4.3%</td></tr>\n",
" <tr class=\"ignore\"><th>Missing (%)</th>\n",
" <td>0.0%</td></tr>\n",
" <tr class=\"ignore\"><th>Missing (n)</th>\n",
" <td>0</td></tr>\n",
" </table>\n",
"\n",
"\n",
"\n",
" </div>\n",
" \n",
" <div class=\"col-md-6 collapse in\" id=\"minifreqtable-8300871828479933971\">\n",
" <table class=\"mini freq\">\n",
" <tr class=\"\">\n",
" <th>자동차부품</th>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:9%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\" data-delay=500 title=\"Percentage: 7.0%\">\n",
"&nbsp;\n",
" </div>128\n",
" </td>\n",
" </tr>\n",
"<tr class=\"\">\n",
" <th>화학</th>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:7%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\" data-delay=500 title=\"Percentage: 5.4%\">\n",
"&nbsp;\n",
" </div>100\n",
" </td>\n",
" </tr>\n",
"<tr class=\"\">\n",
" <th>기계</th>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:7%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\" data-delay=500 title=\"Percentage: 5.1%\">\n",
"&nbsp;\n",
" </div>94\n",
" </td>\n",
" </tr>\n",
"<tr class=\"other\">\n",
" <th>Other values (77)</th>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:100%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\" data-delay=500 title=\"Percentage: 82.5%\">\n",
"1518\n",
" </div>\n",
" </td>\n",
" </tr>\n",
"\n",
" </table>\n",
" </div>\n",
"\n",
"\n",
" <div class=\"col-md-12 text-right\">\n",
" <a role=\"button\" data-toggle=\"collapse\" data-target=\"#freqtable-8300871828479933971, #minifreqtable-8300871828479933971\" aria-expanded=\"true\" aria-controls=\"collapseExample\">\n",
" Toggle details\n",
" </a>\n",
" </div>\n",
" \n",
"\n",
" <div class=\"col-md-12 collapse extrapadding\" id=\"freqtable-8300871828479933971\">\n",
" <table class=\"freq table table-hover\">\n",
" <thead><tr>\n",
" <td class=\"fillremaining\">Value</td>\n",
" <td class=\"number\">Count</td>\n",
" <td class=\"number\">Frequency (%)</td>\n",
" <td style=\"min-width:200px\">&nbsp;</td>\n",
" </tr></thead>\n",
"\n",
" \n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">자동차부품</td>\n",
" <td class=\"number\">128</td>\n",
" <td class=\"number\">7.0%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:22%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">화학</td>\n",
" <td class=\"number\">100</td>\n",
" <td class=\"number\">5.4%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:17%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">기계</td>\n",
" <td class=\"number\">94</td>\n",
" <td class=\"number\">5.1%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:16%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">반도체와반도체장비</td>\n",
" <td class=\"number\">94</td>\n",
" <td class=\"number\">5.1%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:16%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">제약</td>\n",
" <td class=\"number\">92</td>\n",
" <td class=\"number\">5.0%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:16%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">디스플레이장비및부품</td>\n",
" <td class=\"number\">82</td>\n",
" <td class=\"number\">4.5%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:14%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">전자장비와기기</td>\n",
" <td class=\"number\">72</td>\n",
" <td class=\"number\">3.9%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:13%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">핸드셋</td>\n",
" <td class=\"number\">67</td>\n",
" <td class=\"number\">3.6%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:12%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">섬유,의류,신발,호화품</td>\n",
" <td class=\"number\">64</td>\n",
" <td class=\"number\">3.5%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:11%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">식품</td>\n",
" <td class=\"number\">58</td>\n",
" <td class=\"number\">3.2%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:10%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">건설</td>\n",
" <td class=\"number\">58</td>\n",
" <td class=\"number\">3.2%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:10%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">철강</td>\n",
" <td class=\"number\">47</td>\n",
" <td class=\"number\">2.6%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:8%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">방송과엔터테인먼트</td>\n",
" <td class=\"number\">45</td>\n",
" <td class=\"number\">2.4%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:8%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">IT서비스</td>\n",
" <td class=\"number\">44</td>\n",
" <td class=\"number\">2.4%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:8%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">통신장비</td>\n",
" <td class=\"number\">44</td>\n",
" <td class=\"number\">2.4%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:8%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">비철금속</td>\n",
" <td class=\"number\">37</td>\n",
" <td class=\"number\">2.0%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:7%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">소프트웨어</td>\n",
" <td class=\"number\">34</td>\n",
" <td class=\"number\">1.8%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:6%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">건축자재</td>\n",
" <td class=\"number\">31</td>\n",
" <td class=\"number\">1.7%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:6%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">게임소프트웨어와서비스</td>\n",
" <td class=\"number\">28</td>\n",
" <td class=\"number\">1.5%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:5%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">건강관리장비와용품</td>\n",
" <td class=\"number\">28</td>\n",
" <td class=\"number\">1.5%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:5%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"other\">\n",
" <td class=\"fillremaining\">Other values (60)</td>\n",
" <td class=\"number\">593</td>\n",
" <td class=\"number\">32.2%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
" </table>\n",
"\n",
" </div>\n",
"\n",
"\n",
" </div><div class=\"row variablerow\">\n",
" <div class=\"col-md-3 namecol\">\n",
" <p class=\"h4\">WICS업종명(중)<br/><small>Categorical</small></p>\n",
" </div>\n",
"\n",
" <div class=\"col-md-3\">\n",
"\n",
" <table class=\"stats \">\n",
" <tr class=\"\"><th>Distinct count</th>\n",
" <td>29</td></tr>\n",
" <tr><th>Unique (%)</th>\n",
" <td>1.6%</td></tr>\n",
" <tr class=\"ignore\"><th>Missing (%)</th>\n",
" <td>0.0%</td></tr>\n",
" <tr class=\"ignore\"><th>Missing (n)</th>\n",
" <td>0</td></tr>\n",
" </table>\n",
"\n",
"\n",
"\n",
" </div>\n",
" \n",
" <div class=\"col-md-6 collapse in\" id=\"minifreqtable3317665116575178146\">\n",
" <table class=\"mini freq\">\n",
" <tr class=\"\">\n",
" <th>자본재</th>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:25%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\" data-delay=500 title=\"Percentage: 15.4%\">\n",
"283\n",
" </div>\n",
" </td>\n",
" </tr>\n",
"<tr class=\"\">\n",
" <th>소재</th>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:20%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\" data-delay=500 title=\"Percentage: 12.3%\">\n",
"&nbsp;\n",
" </div>226\n",
" </td>\n",
" </tr>\n",
"<tr class=\"\">\n",
" <th>기술하드웨어와장비</th>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:18%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\" data-delay=500 title=\"Percentage: 10.9%\">\n",
"&nbsp;\n",
" </div>200\n",
" </td>\n",
" </tr>\n",
"<tr class=\"other\">\n",
" <th>Other values (26)</th>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:100%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\" data-delay=500 title=\"Percentage: 61.5%\">\n",
"1131\n",
" </div>\n",
" </td>\n",
" </tr>\n",
"\n",
" </table>\n",
" </div>\n",
"\n",
"\n",
" <div class=\"col-md-12 text-right\">\n",
" <a role=\"button\" data-toggle=\"collapse\" data-target=\"#freqtable3317665116575178146, #minifreqtable3317665116575178146\" aria-expanded=\"true\" aria-controls=\"collapseExample\">\n",
" Toggle details\n",
" </a>\n",
" </div>\n",
" \n",
"\n",
" <div class=\"col-md-12 collapse extrapadding\" id=\"freqtable3317665116575178146\">\n",
" <table class=\"freq table table-hover\">\n",
" <thead><tr>\n",
" <td class=\"fillremaining\">Value</td>\n",
" <td class=\"number\">Count</td>\n",
" <td class=\"number\">Frequency (%)</td>\n",
" <td style=\"min-width:200px\">&nbsp;</td>\n",
" </tr></thead>\n",
"\n",
" \n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">자본재</td>\n",
" <td class=\"number\">283</td>\n",
" <td class=\"number\">15.4%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">소재</td>\n",
" <td class=\"number\">226</td>\n",
" <td class=\"number\">12.3%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:80%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">기술하드웨어와장비</td>\n",
" <td class=\"number\">200</td>\n",
" <td class=\"number\">10.9%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:70%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">제약과생물공학</td>\n",
" <td class=\"number\">136</td>\n",
" <td class=\"number\">7.4%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:48%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">자동차와부품</td>\n",
" <td class=\"number\">134</td>\n",
" <td class=\"number\">7.3%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:47%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">소프트웨어와서비스</td>\n",
" <td class=\"number\">123</td>\n",
" <td class=\"number\">6.7%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:44%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">내구소비재와의류</td>\n",
" <td class=\"number\">107</td>\n",
" <td class=\"number\">5.8%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:38%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">반도체와반도체장비</td>\n",
" <td class=\"number\">94</td>\n",
" <td class=\"number\">5.1%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:33%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">디스플레이</td>\n",
" <td class=\"number\">84</td>\n",
" <td class=\"number\">4.6%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:30%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">식품,음료,담배</td>\n",
" <td class=\"number\">71</td>\n",
" <td class=\"number\">3.9%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:25%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">미디어</td>\n",
" <td class=\"number\">55</td>\n",
" <td class=\"number\">3.0%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:20%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">전자와 전기제품</td>\n",
" <td class=\"number\">45</td>\n",
" <td class=\"number\">2.4%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:16%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">운송</td>\n",
" <td class=\"number\">33</td>\n",
" <td class=\"number\">1.8%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:12%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">건강관리장비와서비스</td>\n",
" <td class=\"number\">33</td>\n",
" <td class=\"number\">1.8%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:12%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">소매(유통)</td>\n",
" <td class=\"number\">33</td>\n",
" <td class=\"number\">1.8%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:12%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">증권</td>\n",
" <td class=\"number\">23</td>\n",
" <td class=\"number\">1.2%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:9%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">에너지</td>\n",
" <td class=\"number\">23</td>\n",
" <td class=\"number\">1.2%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:9%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">유틸리티</td>\n",
" <td class=\"number\">20</td>\n",
" <td class=\"number\">1.1%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:7%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">상업서비스와공급품</td>\n",
" <td class=\"number\">19</td>\n",
" <td class=\"number\">1.0%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:7%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">다각화된금융</td>\n",
" <td class=\"number\">17</td>\n",
" <td class=\"number\">0.9%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:6%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"other\">\n",
" <td class=\"fillremaining\">Other values (9)</td>\n",
" <td class=\"number\">81</td>\n",
" <td class=\"number\">4.4%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:29%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
" </table>\n",
"\n",
" </div>\n",
"\n",
"\n",
" </div><div class=\"row variablerow\">\n",
" <div class=\"col-md-3 namecol\">\n",
" <p class=\"h4\">WICS업종코드(대)<br/><small>Categorical</small></p>\n",
" </div>\n",
"\n",
" <div class=\"col-md-3\">\n",
"\n",
" <table class=\"stats \">\n",
" <tr class=\"\"><th>Distinct count</th>\n",
" <td>10</td></tr>\n",
" <tr><th>Unique (%)</th>\n",
" <td>0.5%</td></tr>\n",
" <tr class=\"ignore\"><th>Missing (%)</th>\n",
" <td>0.0%</td></tr>\n",
" <tr class=\"ignore\"><th>Missing (n)</th>\n",
" <td>0</td></tr>\n",
" </table>\n",
"\n",
"\n",
"\n",
" </div>\n",
" \n",
" <div class=\"col-md-6 collapse in\" id=\"minifreqtable-8881448017262349006\">\n",
" <table class=\"mini freq\">\n",
" <tr class=\"\">\n",
" <th>G45</th>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:90%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\" data-delay=500 title=\"Percentage: 29.7%\">\n",
"546\n",
" </div>\n",
" </td>\n",
" </tr>\n",
"<tr class=\"\">\n",
" <th>G25</th>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:59%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\" data-delay=500 title=\"Percentage: 19.5%\">\n",
"358\n",
" </div>\n",
" </td>\n",
" </tr>\n",
"<tr class=\"\">\n",
" <th>G20</th>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:56%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\" data-delay=500 title=\"Percentage: 18.2%\">\n",
"335\n",
" </div>\n",
" </td>\n",
" </tr>\n",
"<tr class=\"other\">\n",
" <th>Other values (7)</th>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:100%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\" data-delay=500 title=\"Percentage: 32.7%\">\n",
"601\n",
" </div>\n",
" </td>\n",
" </tr>\n",
"\n",
" </table>\n",
" </div>\n",
"\n",
"\n",
" <div class=\"col-md-12 text-right\">\n",
" <a role=\"button\" data-toggle=\"collapse\" data-target=\"#freqtable-8881448017262349006, #minifreqtable-8881448017262349006\" aria-expanded=\"true\" aria-controls=\"collapseExample\">\n",
" Toggle details\n",
" </a>\n",
" </div>\n",
" \n",
"\n",
" <div class=\"col-md-12 collapse extrapadding\" id=\"freqtable-8881448017262349006\">\n",
" <table class=\"freq table table-hover\">\n",
" <thead><tr>\n",
" <td class=\"fillremaining\">Value</td>\n",
" <td class=\"number\">Count</td>\n",
" <td class=\"number\">Frequency (%)</td>\n",
" <td style=\"min-width:200px\">&nbsp;</td>\n",
" </tr></thead>\n",
"\n",
" \n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">G45</td>\n",
" <td class=\"number\">546</td>\n",
" <td class=\"number\">29.7%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">G25</td>\n",
" <td class=\"number\">358</td>\n",
" <td class=\"number\">19.5%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:65%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">G20</td>\n",
" <td class=\"number\">335</td>\n",
" <td class=\"number\">18.2%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:61%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">G15</td>\n",
" <td class=\"number\">226</td>\n",
" <td class=\"number\">12.3%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:41%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">G35</td>\n",
" <td class=\"number\">169</td>\n",
" <td class=\"number\">9.2%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:31%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">G30</td>\n",
" <td class=\"number\">81</td>\n",
" <td class=\"number\">4.4%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:15%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">G40</td>\n",
" <td class=\"number\">76</td>\n",
" <td class=\"number\">4.1%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:14%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">G10</td>\n",
" <td class=\"number\">23</td>\n",
" <td class=\"number\">1.2%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:5%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">G55</td>\n",
" <td class=\"number\">20</td>\n",
" <td class=\"number\">1.1%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:4%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">G50</td>\n",
" <td class=\"number\">6</td>\n",
" <td class=\"number\">0.3%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:2%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
" </table>\n",
"\n",
" </div>\n",
"\n",
"\n",
" </div><div class=\"row variablerow\">\n",
" <div class=\"col-md-3 namecol\">\n",
" <p class=\"h4\">WICS업종코드(소)<br/><small>Categorical</small></p>\n",
" </div>\n",
"\n",
" <div class=\"col-md-3\">\n",
"\n",
" <table class=\"stats \">\n",
" <tr class=\"alert\"><th>Distinct count</th>\n",
" <td>80</td></tr>\n",
" <tr><th>Unique (%)</th>\n",
" <td>4.3%</td></tr>\n",
" <tr class=\"ignore\"><th>Missing (%)</th>\n",
" <td>0.0%</td></tr>\n",
" <tr class=\"ignore\"><th>Missing (n)</th>\n",
" <td>0</td></tr>\n",
" </table>\n",
"\n",
"\n",
"\n",
" </div>\n",
" \n",
" <div class=\"col-md-6 collapse in\" id=\"minifreqtable5364978115269279196\">\n",
" <table class=\"mini freq\">\n",
" <tr class=\"\">\n",
" <th>G251010</th>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:9%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\" data-delay=500 title=\"Percentage: 7.0%\">\n",
"&nbsp;\n",
" </div>128\n",
" </td>\n",
" </tr>\n",
"<tr class=\"\">\n",
" <th>G151010</th>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:7%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\" data-delay=500 title=\"Percentage: 5.4%\">\n",
"&nbsp;\n",
" </div>100\n",
" </td>\n",
" </tr>\n",
"<tr class=\"\">\n",
" <th>G201060</th>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:7%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\" data-delay=500 title=\"Percentage: 5.1%\">\n",
"&nbsp;\n",
" </div>94\n",
" </td>\n",
" </tr>\n",
"<tr class=\"other\">\n",
" <th>Other values (77)</th>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:100%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\" data-delay=500 title=\"Percentage: 82.5%\">\n",
"1518\n",
" </div>\n",
" </td>\n",
" </tr>\n",
"\n",
" </table>\n",
" </div>\n",
"\n",
"\n",
" <div class=\"col-md-12 text-right\">\n",
" <a role=\"button\" data-toggle=\"collapse\" data-target=\"#freqtable5364978115269279196, #minifreqtable5364978115269279196\" aria-expanded=\"true\" aria-controls=\"collapseExample\">\n",
" Toggle details\n",
" </a>\n",
" </div>\n",
" \n",
"\n",
" <div class=\"col-md-12 collapse extrapadding\" id=\"freqtable5364978115269279196\">\n",
" <table class=\"freq table table-hover\">\n",
" <thead><tr>\n",
" <td class=\"fillremaining\">Value</td>\n",
" <td class=\"number\">Count</td>\n",
" <td class=\"number\">Frequency (%)</td>\n",
" <td style=\"min-width:200px\">&nbsp;</td>\n",
" </tr></thead>\n",
"\n",
" \n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">G251010</td>\n",
" <td class=\"number\">128</td>\n",
" <td class=\"number\">7.0%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:22%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">G151010</td>\n",
" <td class=\"number\">100</td>\n",
" <td class=\"number\">5.4%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:17%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">G201060</td>\n",
" <td class=\"number\">94</td>\n",
" <td class=\"number\">5.1%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:16%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">G453010</td>\n",
" <td class=\"number\">94</td>\n",
" <td class=\"number\">5.1%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:16%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">G352020</td>\n",
" <td class=\"number\">92</td>\n",
" <td class=\"number\">5.0%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:16%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">G454020</td>\n",
" <td class=\"number\">82</td>\n",
" <td class=\"number\">4.5%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:14%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">G452030</td>\n",
" <td class=\"number\">72</td>\n",
" <td class=\"number\">3.9%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:13%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">G452015</td>\n",
" <td class=\"number\">67</td>\n",
" <td class=\"number\">3.6%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:12%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">G252060</td>\n",
" <td class=\"number\">64</td>\n",
" <td class=\"number\">3.5%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:11%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">G302020</td>\n",
" <td class=\"number\">58</td>\n",
" <td class=\"number\">3.2%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:10%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">G201030</td>\n",
" <td class=\"number\">58</td>\n",
" <td class=\"number\">3.2%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:10%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">G151050</td>\n",
" <td class=\"number\">47</td>\n",
" <td class=\"number\">2.6%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:8%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">G254020</td>\n",
" <td class=\"number\">45</td>\n",
" <td class=\"number\">2.4%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:8%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">G452010</td>\n",
" <td class=\"number\">44</td>\n",
" <td class=\"number\">2.4%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:8%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">G451020</td>\n",
" <td class=\"number\">44</td>\n",
" <td class=\"number\">2.4%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:8%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">G151040</td>\n",
" <td class=\"number\">37</td>\n",
" <td class=\"number\">2.0%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:7%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">G451030</td>\n",
" <td class=\"number\">34</td>\n",
" <td class=\"number\">1.8%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:6%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">G201025</td>\n",
" <td class=\"number\">31</td>\n",
" <td class=\"number\">1.7%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:6%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">G451035</td>\n",
" <td class=\"number\">28</td>\n",
" <td class=\"number\">1.5%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:5%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">G351010</td>\n",
" <td class=\"number\">28</td>\n",
" <td class=\"number\">1.5%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:5%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"other\">\n",
" <td class=\"fillremaining\">Other values (60)</td>\n",
" <td class=\"number\">593</td>\n",
" <td class=\"number\">32.2%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
" </table>\n",
"\n",
" </div>\n",
"\n",
"\n",
" </div><div class=\"row variablerow\">\n",
" <div class=\"col-md-3 namecol\">\n",
" <p class=\"h4\">WICS업종코드(중)<br/><small>Categorical</small></p>\n",
" </div>\n",
"\n",
" <div class=\"col-md-3\">\n",
"\n",
" <table class=\"stats \">\n",
" <tr class=\"\"><th>Distinct count</th>\n",
" <td>29</td></tr>\n",
" <tr><th>Unique (%)</th>\n",
" <td>1.6%</td></tr>\n",
" <tr class=\"ignore\"><th>Missing (%)</th>\n",
" <td>0.0%</td></tr>\n",
" <tr class=\"ignore\"><th>Missing (n)</th>\n",
" <td>0</td></tr>\n",
" </table>\n",
"\n",
"\n",
"\n",
" </div>\n",
" \n",
" <div class=\"col-md-6 collapse in\" id=\"minifreqtable-2139250075341868693\">\n",
" <table class=\"mini freq\">\n",
" <tr class=\"\">\n",
" <th>G2010</th>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:25%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\" data-delay=500 title=\"Percentage: 15.4%\">\n",
"283\n",
" </div>\n",
" </td>\n",
" </tr>\n",
"<tr class=\"\">\n",
" <th>G1510</th>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:20%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\" data-delay=500 title=\"Percentage: 12.3%\">\n",
"&nbsp;\n",
" </div>226\n",
" </td>\n",
" </tr>\n",
"<tr class=\"\">\n",
" <th>G4520</th>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:18%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\" data-delay=500 title=\"Percentage: 10.9%\">\n",
"&nbsp;\n",
" </div>200\n",
" </td>\n",
" </tr>\n",
"<tr class=\"other\">\n",
" <th>Other values (26)</th>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:100%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\" data-delay=500 title=\"Percentage: 61.5%\">\n",
"1131\n",
" </div>\n",
" </td>\n",
" </tr>\n",
"\n",
" </table>\n",
" </div>\n",
"\n",
"\n",
" <div class=\"col-md-12 text-right\">\n",
" <a role=\"button\" data-toggle=\"collapse\" data-target=\"#freqtable-2139250075341868693, #minifreqtable-2139250075341868693\" aria-expanded=\"true\" aria-controls=\"collapseExample\">\n",
" Toggle details\n",
" </a>\n",
" </div>\n",
" \n",
"\n",
" <div class=\"col-md-12 collapse extrapadding\" id=\"freqtable-2139250075341868693\">\n",
" <table class=\"freq table table-hover\">\n",
" <thead><tr>\n",
" <td class=\"fillremaining\">Value</td>\n",
" <td class=\"number\">Count</td>\n",
" <td class=\"number\">Frequency (%)</td>\n",
" <td style=\"min-width:200px\">&nbsp;</td>\n",
" </tr></thead>\n",
"\n",
" \n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">G2010</td>\n",
" <td class=\"number\">283</td>\n",
" <td class=\"number\">15.4%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">G1510</td>\n",
" <td class=\"number\">226</td>\n",
" <td class=\"number\">12.3%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:80%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">G4520</td>\n",
" <td class=\"number\">200</td>\n",
" <td class=\"number\">10.9%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:70%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">G3520</td>\n",
" <td class=\"number\">136</td>\n",
" <td class=\"number\">7.4%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:48%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">G2510</td>\n",
" <td class=\"number\">134</td>\n",
" <td class=\"number\">7.3%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:47%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">G4510</td>\n",
" <td class=\"number\">123</td>\n",
" <td class=\"number\">6.7%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:44%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">G2520</td>\n",
" <td class=\"number\">107</td>\n",
" <td class=\"number\">5.8%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:38%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">G4530</td>\n",
" <td class=\"number\">94</td>\n",
" <td class=\"number\">5.1%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:33%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">G4540</td>\n",
" <td class=\"number\">84</td>\n",
" <td class=\"number\">4.6%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:30%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">G3020</td>\n",
" <td class=\"number\">71</td>\n",
" <td class=\"number\">3.9%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:25%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">G2540</td>\n",
" <td class=\"number\">55</td>\n",
" <td class=\"number\">3.0%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:20%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">G4535</td>\n",
" <td class=\"number\">45</td>\n",
" <td class=\"number\">2.4%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:16%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">G3510</td>\n",
" <td class=\"number\">33</td>\n",
" <td class=\"number\">1.8%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:12%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">G2550</td>\n",
" <td class=\"number\">33</td>\n",
" <td class=\"number\">1.8%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:12%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">G2030</td>\n",
" <td class=\"number\">33</td>\n",
" <td class=\"number\">1.8%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:12%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">G1010</td>\n",
" <td class=\"number\">23</td>\n",
" <td class=\"number\">1.2%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:9%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">G4020</td>\n",
" <td class=\"number\">23</td>\n",
" <td class=\"number\">1.2%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:9%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">G5510</td>\n",
" <td class=\"number\">20</td>\n",
" <td class=\"number\">1.1%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:7%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">G2020</td>\n",
" <td class=\"number\">19</td>\n",
" <td class=\"number\">1.0%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:7%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"\">\n",
" <td class=\"fillremaining\">G4030</td>\n",
" <td class=\"number\">17</td>\n",
" <td class=\"number\">0.9%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:6%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr class=\"other\">\n",
" <td class=\"fillremaining\">Other values (9)</td>\n",
" <td class=\"number\">81</td>\n",
" <td class=\"number\">4.4%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:29%\">&nbsp;</div>\n",
" </td>\n",
"</tr>\n",
"\n",
" </table>\n",
"\n",
" </div>\n",
"\n",
"\n",
" </div><div class=\"row variablerow\">\n",
" <div class=\"col-md-3 namecol\">\n",
" <p class=\"h4\">WMI500 시가총액규모별(1:대형주,2:중형주,3:소형주,0:WMI500미해당)<br/><small>Numeric</small></p>\n",
" </div>\n",
"\n",
" <div class=\"col-md-6\">\n",
" <div class=\"row\">\n",
" <div class=\"col-sm-6\">\n",
" <table class=\"stats \">\n",
" <tr><th>Distinct count</th>\n",
" <td>4</td></tr>\n",
" <tr><th>Unique (%)</th>\n",
" <td>0.2%</td></tr>\n",
" <tr class=\"ignore\"><th>Missing (%)</th>\n",
" <td>0.0%</td></tr>\n",
" <tr class=\"ignore\"><th>Missing (n)</th>\n",
" <td>0</td></tr>\n",
" </table>\n",
"\n",
" </div>\n",
" <div class=\"col-sm-6\">\n",
" <table class=\"stats \">\n",
"\n",
" <tr><th>Mean</th>\n",
" <td>0.59728</td></tr>\n",
" <tr><th>Minimum</th>\n",
" <td>0</td></tr>\n",
" <tr><th>Maximum</th>\n",
" <td>3</td></tr>\n",
" <tr class=\"alert\"><th>Zeros (%)</th>\n",
" <td>72.9%</td></tr>\n",
" </table>\n",
" </div>\n",
" </div>\n",
" </div>\n",
" <div class=\"col-md-3 collapse in\" id=\"minihistogram-3659563249147917418\">\n",
" <img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAMgAAABLCAYAAAA1fMjoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAABBRJREFUeJzt3T9II2kcxvEnfwyKjUcyIWqTQq868JDFQgsJBCLidccFrrUVCz0DgoicqIUBGyuRiGCTa8QjWChItBCLqOtuI4vgFncQJy4RjsMLOr5XHDcguL8lupM3Mc%2BnG1%2BHecH3q%2B8Ek3EppRQqzDRNpNNpxONxBIPBSl%2BeaozO9eLSEQgAWJb1rPPcbjdcLtdXng3R07y6Lrz4%2B3v8WfynrHPav2nELz98B69X27Spzmhbae/%2B%2BAsf8n%2BXdc63oWaHZkP0NLfuCRBVMwZCJGAgRAIGQiRgIEQCBkIkYCBEAgZCJGAgRAIGQiRgIEQCBkIkYCBEAgZCJGAgRAIGQiRgIEQCBkIkYCBEAgZCJGAgRAIGQiRgIEQCBkIkYCBEAgZCJGAgRAIGQiRgIEQCBkIkYCBEAgZCJGAgRAIGQiRgIEQCBkIkYCBEAj5P%2BRVQSj37ufMej6diz51/yTx1PfqbgbwClmVh%2Bre3%2BPjptqzzwv4m/PrT9xVbfC%2BZ5/zPbxyalYyBvBIfP92W/dx5HWplnv9jIIJa2bqQcxiIoFa2LuQc/gS/oNa2BPR18WVeIoG2vyA/vmnDze1dWee0NDU8%2B57gOSzLQtjfVPZ5YX8T5/mEl8xTF5dSSlX6oqZpIp1OIx6PIxgMVvryVGN0rhctW6xCoYDl5WUUCgUdl6cao3O98B6ESMBAiAQMhEjAQIgEWgIxDAMjIyMwDEPH5anG6FwvWl7mJaoV3GIRCRgIkYCBEAkYCJGAgRAJGAiRgIEQCRx/P8jKygp2dnbg9XrR1dWFycnJR%2BObm5vY2NhAY2Mj2tvbMTc3h4aGBqenRVVIKYVkMolcLgev1wu/34%2BFhQU0Nzfb31Px9aIcdHZ2poaGhlSpVFIPDw9qeHhY7e7u2uP5fF719/erYrGolFJqampKra2tOTklqmLHx8dqfHzcPk4kEiqVStnHOtaLo1usg4MDRKNR%2BHw%2BuFwuxGIxZLNZe/zw8BA9PT1oaWkBAAwODmJ/f9/JKVEV6%2B7uRjKZBADc3d3h%2Bvoara2t9riO9eJoIKZpIhAI2MeGYSCfz392PBAIPBqn%2BrS4uIhIJIJwOIyBgQH76zrWC2/SqepMTEwgm83i5uYGq6urWufiaCChUAimadrHpmmira3t0fjV1dVnx6m%2BXFxc4Pz8HMB/n8Ubi8WQy%2BXscR3rxdFAIpEI9vb2UCqVcH9/j0wmg2g0ao/39fXh9PQUxWIRALC1tfVonOrL5eUlZmdn7U9aOTk5QWdnpz2uY704/u/u6%2BvryGQy8Hg86O3txejoKMbGxpBIJBAKhbC9vY1UKgWfz4eOjg7MzMzA7ebOr14tLS3h6OgIbrcbhmFgfn4e09PT2tYL3w9CJOCvaiIBAyES/AtLI2EmlZmxPgAAAABJRU5ErkJggg%3D%3D\">\n",
"\n",
" </div>\n",
" <div class=\"col-md-12 text-right\">\n",
" <a role=\"button\" data-toggle=\"collapse\" data-target=\"#descriptives-3659563249147917418,#minihistogram-3659563249147917418\" aria-expanded=\"false\" aria-controls=\"collapseExample\">\n",
" Toggle details\n",
" </a>\n",
" </div>\n",
" <div class=\"row collapse col-md-12\" id=\"descriptives-3659563249147917418\">\n",
" <div class=\"col-sm-4\">\n",
" <p class=\"h4\">Quantile statistics</p>\n",
" <table class=\"stats indent\">\n",
" <tr><th>Minimum</th>\n",
" <td>0</td></tr>\n",
" <tr><th>5-th percentile</th>\n",
" <td>0</td></tr>\n",
" <tr><th>Q1</th>\n",
" <td>0</td></tr>\n",
" <tr><th>Median</th>\n",
" <td>0</td></tr>\n",
" <tr><th>Q3</th>\n",
" <td>1</td></tr>\n",
" <tr><th>95-th percentile</th>\n",
" <td>3</td></tr>\n",
" <tr><th>Maximum</th>\n",
" <td>3</td></tr>\n",
" <tr><th>Range</th>\n",
" <td>3</td></tr>\n",
" <tr><th>Interquartile range</th>\n",
" <td>1</td></tr>\n",
" </table>\n",
" <p class=\"h4\">Descriptive statistics</p>\n",
" <table class=\"stats indent\">\n",
" <tr><th>Standard deviation</th>\n",
" <td>1.0539</td></tr>\n",
" <tr><th>Coef of variation</th>\n",
" <td>1.7645</td></tr>\n",
" <tr><th>Kurtosis</th>\n",
" <td>0.3618</td></tr>\n",
" <tr><th>Mean</th>\n",
" <td>0.59728</td></tr>\n",
" <tr><th>MAD</th>\n",
" <td>0.8706</td></tr>\n",
" <tr class=\"\"><th>Skewness</th>\n",
" <td>1.417</td></tr>\n",
" <tr><th>Sum</th>\n",
" <td>1099</td></tr>\n",
" <tr><th>Variance</th>\n",
" <td>1.1107</td></tr>\n",
" <tr><th>Memory size</th>\n",
" <td>14.5 KiB</td></tr>\n",
" </table>\n",
" </div>\n",
" <div class=\"col-sm-8 histogram\">\n",
" <img 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Onv2rGprayVJgwYN0owZM1RQUKCysjLNmDFDFRUV2rdvn7Zu3SpJmjdvnvLy8jR9%2BnTFxsaquLhYoaGhmjx5sux2uzIyMlRYWKikpCSFhoZq/fr1uvrqq7t9BSsmJqbzS47l%2B4wdvz%2B0tLgl%2BfePu7W1TS0tPMD0FPWzhvpZQ/2soX491%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%2B88YavpgQAAPALnwWs%2B%2B%2B/Xw0NDfrVr36la6%2B9Vps3b1ZDQ4OvpgcAAPAZnwWs9PR0/e53v9PevXv1i1/8Qn/5y180ZcoU3XnnnaqoqPDVMgAAALzO509yj4qK0oIFC/Tcc88pPz9fBw4c0M0336zrrrtOr776qq%2BXAwAAYFywrydsaGjQ9u3b9ec//1kffvihJk2apMzMTB07dkz33nuvjh07pptuusnSHIcOHdLatWtVU1Oj0NBQpaamavny5RowYID279%2Bvxx9/XO%2B//76GDh2qrKwspaent/9sSUmJ/vSnP%2Bnzzz9XbGys7r77bjmdTquHDQAAfkB8FrD27t2rsrIy7d69W5GRkbrxxhuVmZmpIUOGtI%2B55JJLtHTpUksBq7W1VbfeeqtmzZql3/3udzp58qTy8vJ0//33a/ny5crOztZ9992n66%2B/Xm%2B%2B%2BaZuv/12XXLJJUpKStKuXbtUWFio4uJixcbG6umnn9bChQv1l7/8ReHh4SbKAAAAfgB8dovwtttuU1NTk9atW6c9e/YoJyenQ7iSpHHjxlkOMp9//rnq6%2BuVnp6ukJAQRUVF6ZprrtGhQ4e0c%2BdOjRw5UrNmzVJISIgmTJigq666SmVlZZKksrIyzZ49W06nUyEhIcrKypLdbtfu3bstrQkAAPyw%2BCxgvfLKK9q8ebOmTp2qoKAgSdLJkyc7jOnTp4/l52ENGjRIiYmJKi0t1alTp/TFF1/ov/7rvzRlyhRVV1crMTGxw/j4%2BHhVVlZKkqqqqpSQkNChPy4urr0fAACgO3wWsOx2u6677jqVl5e3t23dulXXXXedPvnkE6NzPf744yovL9e4ceM0ceJEud1u5eXlyeVyyeFwdBgbGRmpxsZGSeqy3%2BVyGV0fAADo3Xz2HKyHHnpIsbGxGjduXHvbjBkzdOTIET3yyCPauHGjkXnOnDmjhQsX6tprr9Vtt92mU6dO6f7779ddd90lSXK73Ubm6UpdXZ3q6%2Bu9Ooc/BAfb5K9PVgoKsnf4ivND/ayhftZQP2uonzVBQfYuz8vR0dGKiYnx2tw%2BC1hvvfWW/vu//7vDc6x%2B9KMf6b777tPUqVONzVNRUaFjx44pNzdXktS3b19lZ2crPT1dP/3pTz2uRrlcLg0cOFDSP95C4purWd/uHzt2bLfnLy0tVUFBgUf7uOzi8z2UC0pERLiCg33%2BotMOHA5eaGAF9bOG%2BllD/ayhfj23ZUtxp%2Bfl7OxsLV682Gvz%2BuyM6Xa7debMGY8nsZ88eVKtra3G5mlra2v/z27/R%2BJvaWmRzWbTFVdcoT//%2Bc8dxldWVio5OVmSlJSUpOrq6va3bWhra1NNTY3mzJnT7fkzMzOVlpbm0f5QeWC/a31TU7P8eQXL4QjXiRPNam1t88saAhn1s4b6WUP9rKF%2B1gQF2bs8L0dHR3t1bp8FrEmTJmnZsmXKycnR8OHD1dbWpr///e9av369pkyZYmyeyy67TBdddJGefPJJ3X777WpubtZTTz2llJQUzZgxQwUFBSorK9OMGTNUUVGhffv2aevWrZKkefPmKS8vT9OnT1dsbKyKi4sVGhp6XuuLiYnp/JJj%2BT5DR%2BgfLS1uSf79425tbVNLCw8wPUX9rKF%2B1lA/a6hfz3V5XvYynwWs5cuXa9GiRUpPT5fNZmtvT0lJ0apVq4zN079/f/3%2B97/X2rVrNXnyZPXp00fjx4/X%2BvXrFRUVpU2bNmnNmjV64IEHNGzYMOXn52vMmDGS/hECc3NzlZOTo4aGBjmdThUVFSkkJMTY%2BgAAQO9nc3v7Wd/fcfjwYX300UcKCgrSxRdfrEsuucSX0/vN/A37dOT4V/5eRo%2BMHdxXT8xNlmQ751hvCA62a8CAvmps/Ip/wfUA9bOG%2BllD/ayhftZ8Uz%2B/zO3rCePi4hQXF%2BfraQEAAHzGZwGrsrJSa9as0ZEjR3T69GmP/kOHDvlqKQAAAF7ls4C1cuVKXXTRRVq8eLEuuugiX00LAADgcz4LWB9%2B%2BKFef/119evXz1dTAgAA%2BIXP3tho6NChvpoKAADAr3wWsO688049/PDDHh/wDAAA0Nv47BZhYWGhjh49qh07dqh///7t77L%2Bjddff91XSwEAAPAqnwWszt6mHgAAoDfyWcDKzs721VQAAAB%2B5dNP792/f7%2BWLl2qf/3Xf5X0jw9Tfumll3y5BAAAAK/zWcD6z//8T916661qamrS//zP/0iSjh8/rlWrVmnbtm2%2BWgYAAIDX%2BSxgPfXUU1q3bp02bdrU/mHPQ4cO1ZNPPqn/%2BI//8NUyAAAAvM5nAevjjz/W1VdfLUntAUuSxo8fr6NHj/pqGQAAAF7ns4A1YMAANTQ0eLR/8MEH6tvXP590DQAA4A0%2BC1gTJkzQihUr9Pe//12S5HK59PrrrysnJ0dTp0711TIAAAC8zmcBa9myZWpubtb06dP19ddfKzU1Vb/%2B9a81bNgw3X333b5aBgAAgNf57H2w%2Bvfvr6efflqHDx/W%2B%2B%2B/r7CwMI0cOVIjR4701RIAAAB8wmcB6xtxcXGKi4vz9bQAAAA%2B47OAFRcX1%2BHVg9916NAhXy0FAADAq3wWsFatWtUhYLW2tuqDDz7Q3r17tXDhQl8tAwAAwOt8FrDmzZvXaftf//pXlZaWatasWb5aCgAAgFf59LMIO3P55Zdrz549/l4GAACAMX4PWOXl5QoO9vlz7QEAALzGZ8lm4sSJHm2nT5/WV1991eXtQwAAgEDks4CVmZnp8SrC0NBQjR49Wmlpab5aBgAAgNf5LGAtXrzYV1MBAAD4lc8CVkFBQbfG2Ww2LVq0yMurAQAA8B6fBawdO3aovr5eX3/9tSIjI9XW1qampiaFhYWpX79%2BHcYSsAAAQCDzWcDKzc3V7t27dffdd2vgwIGSpOPHj2vt2rX62c9%2Bpuuvv95XSwEAAPAqn71NwxNPPKF77723PVxJ0uDBg3Xffffpt7/9ra%2BWAQAA4HU%2BC1ifffaZgoKCPNr79OmjL774wlfLAAAA8DqfBawxY8Zo2bJlqq6u1okTJ3Ty5EkdPnxYy5cvV2xsrK%2BWAQAA4HU%2Bew7Wgw8%2BqLy8PP385z9vfz8st9utoUOHauPGjb5aBgAAgNf5LGAlJCTo5ZdfVlVVlT777DO53W4NHjxYSUlJstv9/ok9AAAAxvj8QwAdDoeampqUmprq66kBAAB8wmeXjhoaGjR37lxdffXVuuWWWyRJ9fX1mj59uj777DPj8xUWFmrixIm67LLLdPPNN%2Bvo0aOSpP3792vOnDkaN26cbrjhBu3YsaPDz5WUlGjatGlKSUnRggULVFlZaXxtAACgd/NZwFq7dq3CwsK0bdu29luCERERSkhI0KOPPmp0rmeffVYvvPCCnnnmGe3bt0%2BjRo1SSUmJ6urqlJ2drfnz56uiokIrVqzQ6tWrVVVVJUnatWuXCgsLlZ%2BfrwMHDigtLU0LFy5Uc3Oz0fUBAIDezWcBa%2B/evXr00UfldDrbn%2BQeFhame%2B65R6%2B//rrRuTZv3qzc3FxdfPHF6tevn%2B69917de%2B%2B92rlzp0aOHKlZs2YpJCREEyZM0FVXXaWysjJJUllZmWbPni2n06mQkBBlZWXJbrdr9%2B7dRtcHAAB6N58FrLNnzyomJsajPSwsTGfPnjU2T21trY4ePaoTJ07o%2Buuv1/jx45WTk6PGxkZVV1crMTGxw/j4%2BPj224BVVVVKSEjo0B8XF8dtQgAAcF58FrBGjx6tV1991aO9tLRUo0aNMjZPbW2tJOmVV17Rli1b9MILL%2Bj48eO677775HK55HA4OoyPjIxUY2OjJHXZ73K5jK0PAAD0fj57FeHNN9%2BspUuX6qWXXlJra6sefPBBVVdX65133tHjjz9ubB632y1JuuWWW/SjH/1IkrR48WLdcsstuvzyy9v7vaWurk719fVencMfgoNt8mEe7yAoyN7hK84P9bOG%2BllD/ayhftYEBdm7PC9HR0d3emfNFJ8FrGnTpikyMlLPPvusfvzjH%2Butt97SyJEjtXz5cl166aXG5vkmVEVERLS3DR06VG1tbZLkcTXK5XK1fz5iVFRU%2B9Wsb/ePHTu22/OXlpaqoKDAo31cdnG3f8eFKCIiXMHBPn9Xjw4cjnC/zh/oqJ811M8a6mcN9eu5LVuKOz0vZ2dna/HixV6b1ydnzLa2NlVVVSk1NdXr7381ePBgRURE6NChQ4qPj5ckHT16VH369NHkyZM93pahsrJSycnJkqSkpCRVV1crPT29fd01NTWaM2dOt%2BfPzMxUWlqaR/tD5Q09PaQLQlNTs/x5BcvhCNeJE81qbW3zyxoCGfWzhvpZQ/2soX7WBAXZuzwvR0dHe3VunwQsu92uX/7yl3rjjTc6/cBnk4KCgpSRkaFNmzYpJSVFffv21caNGzVz5kylp6dr48aNKisr04wZM1RRUaF9%2B/Zp69atkqR58%2BYpLy9P06dPV2xsrIqLixUaGqopU6Z0e/6YmJjOLzmW7zN0hP7R0uKW5N8/7tbWNrW08ADTU9TPGupnDfWzhvr1XJfnZS/z2T2fmTNnqqSkRDfffHP72zR4S05Ojk6fPq05c%2BaopaVF11xzjVasWKHw8HBt2rRJa9as0QMPPKBhw4YpPz9fY8aMkSRNmjRJubm5ysnJUUNDg5xOp4qKihQSEuLV9QIAgN7F5vb2s77/3%2B23366qqiq1tLRo6NChHqHlT3/6ky%2BW4TfzN%2BzTkeNf%2BXsZPTJ2cF89MTdZkneDcVeCg%2B0aMKCvGhu/4l9wPUD9rKF%2B1lA/a6ifNd/Uzy9z%2B2qiAQMGaNKkSb6aDgAAwG%2B8HrCWLFmiJ598Uo888kh725NPPqklS5Z4e2oAAAC/8PrLwvbs2ePR9vvf/97b0wIAAPiNX15376OnfQEAAPiFXwKWt19FCAAA4E%2B89z4AAIBhBCwAAADDvP4qwrNnzyovL%2B%2BcbY899pi3lwIAAOATXg9Y48aNU11d3TnbAAAAeguvB6ynn37a21MAAABcUHgOFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGG9PmA9/PDDiouLa/9%2B//79mjNnjsaNG6cbbrhBO3bs6DC%2BpKRE06ZNU0pKihYsWKDKykpfLxkAAAS4Xh2wDh06pOeff142m02SVFtbq%2BzsbM2fP18VFRVasWKFVq9eraqqKknSrl27VFhYqPz8fB04cEBpaWlauHChmpub/XkYAAAgwPTagOV2u7V69WrdfPPN7W07d%2B7UyJEjNWvWLIWEhGjChAm66qqrVFZWJkkqKyvT7Nmz5XQ6FRISoqysLNntdu3evdtfhwEAAAJQrw1Yf/zjHxUeHq7p06erR%2BryAAAQwElEQVS3t9XU1CgxMbHDuPj4%2BPbbgFVVVUpISOjQHxcXx21CAABwXoL9vQBv%2BPzzz7Vx40Y9%2B%2ByzHdpdLpcGDx7coS0yMlKNjY3t/Q6Hw6Pf5XJ5d8EAAKBX6ZUBa%2B3atZo7d67%2B6Z/%2BSceOHevQ53a7vTp3XV2d6uvrvTqHPwQH2%2BSvC55BQfYOX3F%2BqJ811M8a6mcN9bMmKMje5Xk5OjpaMTExXpu71wWsiooKVVVV6eGHH5bUMVANGDDA42qUy%2BXSwIEDJUlRUVHtV7O%2B3T927Nhuz19aWqqCggKP9nHZxd3%2BHReiiIhwBQf7d7s4HOF%2BnT/QUT9rqJ811M8a6tdzW7YUd3pezs7O1uLFi702b68LWC%2B88IJqa2v105/%2BVNI/Apbb7VZqaqpuuukm7dy5s8P4yspKJScnS5KSkpJUXV2t9PR0SVJbW5tqamo0Z86cbs%2BfmZmptLQ0j/aHyht6ekgXhKamZvnzCpbDEa4TJ5rV2trmlzUEMupnDfWzhvpZQ/2sCQqyd3lejo6O9urcvS5gLV%2B%2BXDk5Oe3fHz9%2BXJmZmXr%2B%2BefV2tqqoqIilZWVacaMGaqoqNC%2Bffu0detWSdK8efOUl5en6dOnKzY2VsXFxQoNDdWUKVO6PX9MTEznlxzL91k9NL9qaXFL8u8fd2trm1paeIDpKepnDfWzhvpZQ/16rsvzspf1uoAVERGhiIiI9u9bWlpks9nai7tp0yatWbNGDzzwgIYNG6b8/HyNGTNGkjRp0iTl5uYqJydHDQ0NcjqdKioqUkhIiF%2BOBQAABKZeF7C%2Ba9iwYTp06FD79ykpKR7v3v5tc%2BfO1dy5c32xNAAA0EvxsgQAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhvXKgPXpp58qOztb48eP1xVXXKFly5bp5MmTkqT9%2B/drzpw5GjdunG644Qbt2LGjw8%2BWlJRo2rRpSklJ0YIFC1RZWemPQwAAAAGsVwasO%2B64Qw6HQ3v27NGLL76o999/X48%2B%2Bqjq6uqUnZ2t%2BfPnq6KiQitWrNDq1atVVVUlSdq1a5cKCwuVn5%2BvAwcOKC0tTQsXLlRzc7OfjwgAAASSXhewTp48qcTERN11110KCwvTwIEDlZ6eroMHD2rnzp0aOXKkZs2apZCQEE2YMEFXXXWVysrKJEllZWWaPXu2nE6nQkJClJWVJbvdrt27d/v5qAAAQCDpdQGrX79%2BeuihhxQVFdXe9umnn2rQoEGqrq5WYmJih/Hx8fHttwGrqqqUkJDQoT8uLo7bhAAA4Lz0uoD1XZWVlXruued0%2B%2B23y%2BVyyeFwdOiPjIxUY2OjJHXZ73K5fLZeAAAQ%2BIL9vQBv%2Btvf/qY77rhDd911l1JTU1VcXCy32%2B3VOevq6lRfX%2B/VOfwhONgmf%2BXxoCB7h684P9TPmsCvn/v///MPu11qaWmR3S4F9/iMY/v//354/L///Lt/rLLbuz4vR0dHKyYmxmtz99qAVV5ern//93/XfffdpxkzZkiSBgwY4HE1yuVyaeDAgZKkqKio9qtZ3%2B4fO3Zst%2BctLS1VQUGBR/u47OLzPYQLSkREuIJ7/uhohMMR7tf5Ax31syZQ69fS0qL7tv6PPvwiMF%2Bsc/HAcD2Q8c9%2Bf/zxN3/tv96wf4Z8UdHpeTk7O1uLFy/22ty9cse%2B%2Beabuueee7Rhwwalpqa2tyclJWn79u0dxlZWVio5Obm9v7q6Wunp6ZKktrY21dTUaM6cOd2eOzMzU2lpaR7tD5U39ORQLhhNTc3y5xUshyNcJ040q7W1zS9rCGTUz5rAr1%2BbPvyiWUeOf%2BXvhfSYPx9//M3/%2By/w909OF%2Bfl6Ohor87b6wJWa2urVq5c2X5b8NtmzJihgoIClZWVacaMGaqoqNC%2Bffu0detWSdK8efOUl5en6dOnKzY2VsXFxQoNDdWUKVO6PX9MTEznlxzL91k5LL9raXFL8u/JpbW1TS0tgXiCuzBQP2sCt36Be3vnGxfC44%2B/%2BW//Bf7%2B6fK87GW9LmC99dZbev/997VmzRo9%2BOCDstlscrvdstlseuWVV7Rp0yatWbNGDzzwgIYNG6b8/HyNGTNGkjRp0iTl5uYqJydHDQ0NcjqdKioqUkhIiJ%2BPCgAABJJeF7BSUlJ06NChLvuHDBni8e7t3zZ37lzNnTvXG0sDAAA/ED/Mm9oAAABeRMACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABgW7O8FADgXt8Wfb1NLS4ukNgO/ywqbH%2BcGAN8iYAEB4Ld/OaIPv2j29zJ65OKB4brzZ2P9vQwA8CkCFhAAPvyiWUeOf%2BXvZQAAuonnYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwvuPo0aO69dZbNX78eKWlpek3v/mN3G63v5cFAAACCAHrO5YsWaIhQ4aovLxcW7ZsUXl5uUpKSvy9LAAAEEAIWN9SWVmpI0eOaOnSperbt69GjBihm266Sdu2bfP30gAAQAAhYH1LTU2Nhg0bpn79%2BrW3xcfH64MPPtCpU6f8uDIAABBICFjf4nK55HA4OrT1799fktTY2OiPJQEAgAAU7O8FXGisPqG9rq5O9fX1Hu0XDwy39Hv96eKB4Qr2406x26WWlhbZ7fLrOvyJ/eM/vWH/sX8C14Ww/wJ9/3R1Xo6OjlZMTIzX5v4Bb1tPUVFRcrlcHdpcLpdsNpuioqK69TtKS0tVUFDg0X755Zfr8fXrvfo/s7eqq6vTH/7wH8rMzPzB1u/h%2BSk9/tm6ujqVlpb%2BoOtnRW/Yf1b2j1XsP2suhP3nz/1jVV1dnXJzc3Xw4EGPvuzsbC1evNhrcxOwviUpKUmfffaZXC5X%2B63Bd955R6NHj1Z4ePcSfGZmptLS0jq0vffee1q6dKnq6%2Bt5gOmB%2Bvp6FRQUKC0tjfr1APWzhvpZQ/2soX7W1NfX6%2BDBg8rPz9fo0aM79EVHR3t1bgLWt8THx8vpdOqxxx7TsmXLVFtbq5KSEmVlZXX7d8TExPBHAADABWT06NFKTEz06Zw8yf07nnjiCdXW1mrixIn65S9/qVmzZmnevHn%2BXhYAAAggXMH6jkGDBqmoqMjfywAAAAGMK1gAAACGBa1evXq1vxfxQ9C3b1/95Cc/Ud%2B%2Bff29lIBE/ayhftZQP2uonzXUzxp/1c/m5pOMAQAAjOIWIQAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwApYhR48e1a233qrx48crLS1Nv/nNb9TVe7iWlJRo2rRpSklJ0YIFC1RZWenj1V54ulu/goICJSQkKDk5WcnJybr00kuVnJyshoYGP6z6wrF3715deeWVysvLO%2BdY9p%2Bn7taP/de5Tz/9VNnZ2Ro/fryuuOIKLVu2TCdPnux0LPvPU3frx/7r3OHDh/WrX/1KKSkpmjhxou688059/vnnnY715f4jYBmyZMkSDRkyROXl5dqyZYvKy8tVUlLiMW7Xrl0qLCxUfn6%2BDhw4oLS0NC1cuFDNzc2%2BX/QFpLv1k6SZM2fq7bff1ttvv6133nlHb7/9tqKiony74AtIUVGR8vPzNWrUqHOOZf95Op/6Sey/ztxxxx1yOBzas2ePXnzxRb3//vt69NFHPcax/zrX3fpJ7L/vOnPmjLKysjRhwgRVVFTohRdeUH19ve6//36Psb7efwQsAyorK3XkyBEtXbpUffv21YgRI3TTTTdp27ZtHmPLyso0e/ZsOZ1OhYSEKCsrS3a7Xbt37/bDyi8M51M/eOrfv7%2B2bdum4cOHn3Ms%2B8/T%2BdQPnk6ePKnExETdddddCgsL08CBA5Wenq6DBw96jGX/eTqf%2BsHT6dOndeedd%2BrWW29Vnz59FBUVpWuuuUZHjhzxGOvr/UfAMqCmpkbDhg1Tv3792tvi4%2BP1wQcf6NSpUx3GVlVVKSEhoUNbXFzcD/oy%2BfnUT5LeffddzZ07V%2BPGjdMNN9yg/fv3%2B3K5F5yMjAyFhYV1ayz7z9P51E9i/31Xv3799NBDD3W4inLs2DENGjTIYyz7z9P51E9i/32Xw%2BHQjTfeKLv9H3Hmo48%2B0vbt2zV9%2BnSPsb7efwQsA1wulxwOR4e2/v37S5IaGxvPOTYyMlIul8u7i7yAnU/9YmJiNGzYMK1du1avv/660tPTddttt%2BmDDz7w2XoDGfvPGvbfuVVWVuq5557TwoULPfrYf%2Bf2ffVj/3Xt008/VVJSkq699lolJSUpOzvbY4yv9x8By5CuntCO7ulu/TIyMrRhwwZdfPHFCg8PV1ZWluLj4/XCCy94eYUA%2B%2B9c/va3v%2BnXv/61li5dqgkTJvh7OQHnXPVj/3Vt6NChqqqq0iuvvKKPPvpIubm5/l4SAcuEqKgojwTscrlks9k8nnwYFRXV6VWtH/KTFM%2Bnfp0ZPny46uvrvbW8XoX9Zx777x/Ky8t12223acWKFVqwYEGnY9h/XetO/TrD/uvoxz/%2Bse688069/PLLHnvN1/uPgGVAUlKSPvvssw4h4Z133tHo0aMVHh7uMba6urr9%2B7a2NtXU1Cg5Odln673QnE/9nnrqKb3xxhsd2t577z2NGDHCJ2sNdOw/a9h/nXvzzTd1zz33aMOGDZoxY0aX49h/netu/dh/nvbv36%2Bf/exnamtra2%2Bz2Wyy2Wzq06dPh7G%2B3n8ELAPi4%2BPldDr12GOP6eTJk3rvvfdUUlKi%2BfPnS5KmTZumN998U5I0b948Pf/883r77bd1%2BvRpbdy4UaGhoZoyZYofj8C/zqd%2BDQ0NWrNmjT755BOdOXNGmzdv1ieffKJZs2b58xD8qra2VsePH1dzc7NOnz6t2tpa1dbWtvdfe%2B217L/vcT71Y/95am1t1cqVK3XXXXcpNTXVo5/99/3Op37sP09Op1OnTp3SunXr1NzcrIaGBhUUFCglJUX9%2BvXz7/nXDSOOHz/uvuWWW9zJycnuK6%2B80l1QUNDeFxcX5963b1/793/84x/dU6ZMcV966aXuBQsWuP/3f//XH0u%2BoHS3fl9//bX7kUcecf/0pz91Jycnu2%2B88Ub3O%2B%2B8469lXxBiY2PdcXFx7f998/032H/f73zqx/7zdPDgQXdcXJz70ksvdTudzg5fjx07xv47h/OpH/uvc4cPH3b/4he/cP/zP/%2Bz%2B4orrnDn5ua6a2tr3W63fx//bG43z84GAAAwiVuEAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGDY/wHWedKybDEamgAAAABJRU5ErkJggg%3D%3D\">\n",
" </div>\n",
" </div>\n",
" </div><div class=\"row variablerow\">\n",
" <div class=\"col-md-3 namecol\">\n",
" <p class=\"h4\">결산월<br/><small>Numeric</small></p>\n",
" </div>\n",
"\n",
" <div class=\"col-md-6\">\n",
" <div class=\"row\">\n",
" <div class=\"col-sm-6\">\n",
" <table class=\"stats \">\n",
" <tr><th>Distinct count</th>\n",
" <td>6</td></tr>\n",
" <tr><th>Unique (%)</th>\n",
" <td>0.3%</td></tr>\n",
" <tr class=\"ignore\"><th>Missing (%)</th>\n",
" <td>0.0%</td></tr>\n",
" <tr class=\"ignore\"><th>Missing (n)</th>\n",
" <td>0</td></tr>\n",
" </table>\n",
"\n",
" </div>\n",
" <div class=\"col-sm-6\">\n",
" <table class=\"stats \">\n",
"\n",
" <tr><th>Mean</th>\n",
" <td>11.846</td></tr>\n",
" <tr><th>Minimum</th>\n",
" <td>3</td></tr>\n",
" <tr><th>Maximum</th>\n",
" <td>12</td></tr>\n",
" <tr class=\"ignore\"><th>Zeros (%)</th>\n",
" <td>0.0%</td></tr>\n",
" </table>\n",
" </div>\n",
" </div>\n",
" </div>\n",
" <div class=\"col-md-3 collapse in\" id=\"minihistogram-4124153924871742031\">\n",
" <img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAMgAAABLCAYAAAA1fMjoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAA11JREFUeJzt3DFIa2cYh/H/MUJRb8uFkLQ4ZShdXOoocaguDsFNGhEidFApCApOLlXqoIOLIAiCFDqIcRMUHAWHblJQRMSh4h3uTayeirW95YTToVAoymssud%2BXA89vTD54X%2BQ80RNJgjiOYzlWqVRULpdVLBaVzWZdj0edoijSd9s/65df/3j2bC7dpu%2B//lKtra0N38Pn9RL4CATJEEWRRtd%2B0vnb3589%2B8VnHfrx254PEohPLb4XAJoZgQAGAgEMBAIYCAQwEAhgIBDAQCCAgUAAA4EABgIBDAQCGAgEMBAIYCAQwEAggIFAAAOBAAYCAQwEAhgIBDAQCGAgEMBAIICBQAADgQAGAgEMBAIYCAQwEAhgIBDAQCCAgUAAA4EABgIBDAQCGAgEMBAIYCAQwEAggIFAAAOBAAYCAQwEAhgIBDC0%2Bl6gHnEcv%2Bh8EAQNnV2r1eo%2Bn0qlGjoffiUikDfV3/Tn%2B7%2BePRdIev1xm16/amvY7Fqtph8OLvTu7v2zZz/95CN989XnSqVSDZvvU61WUy5d38%2By3nNJE8QvfXlugEqlonK5rGKxqGw263o8Esbn9eLlHqRarWp1dVXVatXHeCSMz%2BuFm3TAQCCAgUAAA4EABi%2BBZDIZTU5OKpPJ%2BBiPhPF5vXh5mxdICv7EAgwEAhgIBDAQCGAgEMBAIICBQACDl0DW19c1NDSk4eFhzc7OKooiH2ugid3d3Wl6elq9vb2PntvY2FB/f7%2BTPZwHcnR0pL29PW1vb2tra0v39/fa2dlxvQaa3MzMzJNxXFxc6PDw0NmnNp0H0t3drc3NTbW0/DM6nU7r5ubG9RpocisrK%2Brp6fnPY1EUaW5uTvPz8872cB5IEATq6OiQJF1eXurg4ECFQsH1Gmhy7e3tjx5bW1tToVBQLpd78fcU/F/ebtLPzs40NjamxcVFdXZ2%2BloDCXF8fKyTkxONjIw4neslkNPTU01NTWl5efnRr1HgKfv7%2B7q9vdXo6KhKpZKur681Pj7%2B4QfHjj08PMQDAwPx%2Bfm569FImKurqzifzz/5XF9fn5MdnH/tz%2B7ursIw1MLCguI4VhAEyufzmpiYcL0KmlQYhiqVSoqiSGEYanBwUF1dXVpaWvr3jKt3sfg8CGDgP%2BmAgUAAw9%2BSuvQyjOy4FQAAAABJRU5ErkJggg%3D%3D\">\n",
"\n",
" </div>\n",
" <div class=\"col-md-12 text-right\">\n",
" <a role=\"button\" data-toggle=\"collapse\" data-target=\"#descriptives-4124153924871742031,#minihistogram-4124153924871742031\" aria-expanded=\"false\" aria-controls=\"collapseExample\">\n",
" Toggle details\n",
" </a>\n",
" </div>\n",
" <div class=\"row collapse col-md-12\" id=\"descriptives-4124153924871742031\">\n",
" <div class=\"col-sm-4\">\n",
" <p class=\"h4\">Quantile statistics</p>\n",
" <table class=\"stats indent\">\n",
" <tr><th>Minimum</th>\n",
" <td>3</td></tr>\n",
" <tr><th>5-th percentile</th>\n",
" <td>12</td></tr>\n",
" <tr><th>Q1</th>\n",
" <td>12</td></tr>\n",
" <tr><th>Median</th>\n",
" <td>12</td></tr>\n",
" <tr><th>Q3</th>\n",
" <td>12</td></tr>\n",
" <tr><th>95-th percentile</th>\n",
" <td>12</td></tr>\n",
" <tr><th>Maximum</th>\n",
" <td>12</td></tr>\n",
" <tr><th>Range</th>\n",
" <td>9</td></tr>\n",
" <tr><th>Interquartile range</th>\n",
" <td>0</td></tr>\n",
" </table>\n",
" <p class=\"h4\">Descriptive statistics</p>\n",
" <table class=\"stats indent\">\n",
" <tr><th>Standard deviation</th>\n",
" <td>1.0883</td></tr>\n",
" <tr><th>Coef of variation</th>\n",
" <td>0.091875</td></tr>\n",
" <tr><th>Kurtosis</th>\n",
" <td>53.084</td></tr>\n",
" <tr><th>Mean</th>\n",
" <td>11.846</td></tr>\n",
" <tr><th>MAD</th>\n",
" <td>0.30198</td></tr>\n",
" <tr class=\"\"><th>Skewness</th>\n",
" <td>-7.2952</td></tr>\n",
" <tr><th>Sum</th>\n",
" <td>21796</td></tr>\n",
" <tr><th>Variance</th>\n",
" <td>1.1844</td></tr>\n",
" <tr><th>Memory size</th>\n",
" <td>14.5 KiB</td></tr>\n",
" </table>\n",
" </div>\n",
" <div class=\"col-sm-8 histogram\">\n",
" <img 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wIEDFR4erurqavXr10%2BSdPz4cbW0tCg%2BPv6q1s/IyFBycnKn8cjISJ092ySXKzB/OwpkNptVDkcI/esi%2BucZ%2BucZ8/UvMGs0T/8Ci81mveJx2ZtMGbBqa2vldrvV1NSklpYW1dbWSpKio6NVVlbW4TeQN954Q7t27dLq1asVGRmp2bNnKz8/X1OnTtXQoUO1fv16BQUFKSkpSVarVenp6SoqKlJcXJyCgoK0atUqTZ48%2BarPYEVFRV32lGNj4wW1tvIC6SqXq43%2BeYD%2BeYb%2BecY8/ev6LSLeZJ7%2BBZ4rHZe9yZQBKykpSRaLpf37N998UxaLRTU1NerTp0%2BHbcPDw2W329ubO378eOXl5Sk3N1cNDQ2Kj49XcXGx7Ha7JGn%2B/Pm6ePGipk%2BfLpfLpYkTJ2rp0qW%2B2zkAAGB6Frcnd3TjmnAGq2t69LCqd%2B9Q%2BtdF9M8z9M8z5uufW4%2B%2B%2BhcdP33B34VIkob0DdWmrESdO/epSfoXWD5//vkDb0sAAAAwGAELAADAYAQsAAAAgxGwAAAADEbAAgAAMBgBCwAAwGAELAAAAIMRsAAAAAxGwAIAADAYAQsAAMBgBCwAAACDEbAAAAAMRsACAAAwGAELAADAYAQsAAAAgxGwAAAADEbAAgAAMBgBCwAAwGAELAAAAIMRsAAAAAxGwAIAADAYAQsAAMBgBCwAAACDEbAAAAAMRsACAAAwmGkD1oEDB3TnnXcqPz%2B/09wf//hHzZo1S1//%2BteVnJystWvXdpgvKSnRlClTlJCQoMzMTFVWVrbPNTc3a8mSJUpKSlJiYqLmz5%2BvhoYGr%2B8PAADoPkwZsIqLi1VQUKDBgwd3mqutrdXcuXM1Y8YMHTlyRIWFhdqwYYN27NghSdqzZ4%2BKiopUUFCgw4cPKzk5WVlZWWpqapIkrVy5UseOHVNZWZl2794ti8WiRYsW%2BXT/AACAuZkyYPXq1UtbtmzRjTfe2Gmuvr5eaWlpysjIkNVq1bBhw3THHXforbfekiSVl5dr5syZio%2BPl91u15w5c2S1WrVv3z65XC5t3bpV8%2BbNU3R0tMLCwpSbm6v9%2B/ervr7e17sJAABMypQBKz09XcHBwZeci4uL0xNPPNFh7IMPPlDfvn0lSVVVVRo2bFiH%2BZiYGFVWVurEiRM6d%2B6cYmNj2%2BcGDRqk4OBgVVdXG7wXAACguzJlwLoWL7/8sk6ePKlZs2ZJkpxOpxwOR4dtwsPD5XQ65XQ6ZbFYFB4e3mHe4XCosbHRZzUDAABz6%2BHvArxp8%2BbNWrNmjYqLixUREXHVP%2Bd2u7u8Zl1d3SUvJ0ZGRio4OKzLj/tVZrNZO3zFtaF/nqF/njFf/9r8XcAlmad/gcVms17xuBwVFeW1tbttwPrZz36mrVu3atOmTYqJiWkfj4iI6HQ2yul0asiQIYqIiJDb7ZbT6VRISEj7/Mcff3zVAa20tFSFhYWdxrOzs5WTk9PFvYEkORwh/3wjXBb98wz984xZ%2Btfa2urvEi7JLP0LRBs3rvfLcblbBqwNGzbo9ddfV1lZWfu9V5%2BLi4tTdXW1UlNTJUltbW06evSo0tPTNXDgQIWHh6u6ulr9%2BvWTJB0/flwtLS2Kj4%2B/qrUzMjKUnJzcaTwyMlJnzzbJ5QrM344Cmc1mlcMRQv%2B6iP55hv55xnz9C8wazdO/wGKzWa94XPYmUwas2tpaud1uNTU1qaWlRbW1tZKk6Oho/eMf/9CaNWsuGa4kafbs2crPz9fUqVM1dOhQrV%2B/XkFBQUpKSpLValV6erqKiooUFxenoKAgrVq1SpMnT77qM1hRUVGXPeXY2HhBra28QLrK5Wqjfx6gf56hf54xT/%2B6fouIN5mnf4HnSsdlbzJlwEpKSpLFYmn//s0335TFYlFNTY127NihTz75RN/%2B9rfb591utwYMGKA33nhD48ePV15ennJzc9XQ0KD4%2BHgVFxfLbrdLkubPn6%2BLFy9q%2BvTpcrlcmjhxopYuXerzfQQAAOZlcXtyRzeuCWewuqZHD6t69w6lf11E/zxD/zxjvv659eirf9Hx0xf8XYgkaUjfUG3KStS5c5%2BapH%2BB5fPnnz/wtgQAAACDEbAAAAAMRsACAAAwGAELAADAYAQsAAAAgxGwAAAADEbAAgAAMBgBCwAAwGAELAAAAIMRsAAAAAxGwAIAADAYAQsAAMBgBCwAAACDEbAAAAAM5rOAde7cOV8tBQAA4Fc%2BC1jjxo3TwoULdeTIEV8tCQAA4Bc%2BC1hPP/20Ghoa9O///u/61re%2BpQ0bNqihocFXywMAAPiMzwJWamqqfvGLX%2BjAgQP6zne%2Bo//8z//UhAkT9MMf/lAVFRW%2BKgMAAMDrfH6Te0REhDIzM/XKK6%2BooKBAhw8f1oMPPqh77rlHu3fv9nU5AAAAhuvh6wUbGhq0detW/fa3v9X777%2Bv8ePHKyMjQ6dOndKTTz6pU6dO6YEHHvB1WQAAAIbxWcA6cOCAysvLtW/fPoWHh%2Bvf/u3flJGRoX79%2BrVvc8stt2jBggUELAAAYGo%2BC1g/%2BMEPNHbsWK1cuVLf%2BMY3ZLPZOm0zatQohYSE%2BKokAAAAr/BZwNq1a5e%2B9rWvqbm5uT1cnT9/Xj179mzf5rrrruM%2BLAAAYHo%2Bu8ndarXqnnvu0d69e9vHysrKdM899%2Bgf//iHr8oAAADwOp8FrB//%2BMcaOnSoRo0a1T6WkpKi2267Tc8///w1P96BAwd05513Kj8/v9PcoUOHlJaWplGjRmnatGnatm1bh/mSkhJNmTJFCQkJyszMVGVlZftcc3OzlixZoqSkJCUmJmr%2B/Pl8XhcAALgmPgtYf/rTn/Tcc88pMjKyfeyGG27QU089pbfffvuaHqu4uFgFBQUaPHhwp7m6ujplZ2frvvvuU0VFhRYvXqxly5apqqpKkrRnzx4VFRW1f0REcnKysrKy1NTUJElauXKljh07prKyMu3evVsWi0WLFi3yYM8BAMBXjc8CltvtVnNzc6fx8%2BfPy%2BVyXdNj9erVS1u2bNGNN97YaW7nzp0aNGiQZsyYIbvdrrFjx2rSpEkqLy%2BXJJWXl2vmzJmKj4%2BX3W7XnDlzZLVatW/fPrlcLm3dulXz5s1TdHS0wsLClJubq/3796u%2Bvr5rOw4AAL5yfBawxo8fr4ULF%2BrYsWM6f/68zp49q3feeUd5eXmaMGHCNT1Wenq6goODLzlXXV2t4cOHdxiLjY1tvwxYVVWlYcOGdZiPiYlRZWWlTpw4oXPnzik2NrZ9btCgQQoODlZ1dfU11QgAAL66fPYuwkWLFmnevHlKTU2VxWJpH09ISNDSpUsNW8fpdKpv374dxsLDw9XY2Ng%2B73A4Os07nU45nU5ZLBaFh4d3mHc4HO0/DwAA8M/4LGD16dNHr776qo4dO6b//d//lc1m00033aRbbrnF8LXcbrfffr6uru6SlxMjIyMVHBzmSVlfWTabtcNXXBv65xn65xnz9a/N3wVcknn6F1hsNusVj8tRUVFeW9vnfyonJiZGMTExXnv83r17y%2Bl0dhhzOp3q06ePpM/%2BFuKXz0Y5nU4NGTJEERERcrvdcjqdHT7w9OOPP1ZERMRVrV9aWqrCwsJO49nZ2crJybnW3cEXOBx8CK0n6J9n6J9nzNK/1tZWf5dwSWbpXyDauHG9X47LPgtYlZWVWr58uY4fP65PPvmk03xNTY0h68TFxWnr1q2d1h4xYkT7fHV1tVJTUyVJbW1tOnr0qNLT0zVw4ECFh4erurq6/U/4HD9%2BXC0tLYqPj7%2Bq9TMyMpScnNxpPDIyUmfPNsnlCszfjgKZzWaVwxFC/7qI/nmG/nnGfP0LzBrN07/AYrNZr3hc9iafBawlS5bo%2BuuvV05Ojq6//nqPHqu2tlZut1tNTU1qaWlRbW2tJCk6OlopKSkqLCxUeXm5UlJSVFFRoYMHD6qsrEySNHv2bOXn52vq1KkaOnSo1q9fr6CgICUlJclqtSo9PV1FRUWKi4tTUFCQVq1apcmTJ1/1GayoqKjLnnJsbLyg1lZeIF3lcrXRPw/QP8/QP8%2BYp3%2Be3WLiLebpX%2BC50nHZm3wWsN5//3394Q9/6PCncboqKSmpw43yb775piwWi2pqahQREaF169Zp%2BfLleuaZZzRgwAAVFBTo1ltvlfTZuxnz8vKUm5urhoYGxcfHq7i4WHa7XZI0f/58Xbx4UdOnT5fL5dLEiRMNvQkfAAB0fxa3p3eEX6V77rlHZWVlhgQss%2BIMVtf06GFV796h9K%2BL6J9n6J9nzNc/tx599S86fvqCvwuRJA3pG6pNWYk6d%2B5Tk/QvsHz%2B/PMHn70t4Yc//KGee%2B45nT9/3ldLAgAA%2BIXPLhEWFRXp5MmT2rZtm3r16iWrtWO2%2B8Mf/uCrUgAAALzKZwHrUnfwAwAAdEc%2BC1jZ2dm%2BWgoAAMCvfPrRsIcOHdKCBQv03e9%2BV9Jnn0H1%2Buuv%2B7IEAAAAr/NZwPrd736nhx9%2BWOfOndOf//xnSdLp06e1dOlSbdmyxVdlAAAAeJ3PAtZLL72klStXat26de2fYdW/f3%2BtXr1av/rVr3xVBgAAgNf5LGCdOHFCkydPlqQOHxI6ZswYnTx50ldlAAAAeJ3PAlbv3r3V0NDQafzvf/%2B7QkP98yFgAAAA3uCzgDV27FgtXrxY//M//yNJcjqd%2BsMf/qDc3FxNnDjRV2UAAAB4nc8C1sKFC9XU1KSpU6fq008/VWJior7//e9rwIABevzxx31VBgAAgNf57HOwevXqpZdfflnHjh3Te%2B%2B9p%2BDgYA0aNEiDBg3yVQkAAAA%2B4bOA9bmYmBjFxMT4elkAAACf8VnAiomJ6fDuwS%2BrqanxVSkAAABe5bOAtXTp0g4By%2BVy6e9//7sOHDigrKwsX5UBAADgdT4LWLNnz77k%2BB//%2BEeVlpZqxowZvioFAADAq3z6twgvZfTo0dq/f7%2B/ywAAADCM3wPW3r171aOHz%2B%2B1BwAA8BqfJZtx48Z1Gvvkk0904cKFy14%2BBAAAMCOfBayMjIxO7yIMCgrSzTffrOTkZF%2BVAQAA4HU%2BC1g5OTm%2BWgoAAMCvfBawCgsLr2o7i8WiefPmebkaAAAA7/FZwNq2bZvq6%2Bv16aefKjw8XG1tbTp37pyCg4PVs2fPDtsSsAAAgJn5LGDl5eVp3759evzxx9WnTx9J0unTp7VixQp985vf1L333uurUgAAALzKZx/T8POf/1xPPvlke7iSpL59%2B%2Bqpp57Sz372M1%2BVAQAA4HU%2BC1gffvihbDZbp/HrrrtOH330kaFr1dTU6P7779fo0aM1btw4LViwQI2NjZKkQ4cOKS0tTaNGjdK0adO0bdu2Dj9bUlKiKVOmKCEhQZmZmaqsrDS0NgAA0P35LGDdeuutWrhwoaqrq3X27FmdP39ex44d06JFizR06FDD1nG5XHr44Yc1YsQIHTp0SNu3b9eZM2f09NNPq66uTtnZ2brvvvtUUVGhxYsXa9myZaqqqpIk7dmzR0VFRSooKNDhw4eVnJysrKwsNTU1GVYfAADo/nwWsJ599lm99957%2Bva3v60xY8Zo9OjRSk1NVXV1tZYtW2bYOmfOnFF9fb1SU1Nlt9sVERGhu%2B%2B%2BWzU1Ndq5c6cGDRqkGTNmyG63a%2BzYsZo0aZLKy8slSeXl5Zo5c6bi4%2BNlt9s1Z84cWa1W7du3z7D6AABA9%2Bezm9yHDRumN954Q1VVVfrwww/ldrvVt29fxcXFyWo1LudFR0dr%2BPDhKi0t1aOPPqqmpib9/ve/14QJE1RdXa3hw4d32D42NlZvvPGGJKmqqqrTzfYxMTGqrKzUPffcY1iNAACge/P53yJ0OBzq2bOnJk%2BerNtuu83QcPW5F198UXv37tWoUaM0btw4ud1u5efny%2Bl0yuFwdNg2PDy8/f6sy807nU7DawQAAN2Xz85gNTQ06JFHHtGf//xn9ejRQ1VVVaqvr9cDDzygX/ziF%2BrXr58h6zQ3NysrK0vf%2Bta39IMf/EAXL17U008/rccee0yS5Ha7DVnncurq6lRfX99pPDIyUsHBYV5du7uy2awdvuLa0D/P0D/PmK9/bf4u4JLM07/AYrNZr3hcjoqK8traPgtYK1asUHBwsLZs2aLMzExJUlhYmIYNG6YXXnhBL774oiHrVFRU6NSpU8rLy5MkhYaGKjs7W6mpqbrrrrs6nY1yOp3tHx0RERHRfjbri/NDhgy56vVLS0sv%2Ban12dnZ/LkgDzkcIf5QdkdpAAAZnUlEQVQuwdTon2fon2fM0r/W1lZ/l3BJZulfINq4cb1fjss%2BC1gHDhzQa6%2B9pujo6PY/%2BhwcHKwnnnhC3/zmNw1bp62trf1/n19%2BbG1tlcVi0R133KHf/va3HbavrKzUiBEjJElxcXGqrq5Wampq%2B2MdPXpUaWlpV71%2BRkbGJf94dWRkpM6ebZLLFZi/HQUym80qhyOE/nUR/fMM/fOM%2BfoXmDWap3%2BBxWazXvG47E0%2BC1gtLS2XPBUXHByslpYWw9YZOXKkrr/%2Beq1evVpz585VU1OTXnrpJSUkJCglJUWFhYUqLy9XSkqKKioqdPDgQZWVlUmSZs%2Berfz8fE2dOlVDhw7V%2BvXrFRQUpAkTJlz1%2BlFRUZc95djYeEGtrbxAusrlaqN/HqB/nqF/njFP/7x7G0lXmad/gedKx2Vv8tlF3Ztvvlm7d%2B/uNF5aWqrBgwcbtk6vXr30y1/%2BUu%2B8846SkpI0bdo02e12rVq1ShEREVq3bp02b96shIQErVixQgUFBbr11lslSePHj1deXp5yc3M1ZswY/dd//ZeKi4tlt9sNqw8AAHR/PjuD9eCDD2rBggV6/fXX5XK59Oyzz6q6ulp//etfDbv/6nPDhg3Tpk2bLjmXkJDQ6dPbv2jWrFmaNWuWofUAAICvFp%2BdwZoyZYpeeukluVwu/cu//Iv%2B9Kc/acCAAXr11Vc1efJkX5UBAADgdT45g9XW1qaqqiolJiYqMTHRF0sCAAD4jU/OYFmtVt1///1yuVy%2BWA4AAMCvfHaJcPr06SopKfH6B30CAAD4m89ucj99%2BrT27NmjX/ziF%2Brfv3%2Bnd%2Ba9%2BuqrvioFAADAq3wWsHr37q3x48f7ajkAAAC/8XrAmj9/vlavXq3nn3%2B%2BfWz16tWaP3%2B%2Bt5cGAADwC6/fg7V///5OY7/85S%2B9vSwAAIDf%2BOXPc3OjOwAA6M78ErA%2B/2PPAAAA3ZFfAhYAAEB3RsACAAAwmNffRdjS0qL8/Px/OvbTn/7U26UAAAD4hNcD1qhRo1RXV/dPxwAAALoLrwesl19%2B2dtLAAAABBTuwQIAADAYAQsAAMBgBCwAAACDEbAAAAAMRsACAAAwGAELAADAYAQsAAAAgxGwAAAADEbAAgAAMFi3DVhFRUUaN26cRo4cqQcffFAnT56UJB06dEhpaWkaNWqUpk2bpm3btnX4uZKSEk2ZMkUJCQnKzMxUZWWlP8oHAAAm1i0D1q9//Wtt375dmzdv1sGDBzV48GCVlJSorq5O2dnZuu%2B%2B%2B1RRUaHFixdr2bJlqqqqkiTt2bNHRUVFKigo0OHDh5WcnKysrCw1NTX5eY8AAICZdMuAtWHDBuXl5emmm25Sz5499eSTT%2BrJJ5/Uzp07NWjQIM2YMUN2u11jx47VpEmTVF5eLkkqLy/XzJkzFR8fL7vdrjlz5shqtWrfvn1%2B3iMAAGAm3S5g1dbW6uTJkzp79qzuvfdejRkzRrm5uWpsbFR1dbWGDx/eYfvY2Nj2y4BVVVUaNmxYh/mYmBguEwIAgGvSLQOWJO3atUsbN27U9u3bdfr0aT311FNyOp1yOBwdtg8PD1djY6MkXXbe6XT6pngAANAt9PB3AUZzu92SpIceekg33HCDJCknJ0cPPfSQRo8e3T7vLXV1daqvr%2B80HhkZqeDgMK%2Bu3V3ZbNYOX3Ft6J9n6J9nzNe/Nn8XcEnm6V9gsdmsVzwuR0VFeW3tbhewPg9VYWH/F2b69%2B%2BvtrbPXjRfPhvldDrVp08fSVJERET72awvzg8ZMuSq1y8tLVVhYWGn8ezsbOXk5Fz146AzhyPE3yWYGv3zDP3zjFn619ra6u8SLsks/QtEGzeu98txudsFrL59%2ByosLEw1NTWKjY2VJJ08eVLXXXedkpKSOn0sQ2VlpUaMGCFJiouLU3V1tVJTUyVJbW1tOnr0qNLS0q56/YyMDCUnJ3caj4yM1NmzTXK5AvO3o0Bms1nlcITQvy6if56hf54xX/8Cs0bz9C%2Bw2GzWKx6XvanbBSybzab09HStW7dOCQkJCg0N1dq1azV9%2BnSlpqZq7dq1Ki8vV0pKiioqKnTw4EGVlZVJkmbPnq38/HxNnTpVQ4cO1fr16xUUFKQJEyZc9fpRUVGXPeXY2HhBra28QLrK5Wqjfx6gf56hf54xT/%2B8extJV5mnf4HnSsdlb%2Bp2AUuScnNz9cknnygtLU2tra26%2B%2B67tXjxYoWEhGjdunVavny5nnnmGQ0YMEAFBQW69dZbJUnjx49XXl6ecnNz1dDQoPj4eBUXF8tut/t5jwAAgJlY3N6%2B6xvtOIPVNT16WNW7dyj96yL65xn65xnz9c%2BtR1/9i46fvuDvQiRJQ/qGalNWos6d%2B9Qk/Qssnz///IG3JQAAABiMgAUAAGAwAhYAAIDBCFgAAAAGI2ABAAAYjIAFAABgMAIWAACAwQhYAAAABiNgAQAAGIyABQAAYDACFgAAgMEIWAAAAAYjYAEAABiMgAUAAGAwAhYAAIDBCFgAAAAGI2ABAAAYjIAFAABgMAIWAACAwQhYAAAABiNgAQAAGIyABQAAYDACFgAAgMEIWAAAAAYjYAEAABis2wes5557TjExMe3fHzp0SGlpaRo1apSmTZumbdu2ddi%2BpKREU6ZMUUJCgjIzM1VZWenrkgEAgMl164BVU1Oj1157TRaLRZJUW1ur7Oxs3XfffaqoqNDixYu1bNkyVVVVSZL27NmjoqIiFRQU6PDhw0pOTlZWVpaampr8uRsAAMBkum3AcrvdWrZsmR588MH2sZ07d2rQoEGaMWOG7Ha7xo4dq0mTJqm8vFySVF5erpkzZyo%2BPl52u11z5syR1WrVvn37/LUbAADAhLptwPrNb36jkJAQTZ06tX3s6NGjGj58eIftYmNj2y8DVlVVadiwYR3mY2JiuEwIAACuSQ9/F%2BANZ86c0dq1a/XrX/%2B6w7jT6VTfvn07jIWHh6uxsbF93uFwdJp3Op3eLRgAAHQr3TJgrVixQrNmzdLXvvY1nTp1qsOc2%2B326tp1dXWqr6/vNB4ZGang4DCvrt1d2WzWDl9xbeifZ%2BifZ8zXvzZ/F3BJ5ulfYLHZrFc8LkdFRXlt7W4XsCoqKlRVVaXnnntOUsdA1bt3705no5xOp/r06SNJioiIaD%2Bb9cX5IUOGXPX6paWlKiws7DSenZ2tnJycq34cdOZwhPi7BFOjf56hf54xS/9aW1v9XcIlmaV/gWjjxvV%2BOS53u4C1fft21dbW6q677pL0WcByu91KTEzUAw88oJ07d3bYvrKyUiNGjJAkxcXFqbq6WqmpqZKktrY2HT16VGlpaVe9fkZGhpKTkzuNR0ZG6uzZJrlcgfnbUSCz2axyOELoXxfRP8/QP8%2BYr3%2BBWaN5%2BhdYbDbrFY/L3tTtAtaiRYuUm5vb/v3p06eVkZGh1157TS6XS8XFxSovL1dKSooqKip08OBBlZWVSZJmz56t/Px8TZ06VUOHDtX69esVFBSkCRMmXPX6UVFRlz3l2Nh4Qa2tvEC6yuVqo38eoH%2BeoX%2BeMU//vHsbSVeZp3%2BB50rHZW/qdgErLCxMYWH/d69Ta2urLBZLe3PXrVun5cuX65lnntGAAQNUUFCgW2%2B9VZI0fvx45eXlKTc3Vw0NDYqPj1dxcbHsdrtf9gUAAJhTtwtYXzZgwADV1NS0f5%2BQkNDp09u/aNasWZo1a5YvSgMAAN0Ub0sAAAAwGAELAADAYAQsAAAAgxGwAAAADEbAAgAAMBgBCwAAwGAELAAAAIMRsAAAAAxGwAIAADAYAQsAAMBgBCwAAACDEbAAAAAMRsACAAAwGAELAADAYAQsAAAAgxGwAAAADEbAAgAAMBgBCwAAwGAELAAAAIMRsAAAAAxGwAIAADAYAQsAAMBgBCwAAACDEbAAAAAM1i0D1gcffKDs7GyNGTNGd9xxhxYuXKjz589Lkg4dOqS0tDSNGjVK06ZN07Zt2zr8bElJiaZMmaKEhARlZmaqsrLSH7sAAABMrFsGrEceeUQOh0P79%2B/Xjh079N577%2BmFF15QXV2dsrOzdd9996miokKLFy/WsmXLVFVVJUnas2ePioqKVFBQoMOHDys5OVlZWVlqamry8x4BAAAz6XYB6/z58xo%2BfLgee%2BwxBQcHq0%2BfPkpNTdVbb72lnTt3atCgQZoxY4bsdrvGjh2rSZMmqby8XJJUXl6umTNnKj4%2BXna7XXPmzJHVatW%2Bffv8vFcAAMBMul3A6tmzp3784x8rIiKifeyDDz5QdHS0qqurNXz48A7bx8bGtl8GrKqq0rBhwzrMx8TEcJkQAABck24XsL6ssrJSr7zyiubOnSun0ymHw9FhPjw8XI2NjZJ02Xmn0%2BmzegEAgPn18HcB3vT222/rkUce0WOPPabExEStX79ebrfbq2vW1dWpvr6%2B03hkZKSCg8O8unZ3ZbNZO3zFtaF/nqF/njFf/9r8XcAlmad/gcVms17xuBwVFeW1tbttwNq7d69%2B9KMf6amnnlJKSookqXfv3p3ORjmdTvXp00eSFBER0X4264vzQ4YMuep1S0tLVVhY2Gk8OztbOTk517ob%2BAKHI8TfJZga/fMM/fOMWfrX2trq7xIuySz9C0QbN673y3G5Wwasd955R0888YTWrFmjxMTE9vG4uDht3bq1w7aVlZUaMWJE%2B3x1dbVSU1MlSW1tbTp69KjS0tKueu2MjAwlJyd3Go%2BMjNTZs01yuQLzt6NAZrNZ5XCE0L8uon%2BeoX%2BeMV//ArNG8/QvsNhs1isel72p2wUsl8ulJUuWtF8W/KKUlBQVFhaqvLxcKSkpqqio0MGDB1VWViZJmj17tvLz8zV16lQNHTpU69evV1BQkCZMmHDV60dFRV32lGNj4wW1tvIC6SqXq43%2BeYD%2BeYb%2BecY8/fPubSRdZZ7%2BBZ4rHZe9qdsFrD/96U967733tHz5cj377LOyWCxyu92yWCzatWuX1q1bp%2BXLl%2BuZZ57RgAEDVFBQoFtvvVWSNH78eOXl5Sk3N1cNDQ2Kj49XcXGx7Ha7n/cKAACYSbcLWAkJCaqpqbnsfL9%2B/Tp9evsXzZo1S7NmzfJGaQAA4CuCtyUAAAAYjIAFAABgMAIWAACAwQhYAAAABiNgAQAAGIyABQAAYDACFgAAgMEIWAAAAAYjYAEAABiMgAUAAGAwAhYAAIDBCFgAAAAGI2ABAAAYjIAFAABgMAIWAACAwQhYAAAABiNgAQAAGIyABQAAYDACFgAAgMEIWAAAAAYjYAEAABiMgAUAAGAwAhYAAIDBCFgAAAAGI2ABAAAYjID1JSdPntTDDz%2BsMWPGKDk5WT/5yU/kdrv9XRYAADARAtaXzJ8/X/369dPevXu1ceNG7d27VyUlJf4uCwAAmEgPfxcQSCorK3X8%2BHFt2rRJoaGhCg0N1QMPPKCNGzfqgQce8Hd5XuTWgeN1%2Bripxd%2BFSJLCQ67TXUOiJFn8XQoAAF1CwPqCo0ePasCAAerZs2f7WGxsrP7%2B97/r4sWLuv766/1YnXf9xzsf6vjpC/4uQ5I0pG/o/w9YuLRrvWTdptbWVkltXfjZa0EgBoDPEbC%2BwOl0yuFwdBjr1auXJKmxsbFbByyYy8/%2B87je/6jJ32VIkm7qE6IffnOIv8sIYIF6DyeBGPAmAtaXeHpDe11dnerr6zuNR0ZGKjg4zKPH9p423dQnxN9FtLupT4h6fOGZabVKra2tslrVYRyBozv/dzHi%2BVf6x3%2Bo9uynxhbWRdGOIGXcPtBn65nx9Rto/x5Kks3GLdNdYbNZr3hcjory3tUSkzzdfSMiIkJOp7PDmNPplMViUURExFU9RmlpqQoLCzuNjx49WqtWrfLqf0xPPHdfgr9LuKy6ujpt2vQrZWRkBGz/fO1a/nvV1dWptLSU/nWREc%2B/uXcPN7gq8zDj6zeQ/j2sq6tTUVGRqfoXSOrq6pSXl6e33nqr01x2drZycnK8tjYB6wvi4uL04Ycfyul0tl8a/Otf/6qbb75ZISFX9xtNRkaGkpOTO4z97W9/04IFC1RfX88LpAvq6%2BtVWFio5ORk%2BtcF9M8z9M8z9M8z9M8z9fX1euutt1RQUKCbb765w1xkZKRX1yZgfUFsbKzi4%2BP105/%2BVAsXLlRtba1KSko0Z86cq36MqKgoXgQAAASQm2%2B%2BWcOH%2B/ZMMhd1v%2BTnP/%2B5amtrNW7cON1///2aMWOGZs%2Be7e%2ByAACAiXAG60uio6NVXFzs7zIAAICJcQYLAADAYLZly5Yt83cRXwWhoaG6/fbbFRoa6u9STIn%2BeYb%2BeYb%2BeYb%2BeYb%2BecZf/bO4%2BUvGAAAAhuISIQAAgMEIWAAAAAYjYAEAABiMgAUAAGAwAhYAAIDBCFgAAAAGI2ABAAAYjIAFAABgMAKWl33wwQfKzs7WmDFjdMcdd2jhwoU6f/68v8syneeee04xMTH%2BLsOUioqKNG7cOI0cOVIPPvigTp486e%2BSTKOmpkb333%2B/Ro8erXHjxmnBggVqaGjwd1kB68CBA7rzzjuVn5/fae7QoUNKS0vTqFGjNG3aNG3bts0PFQa2K/Xvj3/8o2bNmqWvf/3rSk5O1tq1a/1QYWC7Uv8%2B53a7NXPmTH3ve9/zej0ELC975JFH5HA4tH//fu3YsUPvvfeeXnjhBX%2BXZSo1NTV67bXXZLFY/F2K6fz617/W9u3btXnzZh08eFCDBw9WSUmJv8syBZfLpYcfflgjRozQoUOHtH37dp05c0bPPPOMv0sLSMXFxSooKNDgwYM7zdXV1Sk7O1v33XefKioqtHjxYi1btkxVVVV%2BqDQwXal/tbW1mjt3rmbMmKEjR46osLBQGzZs0I4dO/xQaWC6Uv%2B%2BaPPmzTpx4oRPaiJgedH58%2Bc1fPhwPfbYYwoODlafPn2Umpqqt956y9%2BlmYbb7dayZcv04IMP%2BrsUU9qwYYPy8vJ00003qWfPnnryySf15JNP%2BrssUzhz5ozq6%2BuVmpoqu92uiIgI3X333aqpqfF3aQGpV69e2rJli2688cZOczt37tSgQYM0Y8YM2e12jR07VpMmTVJ5ebkfKg1MV%2BpffX290tLSlJGRIavVqmHDhumOO%2B7gWPIFV%2Brf5%2Brq6rRu3TqfnL2SCFhe1bNnT/34xz9WRERE%2B9ipU6cUHR3tx6rM5Te/%2BY1CQkI0depUf5diOrW1tTp58qTOnj2re%2B%2B9V2PHjtWjjz6qxsZGf5dmCtHR0Ro%2BfLhKS0t18eJFffTRR/r973%2BviRMn%2Bru0gJSenq7g4OBLzlVXV2v48OEdxmJjY1VZWemL0kzhSv2Li4vTE0880WHsgw8%2B4FjyBVfq3%2Beef/55ZWZmauDAgT6piYDlQ5WVlXrllVeUlZXl71JM4cyZM1q7dq2efvppf5diSrW1tZKkXbt2aePGjXrttddUW1urpUuX%2Brky83jxxRe1d%2B9ejRo1SuPGjZPb7VZeXp6/yzIdp9Mph8PRYSw8PJyw30Uvv/yyTp48qdmzZ/u7FNM4ePCg3n33XT300EM%2BW5OA5SNvv/22vv/972vBggUaO3asv8sxhRUrVmjWrFn62te%2B5u9STMntdkuSHnroId1www2Kjo5WTk6O3nzzTbW0tPi5usDX3NysrKwsfetb39KRI0d04MABhYaG6rHHHvN3aab0%2BfMRntm8ebPWrFmjoqKiDldHcHnNzc1avny5li1bpuuuu85n6xKwfGDv3r36wQ9%2BoMWLFyszM9Pf5ZhCRUWFqqqq9PDDD0viH%2BeuuOGGGyRJYWFh7WP9%2B/dXW1ubPvroI3%2BVZRoVFRU6deqU8vLyFBoaqsjISOXk5Gj37t06e/asv8szld69e8vpdHYYczqd6tOnj58qMqef/exnKi4u1qZNm/Sv//qv/i7HNNauXauRI0fq9ttvl%2BS740kPn6zyFfbOO%2B/oiSee0Jo1a5SYmOjvckxj%2B/btqq2t1V133SXpsxeE2%2B1WYmKilixZonvuucfPFQa%2Bvn37KiwsTDU1NYqNjZUknTx5Uj169FBUVJSfqwt8bW1t7f%2BzWj/7XbS1tZV3s3ZBXFyctm7d2mGssrJSI0aM8FNF5rNhwwa9/vrrKisrU9%2B%2Bff1djqns2LFDZ8%2Bebb961NzcrObmZiUmJmrbtm1eu5eNgOVFLpdLS5Ys0WOPPUa4ukaLFi1Sbm5u%2B/enT59WRkaGXnvtNYWHh/uxMvOw2WxKT0/XunXrlJCQoNDQUK1du1bTp09vDwy4vJEjR%2Br666/X6tWrNXfuXDU1Nemll15SQkJCp/uJ8Nk9f263W01NTWppaWm/BzA6OlopKSkqLCxUeXm5UlJSVFFRoYMHD6qsrMzPVQeOK/XvH//4h9asWUO4uoIr9a%2BsrEytra3t277xxhvatWuXVq9ercjISK/VZHFz7cVrjhw5ou9%2B97uy2%2B1yu92yWCztX3ft2qV%2B/fr5u0TTOHXqlL7xjW/wFvlr1NLSohUrVmjnzp1qbW3V3XffrSVLligkJMTfpZnC0aNHtWLFCr377ru67rrrNGbMGD3%2B%2BONe/UfZrGJiYjqc3fv837rPX7NHjhzR8uXL9d5772nAgAHKz8/XN77xDX%2BVG3Cu1L%2B1a9eqsLCww/1DbrdbAwYM0BtvvOGPcgPOP3v%2BfdHWrVu1detWbdq0yas1EbAAAAAMxnUCAAAAgxGwAAAADEbAAgAAMBgBCwAAwGAELAAAAIMRsAAAAAxGwAIAADAYAQsAAMBgBCwAAACDEbAAAAAMRsACAAAwGAELAADAYAQsAAAAg/0/CzzOwhIxTx4AAAAASUVORK5CYII%3D\">\n",
" </div>\n",
" </div>\n",
" </div><div class=\"row variablerow\">\n",
" <div class=\"col-md-3 namecol\">\n",
" <p class=\"h4\">시가총액<br/><small>Numeric</small></p>\n",
" </div>\n",
"\n",
" <div class=\"col-md-6\">\n",
" <div class=\"row\">\n",
" <div class=\"col-sm-6\">\n",
" <table class=\"stats \">\n",
" <tr><th>Distinct count</th>\n",
" <td>1837</td></tr>\n",
" <tr><th>Unique (%)</th>\n",
" <td>99.8%</td></tr>\n",
" <tr class=\"ignore\"><th>Missing (%)</th>\n",
" <td>0.0%</td></tr>\n",
" <tr class=\"ignore\"><th>Missing (n)</th>\n",
" <td>0</td></tr>\n",
" </table>\n",
"\n",
" </div>\n",
" <div class=\"col-sm-6\">\n",
" <table class=\"stats \">\n",
"\n",
" <tr><th>Mean</th>\n",
" <td>774270000000</td></tr>\n",
" <tr><th>Minimum</th>\n",
" <td>4904031480</td></tr>\n",
" <tr><th>Maximum</th>\n",
" <td>190330970144000</td></tr>\n",
" <tr class=\"ignore\"><th>Zeros (%)</th>\n",
" <td>0.0%</td></tr>\n",
" </table>\n",
" </div>\n",
" </div>\n",
" </div>\n",
" <div class=\"col-md-3 collapse in\" id=\"minihistogram-1942515306978973641\">\n",
" <img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAMgAAABLCAYAAAA1fMjoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAA8RJREFUeJzt2zFIa2cYh/H/MTGVCkUwSYNOUju4KC6CEfQKgqDiJGRxKeIWEEQFwS2Ii6OTi4sIgq0IoYOCqIMttCIuYrEdhELjkRpbuJVw1a9DIVQu9%2BU6HE%2B8Pr8tvB7OJ7yP5KjxnHNOz8z3fa2vryuTySiZTD737fHChLkvXhiBAC9FVdgHACoZgQCGaFg3/u6HX/Xn23dPuuZNS1JfN9YHdCLgfaEF8u3Pf%2BiXwtsnXdPxFXHgefEWCzAQCGAgEMBAIICBQAADgQAGAgEMBAIYCAQwEAhgIBDAQCCAgUAAA4EABgIBDAQCGAgEMBAIYCAQwEAggIFAAAOBAAYCAQwEAhgIBDAQCGAgEMBAIICBQAADgQAGAgEMBAIYCAQwEAhgIBDAQCCAgUAAA4EABgIBDAQCGAgEMETDPsDHilZ5ikU83d3dPfnaSCQiz/MCOBU%2BdZ5zzoVx480ff1Pxn3cf/fV1n0fl/1XS5d%2BlJ93nyy8%2B0zdvmhWJRJ56RDyzaLTyfl6HciLf9/X7T98rk8komUyGcQS8IL7va319PZR9CeUZ5OrqSktLS7q6ugrj9nhhwtwXHtIBA4EABgIBDAQCGEIJJJFIKJvNKpFIhHF7vDBh7ktofwcBXgLeYgEGAgEMBAIYCAQwEAhgIBDAQCCAIfB/d19eXtb29rai0aja2to0Ozv7aL65uanV1VXV1NSosbFR8/Pzqq6uDvpYqFD/35empiblcrlHnxN59n1xATo5OXFDQ0OuVCq5h4cHNzY25nZ2dsrzQqHgenp6XLFYdM45Nzc351ZWVoI8EirY0dGRGx4edvf3984557LZrNvY2CjPw9iXQN9iHRwcqK%2BvT7FYTJ7nqb%2B/X3t7e%2BX54eGhOjo6VFdXJ0kaGBjQ/v5%2BkEdCBWtvb9fa2pqqqv5by/r6el1fX5fnYexLoIH4vq94PF5%2BnUgkVCgUPjiPx%2BOP5nhdPM9TbW2tJOni4kJ7e3saHBwsz8PYFx7SUXHOzs40Pj6uhYUFNTQ0hHqWQANJpVLyfb/82vf9R99wKpXS5eXlB%2Bd4fU5PTzUxMaHFxUV1dnY%2BmoWxL4EG0tvbq93dXZVKJd3d3Smfz6uvr6887%2Brq0vHxsYrFoiRpa2vr0Ryvy%2B3trSYnJ7W0tKTW1tb35mHsS6C/5m1padHIyIhGR0cViUSUTqfV3d2tyclJzczMKJVKaWpqSuPj44rFYmpublYmkwnySKhg%2BXxeNzc3yuVycs7J8zyl02mdn59reno6lH3h8yCAgYd0wEAggOFfwEsIaUFQDzgAAAAASUVORK5CYII%3D\">\n",
"\n",
" </div>\n",
" <div class=\"col-md-12 text-right\">\n",
" <a role=\"button\" data-toggle=\"collapse\" data-target=\"#descriptives-1942515306978973641,#minihistogram-1942515306978973641\" aria-expanded=\"false\" aria-controls=\"collapseExample\">\n",
" Toggle details\n",
" </a>\n",
" </div>\n",
" <div class=\"row collapse col-md-12\" id=\"descriptives-1942515306978973641\">\n",
" <div class=\"col-sm-4\">\n",
" <p class=\"h4\">Quantile statistics</p>\n",
" <table class=\"stats indent\">\n",
" <tr><th>Minimum</th>\n",
" <td>4904031480</td></tr>\n",
" <tr><th>5-th percentile</th>\n",
" <td>32694000000</td></tr>\n",
" <tr><th>Q1</th>\n",
" <td>63074000000</td></tr>\n",
" <tr><th>Median</th>\n",
" <td>126280000000</td></tr>\n",
" <tr><th>Q3</th>\n",
" <td>304390000000</td></tr>\n",
" <tr><th>95-th percentile</th>\n",
" <td>2449700000000</td></tr>\n",
" <tr><th>Maximum</th>\n",
" <td>190330970144000</td></tr>\n",
" <tr><th>Range</th>\n",
" <td>190326066112520</td></tr>\n",
" <tr><th>Interquartile range</th>\n",
" <td>241310000000</td></tr>\n",
" </table>\n",
" <p class=\"h4\">Descriptive statistics</p>\n",
" <table class=\"stats indent\">\n",
" <tr><th>Standard deviation</th>\n",
" <td>5068500000000</td></tr>\n",
" <tr><th>Coef of variation</th>\n",
" <td>6.5461</td></tr>\n",
" <tr><th>Kurtosis</th>\n",
" <td>1069.1</td></tr>\n",
" <tr><th>Mean</th>\n",
" <td>774270000000</td></tr>\n",
" <tr><th>MAD</th>\n",
" <td>1075500000000</td></tr>\n",
" <tr class=\"alert\"><th>Skewness</th>\n",
" <td>29.414</td></tr>\n",
" <tr><th>Sum</th>\n",
" <td>1424649135640568</td></tr>\n",
" <tr><th>Variance</th>\n",
" <td>2.5689e+25</td></tr>\n",
" <tr><th>Memory size</th>\n",
" <td>14.5 KiB</td></tr>\n",
" </table>\n",
" </div>\n",
" <div class=\"col-sm-8 histogram\">\n",
" <img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAlgAAAGQCAYAAAByNR6YAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAIABJREFUeJzt3X90VOWB//HPTGJ%2BAE7I0CRCwBp%2BJiQx37NgISobCcpBizFgTYjU7Vq6WiDkYAKLgvzYiisapFWyRtO0jbXrCskKXdFW%2BSUI0lMsriQB5CxgEYEkp8kIgVjiZL5/9HSOMUETeeYOc3m//qHeezPPMz69zNt7bxKHz%2BfzCQAAAMY4gz0BAAAAuyGwAAAADCOwAAAADCOwAAAADCOwAAAADCOwAAAADCOwAAAADCOwAAAADCOwAAAADCOwAAAADCOwAAAADCOwAAAADCOwAAAADCOwAAAADCOwAAAADCOwAAAADCOwAAAADCOwAAAADCOwAAAADCOwAAAADCOwAAAADCOwAAAADCOwAAAADCOwAAAADCOwAAAADCOwAAAADCOwAAAADCOwAAAADCOwAAAADCOwAAAADCOwAAAADCOwAAAADCOwAAAADAvJwDp58qQKCws1btw43XjjjVq0aJFaW1slSbt379Y999yjMWPG6M4779TGjRs7fW1VVZWmTJmisWPHaubMmaqtrfXvu3DhgpYuXaqsrCxlZmaqqKhIzc3NlzzfxsZGrV27Vo2NjZf8Wrj8sL72xvraG%2Btrb8Fc35AMrDlz5sjlcmnHjh167bXXdPToUT355JNqbGxUYWGh7r33Xu3Zs0dLlizRihUrVFdXJ0nasmWLysvLVVpaqnfffVfZ2dmaPXu22traJEmrV6/WoUOHtH79er311ltyOBxavHjxJc%2B3qalJZWVlampquuTXwuWH9bU31tfeWF97C%2Bb6hlxgtba2KjU1VQsWLFBUVJQGDBig3Nxc7d27V5s2bVJSUpKmTZumiIgIjR8/XpMmTVJNTY0kqaamRtOnT1d6eroiIiI0a9YsOZ1Obd%2B%2BXV6vVxs2bNDcuXOVkJCgq6%2B%2BWvPnz9eOHTs48QAAQK%2BEXGD169dPjz/%2BuNxut3/byZMnlZCQoPr6eqWmpnY6PiUlxX8bsK6uTqNHj%2B60Pzk5WbW1tTp%2B/LjOnj2rlJQU/76kpCRFRUWpvr4%2BgO8IAADYTcgF1pfV1tbq5Zdf1o9//GN5PB65XK5O%2B2NiYtTS0iJJF93v8Xjk8XjkcDgUExPTab/L5fJ/PQAAQE%2BEB3sCl%2BJPf/qT5syZowULFigzM1OVlZXy%2BXyX9JqX%2BvWNjY1dbikeOXLkkl4TAAB8c919DsfFxSk%2BPj5gY4ZsYG3btk3/%2Bq//qmXLliknJ0eSFBsbK4/H0%2Bk4j8ejAQMGSJLcbneXq1Eej0cjR46U2%2B2Wz%2BeTx%2BNRdHS0f/%2Bnn37a6Xbk11m3bp3Kysq6bJ87d26X25ewh9TUVH344YfBngYChPW1N9bX3lJTU3XDDTdo4cKFXfYVFhZq3rx5ARs7JANr3759euSRR7R27VplZmb6t6elpWnDhg2djq2trVVGRoZ/f319vXJzcyVJHR0dOnDggPLy8jRkyBDFxMSovr5eAwcOlCQdPnxY7e3tSk9P7/Hc8vPzlZ2d3WV7XFyczpxpk9fb0ev3i8tbWJhTLlc062tTrK%2B9sb72Fhbm1Jo1a7r9ZrW4uLiAjh1ygeX1erV06VL/bcEvysnJUVlZmWpqapSTk6M9e/bonXfe0fr16yVJBQUFKikp0dSpUzVq1ChVVlYqMjJSWVlZcjqdysvLU3l5udLS0hQZGak1a9Zo8uTJvbqCFR8ff9FLji0t5/T555zAduX1drC%2BNsb62hvra19f9bkcSA7fpT50ZLH33ntP9913nyIiIuTz%2BeRwOPx//v73v9cnn3yilStX6ujRo0pMTFRJSYluvfVW/9e/8soreuGFF9Tc3Kz09HStWLFCw4cPlyS1t7dr1apV2rRpk7xeryZOnKjly5erX79%2BRuZOYNlTeLhTsbF9WV%2BbYn3tjfW1t7%2BvbzCEXGCFMk5ge%2BIvaHtjfe2N9bW3YAZWyP%2BYBgAAgMsNgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGBYeLAncKWo%2B6hJbZ%2B1y%2BcL9kx6LjoiTNe4%2BgR7GgAAhBwCyyJPvf6hDpxsDfY0eqXo1iTdnkZgAQDQW9wiBAAAMIzAAgAAMIzAAgAAMIzAAgAAMIzAAgAAMIzAAgAAMIzAAgAAMIzAAgAAMIzAAgAAMIzAAgAAMIzAAgAAMIzAAgAAMIzAAgAAMIzAAgAAMIzAAgAAMIzAAgAAMIzAAgAAMIzAAgAAMIzAAgAAMIzAAgAAMIzAAgAAMIzAAgAAMIzAAgAAMIzAAgAAMIzAAgAAMIzAAgAAMIzAAgAAMIzAAgAAMIzAAgAAMIzAAgAAMIzAAgAAMIzAAgAAMIzAAgAAMIzAAgAAMIzAAgAAMIzAAgAAMIzAAgAAMIzAAgAAMIzAAgAAMIzAAgAAMIzAAgAAMIzAAgAAMIzAAgAAMIzAAgAAMIzAAgAAMCxkA2vnzp266aabVFJS0mn7H//4RyUnJysjI0MZGRm6/vrrlZGRoTfffNN/TFVVlaZMmaKxY8dq5syZqq2t9e%2B7cOGCli5dqqysLGVmZqqoqEjNzc2WvS8AABD6woM9gW%2BioqJCr732moYOHdrt/sTERG3durXbfVu2bFF5ebkqKys1atQovfTSS5o9e7Y2b96s6OhorV69WocOHdL69evVp08fPfroo1q8eLGef/75QL4lAABgIyF5Bat///6qrq7W4MGDe/21NTU1mj59utLT0xUREaFZs2bJ6XRq%2B/bt8nq92rBhg%2BbOnauEhARdffXVmj9/vnbs2KGmpqYAvBMAAGBHIRlYeXl5ioqKuuj%2B1tZWFRYWavz48crKylJVVZV/X11dnUaPHt3p%2BOTkZNXW1ur48eM6e/asUlJS/PuSkpIUFRWl%2Bvp64%2B8DAADYU0gG1lfp27evhg8frvvuu0/vvPOOHnvsMa1du1b//d//LUnyeDxyuVydviYmJkYej0cej0cOh0MxMTGd9rtcLrW0tFj2HgAAQGgLyWewvkpqaqr%2B8z//0//P//iP/6gZM2bo1Vdf1d13392j1/D5fN94/MbGRtvcTnRICg%2B3XYMbFxbm7PQn7IX1tTfW197CwpwX/VyOi4tTfHx8wMa2XWB1Z/Dgwdq8ebMkye12d7ka5fF4NHLkSLndbvl8Pnk8HkVHR/v3f/rpp3K73T0aa926dSorK%2Buy/TvzKi/hHQRHWJhTsbF9gz2NkOFyRX/9QQhZrK%2B9sb729eKLld1%2BLhcWFmrevHkBG9d2gfXmm2/q7Nmz%2Bt73vuffduTIEQ0ZMkSSlJaWpvr6euXm5kqSOjo6dODAAeXl5WnIkCGKiYlRfX29Bg4cKEk6fPiw2tvblZ6e3qPx8/PzlZ2d3WX7k2%2BH3i1Gr7dDLS3ngj2Ny15YmFMuV7TOnGmT19sR7OnAMNbX3lhfewsLc170czkuLi6gY4dkYDU0NMjn86mtrU3t7e1qaGiQJCUkJCg8PFxPPPGErr32Wo0ZM0Z/%2BMMftGHDBpWWlkqSCgoKVFJSoqlTp2rUqFGqrKxUZGSksrKy5HQ6lZeXp/LycqWlpSkyMlJr1qzR5MmTe3wFKz4%2BvvtLjm/vMvb%2BreKT9Pnn/IXTU15vB/%2B%2BbIz1tTfW174u%2BrkcYCEZWFlZWXI4HP5/3rp1qxwOhw4ePKhJkybp4Ycf1rJly3T69Gl961vf0tKlS/31OmHCBBUXF2v%2B/Plqbm5Wenq6KioqFBERIUkqKirS%2BfPnddddd8nr9WrixIlavnx5UN4nAAAITQ7fpTzRjR77p//YpQMnW4M9jV4pujVJt6cNCvY0Lnvh4X97Vq2l5Rz/BWxDrK%2B9sb729vf1DQa%2BbQIAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwywLr7NmzVg0FAAAQVJYF1s0336xFixbpvffes2pIAACAoLAssP7t3/5Nzc3N%2Bud//mfdfvvt%2BtWvfqXm5marhgcAALCMZYGVm5urn//859q5c6e%2B//3va/Pmzbrlllv00EMPac%2BePVZNAwAAIOAsf8jd7XZr5syZevnll1VaWqp3331XP/zhD3XHHXforbfesno6AAAAxoVbPWBzc7M2bNigV199VR999JEmTJig/Px8ffLJJ3r00Uf1ySef6P7777d6WgAAAMZYFlg7d%2B5UTU2Ntm/frpiYGH3ve99Tfn6%2BBg4c6D9m%2BPDhWrhwIYEFAABCmmWB9eCDD2r8%2BPFavXq1br31VoWFhXU5ZsyYMYqOjrZqSgAAAAFhWWD9/ve/17e//W1duHDBH1etra3q16%2Bf/5irrrqK57AAAEDIs%2Bwhd6fTqTvuuEPbtm3zb1u/fr3uuOMOffzxx1ZNAwAAIOAsC6zHH39co0aN0pgxY/zbcnJydP311%2BuJJ56wahoAAAABZ9ktwvfff19vv/12p2esvvWtb2nZsmWaOHGiVdMAAAAIOMuuYPl8Pl24cKHL9tbWVnm9XqumAQAAEHCWBdaECRO0aNEiHTp0SK2trTpz5oz27dun4uJi3XLLLVZNAwAAIOAsu0W4ePFizZ07V7m5uXI4HP7tY8eO1fLly62aBgAAQMBZFlgDBgzQK6%2B8okOHDunPf/6zwsLCdN1112n48OFWTQEAAMASlv%2BqnOTkZCUnJ1s9LAAAgGUsC6za2lqtXLlShw8f1meffdZl/8GDB62aCgAAQEBZFlhLly5Vnz59NG/ePPXp08eqYQEAACxnWWB99NFH2rVrV6dfjQMAAGBHlv2YhkGDBlk1FAAAQFBZFlgPPfSQ/v3f/12tra1WDQkAABAUlt0iLC8v14kTJ7Rx40b1799fTmfnttu1a5dVUwEAAAgoywIrOzvbqqEAAACCyrLAKiwstGooAACAoLLsGSxJ2r17txYuXKj77rtPktTR0aE33njDyikAAAAEnGWB9frrr%2BuBBx7Q2bNn9b//%2B7%2BSpNOnT2v58uWqrq62ahoAAAABZ1lgvfDCC1q9erWef/55/y97HjRokJ599ln98pe/tGoaAAAAAWdZYB0/flyTJ0%2BWJH9gSdK4ceN04sSJXr/ezp07ddNNN6mkpKTLvt27d%2Buee%2B7RmDFjdOedd2rjxo2d9ldVVWnKlCkaO3asZs6cqdraWv%2B%2BCxcuaOnSpcrKylJmZqaKiorU3Nzc6/kBAIArl2WBFRsb222oHDt2TH379u3Va1VUVKi0tFRDhw7tsq%2BxsVGFhYW69957tWfPHi1ZskQrVqxQXV2dJGnLli0qLy9XaWmp3n33XWVnZ2v27Nlqa2uTJK1evVqHDh3S%2BvXr9dZbb8nhcGjx4sXf4B0DAIArlWWBNX78eC1ZskT/93//J0nyeDzatWuX5s%2Bfr4kTJ/bqtfr376/q6moNHjy4y75NmzYpKSlJ06ZNU0REhMaPH69JkyappqZGklRTU6Pp06crPT1dERERmjVrlpxOp7Zv3y6v16sNGzZo7ty5SkhI0NVXX6358%2Bdrx44dampquvR/CQAA4IpgWWAtWrRIbW1tmjp1qv76178qMzNTP/rRj5SYmKiHH364V6%2BVl5enqKiobvfV19crNTW107aUlBT/bcC6ujqNHj260/7k5GTV1tbq%2BPHjOnv2rFJSUvz7kpKSFBUVpfr6%2Bl7NEQAAXLks%2BzlY/fv310svvaRDhw7p6NGjioqKUlJSkpKSkoyO4/F4dM0113TaFhMTo5aWFv9%2Bl8vVZb/H45HH45HD4VBMTEyn/S6Xy//1AAAAX8eywPq75ORkJScnB3QMn88XtK9vbGy0ze1Eh6TwcEt/VFpICgtzdvoT9sL62hvra29hYc6Lfi7HxcUpPj4%2BYGNbFljJycmdvnvwyw4ePGhknNjYWHk8nk7bPB6PBgwYIElyu91drkZ5PB6NHDlSbrdbPp9PHo9H0dHR/v2ffvqp3G53j8Zft26dysrKumz/zrzK3r6VoAsLcyo2tnffgHAlc7miv/4ghCzW195YX/t68cXKbj%2BXCwsLNW/evICNa1lgLV%2B%2BvFNgeb1eHTt2TDt37tTs2bONjZOWlqYNGzZ02lZbW6uMjAz//vr6euXm5kr620%2BTP3DggPLy8jRkyBDFxMSovr5eAwcOlCQdPnxY7e3tSk9P79H4%2Bfn53f7exSffDr1bjF5vh1pazgV7Gpe9sDCnXK5onTnTJq%2B3I9jTgWGsr72xvvYWFua86OdyXFxcQMe2LLAKCgq63f7HP/5R69at07Rp03r8Wg0NDfL5fGpra1N7e7saGhokSQkJCcrJyVFZWZlqamqUk5OjPXv26J133tH69ev98ygpKdHUqVM1atQoVVZWKjIyUllZWXI6ncrLy1N5ebnS0tIUGRmpNWvWaPLkyT2%2BghUfH9/9Jce3d/X4/V0ufJI%2B/5y/cHrK6%2B3g35eNsb72xvra10U/lwPM8mewvuyGG27QnDlzevU1WVlZna6Gbd26VQ6HQwcPHpTb7dbzzz%2BvlStX6ic/%2BYkSExNVWlqqESNGSJImTJig4uJizZ8/X83NzUpPT1dFRYUiIiIkSUVFRTp//rzuuusueb1eTZw4UcuXLzf3hgEAgO05fJf6RPgl2rp1q5YsWaI//OEPwZxGwP3Tf%2BzSgZOtwZ5GrxTdmqTb0wYFexqXvfDwvz2r1tJyjv8CtiHW195YX3v7%2B/oGZWyrBrr55pu7bPvss8907ty5i94%2BBAAACEWWBVZ%2Bfn6X7yKMjIzUsGHDun34DAAAIFRZFliB/FZIAACAy4llgdXdz6DojsPh0Ny5cwM8GwAAgMCxLLA2btyopqYm/fWvf1VMTIw6Ojp09uxZRUVFqV%2B/fp2OJbAAAEAosyywiouLtX37dj388MP%2Bn6p%2B%2BvRprVq1Srfddpu%2B%2B93vWjUVAACAgLLsly8988wzevTRR/1xJUnXXHONli1bpp/%2B9KdWTQMAACDgLAusU6dOKSwsrMv2q666Sn/5y1%2BsmgYAAEDAWRZYI0aM0KJFi1RfX68zZ86otbVVhw4d0uLFizVq1CirpgEAABBwlj2D9dhjj6mkpER33323/%2Bdh%2BXw%2BDRo0SM8995xV0wAAAAg4ywJr9OjR%2Bt3vfqe6ujqdOnVKPp9P11xzjdLS0uR0WnYhDQAAIOAs/2XPLpdLZ8%2BeVWZmptVDAwAAWMKyS0fNzc2aMWOGJk%2BerH/5l3%2BRJDU1NWnq1Kk6deqUVdMAAAAIOMsCa9WqVYqKilJ1dbX/luDVV1%2Bt0aNH68knn7RqGgAAAAFn2S3CnTt36re//a0SEhL8D7lHRUXpkUce0W233WbVNAAAAALOsitY7e3tio%2BP77I9KipK7e3tVk0DAAAg4CwLrGHDhumtt97qsn3dunUaOnSoVdMAAAAIOMtuEf7whz/UwoUL9cYbb8jr9eqxxx5TfX299u/fr5/97GdWTQMAACDgLLuCNWXKFL3wwgvyer269tpr9f777ysxMVGvvPKKJk%2BebNU0AAAAAs6SK1gdHR2qq6tTZmYmP/8KAADYniVXsJxOp37wgx/I6/VaMRwAAEBQWXaL8K677lJVVZV8Pp9VQwIAAASFZQ%2B5nz59Wlu2bNHPf/5zDRo0SBEREZ32v/LKK1ZNBQAAIKAsC6zY2FhNmDDBquEAAACCJuCBVVRUpGeffVZPPPGEf9uzzz6roqKiQA8NAAAQFAF/BmvHjh1dtv3iF78I9LAAAABBY9lD7l/Eg%2B4AAMDOghJYf/9lzwAAAHYUlMACAACwMwILAADAsIB/F2F7e7tKSkq%2BdtvTTz8d6KkAAABYIuCBNWbMGDU2Nn7tNgAAALsIeGC99NJLgR4CAADgssIzWAAAAIYRWAAAAIYRWAAAAIYRWAAAAIYRWAAAAIYRWAAAAIYRWAAAAIYRWAAAAIYRWAAAAIYRWAAAAIYRWAAAAIYRWAAAAIYRWAAAAIYRWAAAAIYRWAAAAIYRWAAAAIYRWAAAAIYRWAAAAIYRWAAAAIYRWAAAAIYRWAAAAIYRWAAAAIYRWAAAAIYRWAAAAIYRWAAAAIYRWAAAAIYRWAAAAIYRWAAAAIYRWAAAAIYRWAAAAIYRWAAAAIYRWAAAAIYRWAAAAIYRWAAAAIYRWAAAAIYRWAAAAIbZMrCSk5N1/fXXKyMjw//nypUrJUm7d%2B/WPffcozFjxujOO%2B/Uxo0bO31tVVWVpkyZorFjx2rmzJmqra0NxlsAAAAhLDzYEwgEh8OhN998UwMHDuy0vbGxUYWFhVq2bJm%2B%2B93vat%2B%2Bffrxj3%2Bs4cOHKy0tTVu2bFF5ebkqKys1atQovfTSS5o9e7Y2b96s6OjoIL0bAAAQamx5Bcvn88nn83XZvmnTJiUlJWnatGmKiIjQ%2BPHjNWnSJNXU1EiSampqNH36dKWnpysiIkKzZs2S0%2BnU9u3brX4LAAAghNkysCRp9erVmjhxom644QYtW7ZM58%2BfV319vVJTUzsdl5KS4r8NWFdXp9GjR3fan5yczG1CAADQK7YMrPT0dI0fP15vvvmm/uu//kvvv/%2B%2BVqxYIY/HI5fL1enYmJgYtbS0SNJF93s8HsvmDgAAQp8tn8Gqrq72/%2B/hw4drwYIFmjNnjsaOHdvtrUOTGhsb1dTUFNAxrOKQFB5uywY3KizM2elP2Avra2%2Bsr72FhTkv%2BrkcFxen%2BPj4gI1ty8D6ssGDB8vr9crpdHa5GuXxeDRgwABJktvt9l/N%2BuL%2BkSNH9nisdevWqaysrMv278yr/AYzD66wMKdiY/sGexohw%2BXiGyHsjPW1N9bXvl58sbLbz%2BXCwkLNmzcvYOPaLrAOHTqkN954Q8XFxf5tR44cUWRkpLKysvTqq692Or62tlYZGRmSpLS0NNXX1ys3N1eS1NHRoQMHDuiee%2B7p8fj5%2BfnKzs7usv3Jt1u6Ofry5vV2qKXlXLCncdkLC3PK5YrWmTNt8no7gj0dGMb62hvra29hYc6Lfi7HxcUFdGzbBVZsbKx%2B85vfKD4%2BXnl5eTpx4oTWrl2rgoIC5eTkqKysTDU1NcrJydGePXv0zjvvaP369ZKkgoIClZSUaOrUqRo1apQqKysVGRmpW265pcfjx8fHd3/J8e1dht6hdXySPv%2Bcv3B6yuvt4N%2BXjbG%2B9sb62tdFP5cDzHaBlZCQoIqKCq1evVpr1qxRVFSU7r77bhUVFemqq67S888/r5UrV%2BonP/mJEhMTVVpaqhEjRkiSJkyYoOLiYs2fP1/Nzc1KT09XRUWFIiIigvyuAABAKHH4Av3UNyRJ//Qfu3TgZGuwp9ErRbcm6fa0QcGexmUvPPxvz6q1tJzjv4BtiPW1N9bX3v6%2BvsHAt00AAAAYRmABAAAYRmABAAAYRmABAAAYRmABAAAYRmABAAAYRmABAAAYRmABAAAYRmABAAAYRmABAAAYRmABAAAYRmABAAAYRmABAAAYRmABAAAYRmABAAAYRmABAAAYRmABAAAYRmABAAAYRmABAAAYRmABAAAYRmABAAAYRmABAAAYRmABAAAYRmABAAAYRmABAAAYRmABAAAYRmABAAAYRmABAAAYRmABAAAYRmABAAAYRmABAAAYRmABAAAYRmABAAAYRmABAAAYRmABAAAYRmABAAAYRmABAAAYRmABAAAYRmABAAAYRmABAAAYRmABAAAYRmABAAAYRmABAAAYRmABAAAYRmABAAAYRmABAAAYRmABAAAYRmABAAAYRmABAAAYRmABAAAYRmABAAAYRmABAAAYRmABAAAYRmABAAAYRmABAAAYRmABAAAYRmABAAAYRmABAAAYRmABAAAYRmABAAAYRmABAAAYRmABAAAYRmABAAAYRmABAAAYRmABAAAYRmABAAAYRmABAAAYRmABAAAYRmABAAAYRmB9yYkTJ/TAAw9o3Lhxys7O1lNPPSWfzxfsaQEAgBBCYH1JUVGRBg4cqG3btunFF1/Utm3bVFVVFexpAQCAEEJgfUFtba0OHz6shQsXqm/fvhoyZIjuv/9%2BVVdXB3tqAAAghIQHewKXkwMHDigxMVH9%2BvXzb0tJSdGxY8d0/vx59enTJ4izs57DIUmhenvUEewJAACuYATWF3g8Hrlcrk7b%2BvfvL0lqaWm54gLLFRWun24%2BrI/%2B0hbsqfTYdQOi9dBtI4M9DQDAFY7A%2BpJLfaC9sbFRTU1NXbZfOyBan3eE1tWgflFhwZ7CNxJu8f%2BrnU7p888/l9Np/dgIPNbX3ljfy0VgnlgKC3Ne9HM5Li5O8fHxARlXIrA6cbvd8ng8nbZ5PB45HA653e4evca6detUVlbWZfsNN9ygn61ZE9DFDIRJ/%2B%2B6YE/hstfY2Khf//qXys/PD7n1xddjfe2N9bW3xsZGFRcXa%2B/evV32FRYWat68eQEbm8D6grS0NJ06dUoej8d/a3D//v0aNmyYoqOje/Qa%2Bfn5ys7O7rTtyJEjWrhwoZqamjiBbaipqUllZWXKzs5mfW2I9bU31tfempqatHfvXpWWlmrYsGGd9sXFxQV0bALrC1JSUpSenq6nn35aixYtUkNDg6qqqjRr1qwev0Z8fDwnKQAAl5Fhw4YpNTXV0jH5MQ1f8swzz6ihoUE333yzfvCDH2jatGkqKCgI9rS0%2BBFgAAAGe0lEQVQAAEAI4QrWlyQkJKiioiLY0wAAACGMK1gAAACGha1YsWJFsCdxJejbt6%2B%2B853vqG/fvsGeCgKA9bU31tfeWF97C9b6Onz8JmMAAACjuEUIAABgGIEFAABgGIEFAABgGIEFAABgGIEFAABgGIEFAABgGIEFAABgGIEFAABgGIFlyIkTJ/TAAw9o3Lhxys7O1lNPPaWL/QzXqqoqTZkyRWPHjtXMmTNVW1tr8WzRWz1d37KyMo0ePVoZGRnKyMjQ9ddfr4yMDDU3Nwdh1uipnTt36qabblJJScnXHsv5G3p6ur6cv6Hp5MmTKiws1Lhx43TjjTdq0aJFam1t7fZYK89fAsuQoqIiDRw4UNu2bdOLL76obdu2qaqqqstxW7ZsUXl5uUpLS/Xuu%2B8qOztbs2fPVltbm/WTRo/1dH0l6a677tIHH3ygDz74QPv379cHH3wgt9tt7YTRYxUVFSotLdXQoUO/9ljO39DTm/WVOH9D0Zw5c%2BRyubRjxw699tprOnr0qJ588skux1l9/hJYBtTW1urw4cNauHCh%2BvbtqyFDhuj%2B%2B%2B9XdXV1l2Nramo0ffp0paenKyIiQrNmzZLT6dT27duDMHP0RG/WF6Gnf//%2Bqq6u1uDBg7/2WM7f0NOb9UXoaW1tVWpqqhYsWKCoqCgNGDBAubm52rt3b5djrT5/CSwDDhw4oMTERPXr18%2B/LSUlRceOHdP58%2Bc7HVtXV6fRo0d32pacnMxthstYb9ZXkj788EPNmDFDY8aM0Z133qndu3dbOV30Ul5enqKionp0LOdv6OnN%2Bkqcv6GmX79%2BevzxxztdZfzkk0%2BUkJDQ5Virz18CywCPxyOXy9VpW//%2B/SVJLS0tX3tsTEyMPB5PYCeJb6w36xsfH6/ExEStWrVKu3btUm5urh588EEdO3bMsvkicDh/7Y3zN/TV1tbq5Zdf1uzZs7vss/r8JbAMudgD7bCHnq5vXl6e1q5dq%2Buuu07R0dGaNWuWUlJS9D//8z8BniGAS8X5G9r%2B9Kc/6Uc/%2BpEWLlyo8ePHB3s6BJYJbre7SwF7PB45HI4uD0e63e5ur2rxEOXlqzfr253BgwerqakpUNODhTh/rzycv6Fh27ZtevDBB7VkyRLNnDmz22OsPn8JLAPS0tJ06tSpTh/C%2B/fv17BhwxQdHd3l2Pr6ev8/d3R06MCBA8rIyLBsvuid3qzvCy%2B8oPfee6/TtiNHjmjIkCGWzBWBxflrb5y/oWnfvn165JFHtHbtWuXk5Fz0OKvPXwLLgJSUFKWnp%2Bvpp59Wa2urjhw5oqqqKt17772SpClTpmjfvn2SpIKCAv32t7/VBx98oM8%2B%2B0zPPfecIiMjdcsttwTxHeCr9GZ9m5ubtXLlSn388ce6cOGCfvWrX%2Bnjjz/WtGnTgvkW8BUaGhp0%2BvRptbW16bPPPlNDQ4MaGhr8%2B2%2B//XbO3xDWm/Xl/A09Xq9XS5cu1YIFC5SZmdllfzDP3/CAvOoV6JlnntHSpUt18803q1%2B/fiooKFBBQYEk6c9//rP/u80mTJig4uJizZ8/X83NzUpPT1dFRYUiIiKCOX18jZ6ub0lJidasWaPvf//7%2BvTTTzVixAj9%2Bte/Vnx8fDCnj6%2BQlZUlh8Ph/%2BetW7fK4XDo4MGDkqSPPvqI8zeE9WZ9OX9Dz/vvv6%2BjR49q5cqVeuyxx%2BRwOOTz%2BeRwOPS73/0uqOevw8fT2QAAAEZxixAAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAFhi586duummm1RSUtLrrz137pwWLFig5ORkHTt27KLHbdmyRcnJydq7d%2B%2BlTPWSEVgAACDgKioqVFpaqqFDh/b6axsaGnT33XerT58%2BnX4y/5e1tbVp1apV6tOnz6VM1QgCCwAABFz//v1VXV2twYMHd7t/z549mjFjhv7hH/5BWVlZeu655/z7zpw5o2XLlunBBx/UV/0CmrVr1%2BrGG29UbGys8fn3FoEFAAACLi8vT1FRUd3uO336tObOnasZM2Zo3759qqys1Lp16/T6669LkkaMGKEbb7zxK1//ww8/1KZNm1RSUvKVEWYVAgsAAATV66%2B/rpEjRyo3N1fS34IqPz9fGzdu7PFrrFixQsXFxYqJiQnUNHslPNgTAAAAV7bjx49r//79ysjI8G/z%2BXw9fl5r/fr1Cg8P9wfa5eD/A8EdHKF1q6FlAAAAAElFTkSuQmCC\">\n",
" </div>\n",
" </div>\n",
" </div><div class=\"row variablerow ignore\">\n",
" <div class=\"col-md-3 namecol\">\n",
" <p class=\"h4\"><s>시가총액(전체)</s><br/><small>Highly correlated</small></p>\n",
" </div>\n",
"\n",
" <div class=\"col-md-3\">\n",
" <p> <em>This variable is highly correlated with <code>시가총액</code> and should be ignored for analysis</em></p>\n",
"\n",
" </div>\n",
" <div class=\"col-md-6\">\n",
" <table class=\"stats \">\n",
" <tr><th>Correlation</th>\n",
" <td>0.99894</td></tr>\n",
" </table>\n",
" </div>\n",
" </div><div class=\"row variablerow\">\n",
" <div class=\"col-md-3 namecol\">\n",
" <p class=\"h4\">주식코드<br/><small>Categorical, Unique</small></p>\n",
" </div>\n",
"\n",
" <div class=\"col-md-3 collapse in\" id=\"minivalues-5500797688343839965\"><table border=\"1\" class=\"dataframe example_values\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>First 3 values</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>A097780</td>\n",
" </tr>\n",
" <tr>\n",
" <td>A033660</td>\n",
" </tr>\n",
" <tr>\n",
" <td>A049800</td>\n",
" </tr>\n",
" </tbody>\n",
"</table></div>\n",
" <div class=\"col-md-6 collapse in\" id=\"minivalues-5500797688343839965\"><table border=\"1\" class=\"dataframe example_values\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>Last 3 values</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>A001250</td>\n",
" </tr>\n",
" <tr>\n",
" <td>A192530</td>\n",
" </tr>\n",
" <tr>\n",
" <td>A001740</td>\n",
" </tr>\n",
" </tbody>\n",
"</table></div>\n",
" <div class=\"col-md-12 text-right\">\n",
" <a role=\"button\" data-toggle=\"collapse\" data-target=\"#values-5500797688343839965,#minivalues-5500797688343839965\" aria-expanded=\"false\" aria-controls=\"collapseExample\">\n",
" Toggle details\n",
" </a>\n",
" </div>\n",
" <div class=\"col-md-12 collapse\" id=\"values-5500797688343839965\">\n",
" <p class=\"h4\">First 20 values</p>\n",
" <table border=\"1\" class=\"dataframe sample table table-hover\">\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>A097780</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>A033660</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>A049800</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>A113810</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>A104480</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>A017510</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>A008470</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>A126700</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>A004490</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>A111770</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>A058610</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>A028050</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>A019540</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>A000030</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>A094480</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>A026960</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>A035620</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>A096350</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>A068760</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>A008560</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
" <p class=\"h4\">Last 20 values</p>\n",
" <table border=\"1\" class=\"dataframe sample table table-hover\">\n",
" <tbody>\n",
" <tr>\n",
" <th>1821</th>\n",
" <td>A145990</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1822</th>\n",
" <td>A196700</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1823</th>\n",
" <td>A101170</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1824</th>\n",
" <td>A085620</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1825</th>\n",
" <td>A090430</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1826</th>\n",
" <td>A059120</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1827</th>\n",
" <td>A086960</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1828</th>\n",
" <td>A011930</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1829</th>\n",
" <td>A003560</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1830</th>\n",
" <td>A900050</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1831</th>\n",
" <td>A001720</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1832</th>\n",
" <td>A031390</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1833</th>\n",
" <td>A029960</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1834</th>\n",
" <td>A045100</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1835</th>\n",
" <td>A011810</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1836</th>\n",
" <td>A078890</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1837</th>\n",
" <td>A025860</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1838</th>\n",
" <td>A001250</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1839</th>\n",
" <td>A192530</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1840</th>\n",
" <td>A001740</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
" </div>\n",
"\n",
" </div>\n",
"\n",
" <div class=\"row headerrow highlight\">\n",
" <h1>Sample</h1>\n",
" </div>\n",
"\n",
" \n",
" <div class=\"row variablerow\">\n",
" <div class=\"col-md-12\" style=\"overflow:scroll; width: 100%%; overflow-y: hidden;\">\n",
" <table border=\"1\" class=\"dataframe sample\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Code</th>\n",
" <th>Name</th>\n",
" <th>결산월</th>\n",
" <th>주식코드</th>\n",
" <th>WICS업종명(대)</th>\n",
" <th>WICS업종코드(대)</th>\n",
" <th>WICS업종명(중)</th>\n",
" <th>WICS업종코드(중)</th>\n",
" <th>WICS업종명(소)</th>\n",
" <th>WICS업종코드(소)</th>\n",
" <th>WI26업종명(대)</th>\n",
" <th>WI26업종코드(대)</th>\n",
" <th>WI26업종명(중)</th>\n",
" <th>KOSPI200 구성종목여부(1:해당, 0:미해당)</th>\n",
" <th>WI26업종코드(중)</th>\n",
" <th>WMI500 시가총액규모별(1:대형주,2:중형주,3:소형주,0:WMI500미해당)</th>\n",
" <th>시가총액</th>\n",
" <th>시가총액(전체)</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>A000020</td>\n",
" <td>동화약품</td>\n",
" <td>12</td>\n",
" <td>A000020</td>\n",
" <td>건강관리</td>\n",
" <td>G35</td>\n",
" <td>제약과생물공학</td>\n",
" <td>G3520</td>\n",
" <td>제약</td>\n",
" <td>G352020</td>\n",
" <td>건강관리</td>\n",
" <td>WI410</td>\n",
" <td>제약</td>\n",
" <td>0</td>\n",
" <td>WI41010</td>\n",
" <td>0</td>\n",
" <td>226524221700</td>\n",
" <td>226524221700</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>A000030</td>\n",
" <td>우리은행</td>\n",
" <td>12</td>\n",
" <td>A000030</td>\n",
" <td>금융</td>\n",
" <td>G40</td>\n",
" <td>은행</td>\n",
" <td>G4010</td>\n",
" <td>은행</td>\n",
" <td>G401010</td>\n",
" <td>은행</td>\n",
" <td>WI500</td>\n",
" <td>은행</td>\n",
" <td>1</td>\n",
" <td>WI50010</td>\n",
" <td>1</td>\n",
" <td>6394960000000</td>\n",
" <td>6394960000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>A000040</td>\n",
" <td>KR모터스</td>\n",
" <td>12</td>\n",
" <td>A000040</td>\n",
" <td>경기관련소비재</td>\n",
" <td>G25</td>\n",
" <td>자동차와부품</td>\n",
" <td>G2510</td>\n",
" <td>자동차</td>\n",
" <td>G251020</td>\n",
" <td>자동차</td>\n",
" <td>WI300</td>\n",
" <td>자동차</td>\n",
" <td>0</td>\n",
" <td>WI30010</td>\n",
" <td>0</td>\n",
" <td>199850850960</td>\n",
" <td>199850850960</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>A000050</td>\n",
" <td>경방</td>\n",
" <td>12</td>\n",
" <td>A000050</td>\n",
" <td>경기관련소비재</td>\n",
" <td>G25</td>\n",
" <td>내구소비재와의류</td>\n",
" <td>G2520</td>\n",
" <td>섬유,의류,신발,호화품</td>\n",
" <td>G252060</td>\n",
" <td>화장품,의류</td>\n",
" <td>WI310</td>\n",
" <td>의류</td>\n",
" <td>1</td>\n",
" <td>WI31020</td>\n",
" <td>3</td>\n",
" <td>538710055500</td>\n",
" <td>538710055500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>A000060</td>\n",
" <td>메리츠화재</td>\n",
" <td>12</td>\n",
" <td>A000060</td>\n",
" <td>금융</td>\n",
" <td>G40</td>\n",
" <td>보험</td>\n",
" <td>G4040</td>\n",
" <td>손해보험</td>\n",
" <td>G404010</td>\n",
" <td>보험</td>\n",
" <td>WI520</td>\n",
" <td>보험</td>\n",
" <td>0</td>\n",
" <td>WI52010</td>\n",
" <td>2</td>\n",
" <td>1695408000000</td>\n",
" <td>1695408000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
" </div>\n",
" </div>\n",
"\n",
"</div>\n",
"\n"
],
"text/plain": [
"<pandas_profiling.ProfileReport at 0x7fd1b91733c8>"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pandas_profiling\n",
"pandas_profiling.ProfileReport(df_wics)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Selection by Mask\n"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>dt</th>\n",
" <th>stk_cd</th>\n",
" <th>open_prc</th>\n",
" <th>high_prc</th>\n",
" <th>low_prc</th>\n",
" <th>close_prc</th>\n",
" <th>trd_amt</th>\n",
" <th>marketvalue</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2888</th>\n",
" <td>20000104</td>\n",
" <td>10</td>\n",
" <td>3930.0</td>\n",
" <td>4255.0</td>\n",
" <td>3850.0</td>\n",
" <td>4050.0</td>\n",
" <td>3217298350</td>\n",
" <td>2750266365750</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2889</th>\n",
" <td>20000105</td>\n",
" <td>10</td>\n",
" <td>4000.0</td>\n",
" <td>4120.0</td>\n",
" <td>3800.0</td>\n",
" <td>3925.0</td>\n",
" <td>2929724650</td>\n",
" <td>2665381601375</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2890</th>\n",
" <td>20000106</td>\n",
" <td>10</td>\n",
" <td>3955.0</td>\n",
" <td>4100.0</td>\n",
" <td>3450.0</td>\n",
" <td>3600.0</td>\n",
" <td>2783021100</td>\n",
" <td>2444681214000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2891</th>\n",
" <td>20000107</td>\n",
" <td>10</td>\n",
" <td>3900.0</td>\n",
" <td>3970.0</td>\n",
" <td>3705.0</td>\n",
" <td>3800.0</td>\n",
" <td>3938483550</td>\n",
" <td>2580496837000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2892</th>\n",
" <td>20000110</td>\n",
" <td>10</td>\n",
" <td>3950.0</td>\n",
" <td>4040.0</td>\n",
" <td>3900.0</td>\n",
" <td>3980.0</td>\n",
" <td>15740428900</td>\n",
" <td>2702730897700</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" dt stk_cd open_prc high_prc low_prc close_prc trd_amt \\\n",
"2888 20000104 10 3930.0 4255.0 3850.0 4050.0 3217298350 \n",
"2889 20000105 10 4000.0 4120.0 3800.0 3925.0 2929724650 \n",
"2890 20000106 10 3955.0 4100.0 3450.0 3600.0 2783021100 \n",
"2891 20000107 10 3900.0 3970.0 3705.0 3800.0 3938483550 \n",
"2892 20000110 10 3950.0 4040.0 3900.0 3980.0 15740428900 \n",
"\n",
" marketvalue \n",
"2888 2750266365750 \n",
"2889 2665381601375 \n",
"2890 2444681214000 \n",
"2891 2580496837000 \n",
"2892 2702730897700 "
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_kospi200[(df_kospi200.dt > 20000101)].head()"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>dt</th>\n",
" <th>stk_cd</th>\n",
" <th>open_prc</th>\n",
" <th>high_prc</th>\n",
" <th>low_prc</th>\n",
" <th>close_prc</th>\n",
" <th>trd_amt</th>\n",
" <th>marketvalue</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2888</th>\n",
" <td>20000104</td>\n",
" <td>10</td>\n",
" <td>3930.0</td>\n",
" <td>4255.0</td>\n",
" <td>3850.0</td>\n",
" <td>4050.0</td>\n",
" <td>3217298350</td>\n",
" <td>2750266365750</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2889</th>\n",
" <td>20000105</td>\n",
" <td>10</td>\n",
" <td>4000.0</td>\n",
" <td>4120.0</td>\n",
" <td>3800.0</td>\n",
" <td>3925.0</td>\n",
" <td>2929724650</td>\n",
" <td>2665381601375</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2890</th>\n",
" <td>20000106</td>\n",
" <td>10</td>\n",
" <td>3955.0</td>\n",
" <td>4100.0</td>\n",
" <td>3450.0</td>\n",
" <td>3600.0</td>\n",
" <td>2783021100</td>\n",
" <td>2444681214000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2891</th>\n",
" <td>20000107</td>\n",
" <td>10</td>\n",
" <td>3900.0</td>\n",
" <td>3970.0</td>\n",
" <td>3705.0</td>\n",
" <td>3800.0</td>\n",
" <td>3938483550</td>\n",
" <td>2580496837000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2892</th>\n",
" <td>20000110</td>\n",
" <td>10</td>\n",
" <td>3950.0</td>\n",
" <td>4040.0</td>\n",
" <td>3900.0</td>\n",
" <td>3980.0</td>\n",
" <td>15740428900</td>\n",
" <td>2702730897700</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" dt stk_cd open_prc high_prc low_prc close_prc trd_amt \\\n",
"2888 20000104 10 3930.0 4255.0 3850.0 4050.0 3217298350 \n",
"2889 20000105 10 4000.0 4120.0 3800.0 3925.0 2929724650 \n",
"2890 20000106 10 3955.0 4100.0 3450.0 3600.0 2783021100 \n",
"2891 20000107 10 3900.0 3970.0 3705.0 3800.0 3938483550 \n",
"2892 20000110 10 3950.0 4040.0 3900.0 3980.0 15740428900 \n",
"\n",
" marketvalue \n",
"2888 2750266365750 \n",
"2889 2665381601375 \n",
"2890 2444681214000 \n",
"2891 2580496837000 \n",
"2892 2702730897700 "
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"boolean_mask = df_kospi200.dt > 20000101\n",
"df_kospi200[boolean_mask].head()"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Code</th>\n",
" <th>Name</th>\n",
" <th>결산월</th>\n",
" <th>주식코드</th>\n",
" <th>WICS업종명(대)</th>\n",
" <th>WICS업종코드(대)</th>\n",
" <th>WICS업종명(중)</th>\n",
" <th>WICS업종코드(중)</th>\n",
" <th>WICS업종명(소)</th>\n",
" <th>WICS업종코드(소)</th>\n",
" <th>WI26업종명(대)</th>\n",
" <th>WI26업종코드(대)</th>\n",
" <th>WI26업종명(중)</th>\n",
" <th>KOSPI200 구성종목여부(1:해당, 0:미해당)</th>\n",
" <th>WI26업종코드(중)</th>\n",
" <th>WMI500 시가총액규모별(1:대형주,2:중형주,3:소형주,0:WMI500미해당)</th>\n",
" <th>시가총액</th>\n",
" <th>시가총액(전체)</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1833</th>\n",
" <td>A900120</td>\n",
" <td>씨케이에이치</td>\n",
" <td>6</td>\n",
" <td>A900120</td>\n",
" <td>건강관리</td>\n",
" <td>G35</td>\n",
" <td>제약과생물공학</td>\n",
" <td>G3520</td>\n",
" <td>제약</td>\n",
" <td>G352020</td>\n",
" <td>건강관리</td>\n",
" <td>WI410</td>\n",
" <td>제약</td>\n",
" <td>0</td>\n",
" <td>WI41010</td>\n",
" <td>0</td>\n",
" <td>281616759200</td>\n",
" <td>281616759200</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1839</th>\n",
" <td>A950130</td>\n",
" <td>엑세스바이오</td>\n",
" <td>12</td>\n",
" <td>A950130</td>\n",
" <td>건강관리</td>\n",
" <td>G35</td>\n",
" <td>제약과생물공학</td>\n",
" <td>G3520</td>\n",
" <td>생명과학도구및서비스</td>\n",
" <td>G352030</td>\n",
" <td>건강관리</td>\n",
" <td>WI410</td>\n",
" <td>생명과학</td>\n",
" <td>0</td>\n",
" <td>WI41020</td>\n",
" <td>0</td>\n",
" <td>208670610300</td>\n",
" <td>208670610300</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Code Name 결산월 주식코드 WICS업종명(대) WICS업종코드(대) WICS업종명(중) \\\n",
"1833 A900120 씨케이에이치 6 A900120 건강관리 G35 제약과생물공학 \n",
"1839 A950130 엑세스바이오 12 A950130 건강관리 G35 제약과생물공학 \n",
"\n",
" WICS업종코드(중) WICS업종명(소) WICS업종코드(소) WI26업종명(대) WI26업종코드(대) WI26업종명(중) \\\n",
"1833 G3520 제약 G352020 건강관리 WI410 제약 \n",
"1839 G3520 생명과학도구및서비스 G352030 건강관리 WI410 생명과학 \n",
"\n",
" KOSPI200 구성종목여부(1:해당, 0:미해당) WI26업종코드(중) \\\n",
"1833 0 WI41010 \n",
"1839 0 WI41020 \n",
"\n",
" WMI500 시가총액규모별(1:대형주,2:중형주,3:소형주,0:WMI500미해당) 시가총액 \\\n",
"1833 0 281616759200 \n",
"1839 0 208670610300 \n",
"\n",
" 시가총액(전체) \n",
"1833 281616759200 \n",
"1839 208670610300 "
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_wics[df_wics[\"WICS업종명(대)\"].isin([\"건강관리\"])].tail(2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Selection by Label/Positions"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>stk_code</th>\n",
" <th>A000010</th>\n",
" <th>A000020</th>\n",
" <th>A000030</th>\n",
" <th>A000050</th>\n",
" <th>A000070</th>\n",
" <th>A000080</th>\n",
" <th>A000100</th>\n",
" <th>A000120</th>\n",
" <th>A000140</th>\n",
" <th>A000150</th>\n",
" <th>...</th>\n",
" <th>A120110</th>\n",
" <th>A128940</th>\n",
" <th>A138930</th>\n",
" <th>A139480</th>\n",
" <th>A145990</th>\n",
" <th>A161390</th>\n",
" <th>A161890</th>\n",
" <th>A170900</th>\n",
" <th>A185750</th>\n",
" <th>A192820</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>20010131</th>\n",
" <td>2685</td>\n",
" <td>1603</td>\n",
" <td>865</td>\n",
" <td>18817</td>\n",
" <td>10034</td>\n",
" <td>2900</td>\n",
" <td>24809</td>\n",
" <td>7216</td>\n",
" <td>40694</td>\n",
" <td>16989</td>\n",
" <td>...</td>\n",
" <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>20010228</th>\n",
" <td>2545</td>\n",
" <td>1661</td>\n",
" <td>865</td>\n",
" <td>18889</td>\n",
" <td>11557</td>\n",
" <td>3100</td>\n",
" <td>25374</td>\n",
" <td>7063</td>\n",
" <td>47530</td>\n",
" <td>18766</td>\n",
" <td>...</td>\n",
" <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>20010330</th>\n",
" <td>1900</td>\n",
" <td>1410</td>\n",
" <td>865</td>\n",
" <td>16005</td>\n",
" <td>11665</td>\n",
" <td>3450</td>\n",
" <td>25500</td>\n",
" <td>6360</td>\n",
" <td>46996</td>\n",
" <td>17482</td>\n",
" <td>...</td>\n",
" <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>20010430</th>\n",
" <td>2230</td>\n",
" <td>1426</td>\n",
" <td>865</td>\n",
" <td>17195</td>\n",
" <td>11935</td>\n",
" <td>3750</td>\n",
" <td>23678</td>\n",
" <td>4143</td>\n",
" <td>47743</td>\n",
" <td>17433</td>\n",
" <td>...</td>\n",
" <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>20010531</th>\n",
" <td>2685</td>\n",
" <td>1926</td>\n",
" <td>865</td>\n",
" <td>18168</td>\n",
" <td>15553</td>\n",
" <td>4000</td>\n",
" <td>29143</td>\n",
" <td>5275</td>\n",
" <td>53404</td>\n",
" <td>23112</td>\n",
" <td>...</td>\n",
" <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",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 435 columns</p>\n",
"</div>"
],
"text/plain": [
"stk_code A000010 A000020 A000030 A000050 A000070 A000080 A000100 A000120 \\\n",
"20010131 2685 1603 865 18817 10034 2900 24809 7216 \n",
"20010228 2545 1661 865 18889 11557 3100 25374 7063 \n",
"20010330 1900 1410 865 16005 11665 3450 25500 6360 \n",
"20010430 2230 1426 865 17195 11935 3750 23678 4143 \n",
"20010531 2685 1926 865 18168 15553 4000 29143 5275 \n",
"\n",
"stk_code A000140 A000150 ... A120110 A128940 A138930 A139480 A145990 \\\n",
"20010131 40694 16989 ... NaN NaN NaN NaN NaN \n",
"20010228 47530 18766 ... NaN NaN NaN NaN NaN \n",
"20010330 46996 17482 ... NaN NaN NaN NaN NaN \n",
"20010430 47743 17433 ... NaN NaN NaN NaN NaN \n",
"20010531 53404 23112 ... NaN NaN NaN NaN NaN \n",
"\n",
"stk_code A161390 A161890 A170900 A185750 A192820 \n",
"20010131 NaN NaN NaN NaN NaN \n",
"20010228 NaN NaN NaN NaN NaN \n",
"20010330 NaN NaN NaN NaN NaN \n",
"20010430 NaN NaN NaN NaN NaN \n",
"20010531 NaN NaN NaN NaN NaN \n",
"\n",
"[5 rows x 435 columns]"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_factors[\"수정주가\"].head()"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"['123', 'd']"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a = [\"123\", \"d\", \"cd\"]\n",
"a[0:2]"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>stk_code</th>\n",
" <th>A000010</th>\n",
" <th>A000020</th>\n",
" <th>A000030</th>\n",
" <th>A000050</th>\n",
" <th>A000070</th>\n",
" <th>A000080</th>\n",
" <th>A000100</th>\n",
" <th>A000120</th>\n",
" <th>A000140</th>\n",
" <th>A000150</th>\n",
" <th>...</th>\n",
" <th>A120110</th>\n",
" <th>A128940</th>\n",
" <th>A138930</th>\n",
" <th>A139480</th>\n",
" <th>A145990</th>\n",
" <th>A161390</th>\n",
" <th>A161890</th>\n",
" <th>A170900</th>\n",
" <th>A185750</th>\n",
" <th>A192820</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>20010131</th>\n",
" <td>2685</td>\n",
" <td>1603</td>\n",
" <td>865</td>\n",
" <td>18817</td>\n",
" <td>10034</td>\n",
" <td>2900</td>\n",
" <td>24809</td>\n",
" <td>7216</td>\n",
" <td>40694</td>\n",
" <td>16989</td>\n",
" <td>...</td>\n",
" <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>20010228</th>\n",
" <td>2545</td>\n",
" <td>1661</td>\n",
" <td>865</td>\n",
" <td>18889</td>\n",
" <td>11557</td>\n",
" <td>3100</td>\n",
" <td>25374</td>\n",
" <td>7063</td>\n",
" <td>47530</td>\n",
" <td>18766</td>\n",
" <td>...</td>\n",
" <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>20010330</th>\n",
" <td>1900</td>\n",
" <td>1410</td>\n",
" <td>865</td>\n",
" <td>16005</td>\n",
" <td>11665</td>\n",
" <td>3450</td>\n",
" <td>25500</td>\n",
" <td>6360</td>\n",
" <td>46996</td>\n",
" <td>17482</td>\n",
" <td>...</td>\n",
" <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",
" </tbody>\n",
"</table>\n",
"<p>3 rows × 435 columns</p>\n",
"</div>"
],
"text/plain": [
"stk_code A000010 A000020 A000030 A000050 A000070 A000080 A000100 A000120 \\\n",
"20010131 2685 1603 865 18817 10034 2900 24809 7216 \n",
"20010228 2545 1661 865 18889 11557 3100 25374 7063 \n",
"20010330 1900 1410 865 16005 11665 3450 25500 6360 \n",
"\n",
"stk_code A000140 A000150 ... A120110 A128940 A138930 A139480 A145990 \\\n",
"20010131 40694 16989 ... NaN NaN NaN NaN NaN \n",
"20010228 47530 18766 ... NaN NaN NaN NaN NaN \n",
"20010330 46996 17482 ... NaN NaN NaN NaN NaN \n",
"\n",
"stk_code A161390 A161890 A170900 A185750 A192820 \n",
"20010131 NaN NaN NaN NaN NaN \n",
"20010228 NaN NaN NaN NaN NaN \n",
"20010330 NaN NaN NaN NaN NaN \n",
"\n",
"[3 rows x 435 columns]"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_factors[\"수정주가\"][0:3] # use positional index"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>stk_code</th>\n",
" <th>A000010</th>\n",
" <th>A000020</th>\n",
" <th>A000030</th>\n",
" <th>A000050</th>\n",
" <th>A000070</th>\n",
" <th>A000080</th>\n",
" <th>A000100</th>\n",
" <th>A000120</th>\n",
" <th>A000140</th>\n",
" <th>A000150</th>\n",
" <th>...</th>\n",
" <th>A120110</th>\n",
" <th>A128940</th>\n",
" <th>A138930</th>\n",
" <th>A139480</th>\n",
" <th>A145990</th>\n",
" <th>A161390</th>\n",
" <th>A161890</th>\n",
" <th>A170900</th>\n",
" <th>A185750</th>\n",
" <th>A192820</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>20100226</th>\n",
" <td>NaN</td>\n",
" <td>5980</td>\n",
" <td>NaN</td>\n",
" <td>85146</td>\n",
" <td>43452</td>\n",
" <td>35550</td>\n",
" <td>155674</td>\n",
" <td>56100</td>\n",
" <td>20400</td>\n",
" <td>106672</td>\n",
" <td>...</td>\n",
" <td>40447</td>\n",
" <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>20100331</th>\n",
" <td>NaN</td>\n",
" <td>6000</td>\n",
" <td>NaN</td>\n",
" <td>92748</td>\n",
" <td>45412</td>\n",
" <td>35550</td>\n",
" <td>164747</td>\n",
" <td>66100</td>\n",
" <td>23200</td>\n",
" <td>126920</td>\n",
" <td>...</td>\n",
" <td>43393</td>\n",
" <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>20100430</th>\n",
" <td>NaN</td>\n",
" <td>5980</td>\n",
" <td>NaN</td>\n",
" <td>87807</td>\n",
" <td>45692</td>\n",
" <td>35000</td>\n",
" <td>152809</td>\n",
" <td>62400</td>\n",
" <td>24850</td>\n",
" <td>120994</td>\n",
" <td>...</td>\n",
" <td>46759</td>\n",
" <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>20100531</th>\n",
" <td>NaN</td>\n",
" <td>5400</td>\n",
" <td>NaN</td>\n",
" <td>79444</td>\n",
" <td>53083</td>\n",
" <td>33050</td>\n",
" <td>139916</td>\n",
" <td>55500</td>\n",
" <td>20950</td>\n",
" <td>94523</td>\n",
" <td>...</td>\n",
" <td>50313</td>\n",
" <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",
" </tbody>\n",
"</table>\n",
"<p>4 rows × 435 columns</p>\n",
"</div>"
],
"text/plain": [
"stk_code A000010 A000020 A000030 A000050 A000070 A000080 A000100 A000120 \\\n",
"20100226 NaN 5980 NaN 85146 43452 35550 155674 56100 \n",
"20100331 NaN 6000 NaN 92748 45412 35550 164747 66100 \n",
"20100430 NaN 5980 NaN 87807 45692 35000 152809 62400 \n",
"20100531 NaN 5400 NaN 79444 53083 33050 139916 55500 \n",
"\n",
"stk_code A000140 A000150 ... A120110 A128940 A138930 A139480 A145990 \\\n",
"20100226 20400 106672 ... 40447 NaN NaN NaN NaN \n",
"20100331 23200 126920 ... 43393 NaN NaN NaN NaN \n",
"20100430 24850 120994 ... 46759 NaN NaN NaN NaN \n",
"20100531 20950 94523 ... 50313 NaN NaN NaN NaN \n",
"\n",
"stk_code A161390 A161890 A170900 A185750 A192820 \n",
"20100226 NaN NaN NaN NaN NaN \n",
"20100331 NaN NaN NaN NaN NaN \n",
"20100430 NaN NaN NaN NaN NaN \n",
"20100531 NaN NaN NaN NaN NaN \n",
"\n",
"[4 rows x 435 columns]"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_factors[\"수정주가\"][\"20100131\":\"20100531\"] # use label index"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>stk_code</th>\n",
" <th>A000010</th>\n",
" <th>A000020</th>\n",
" <th>A000030</th>\n",
" <th>A000050</th>\n",
" <th>A000070</th>\n",
" <th>A000080</th>\n",
" <th>A000100</th>\n",
" <th>A000120</th>\n",
" <th>A000140</th>\n",
" <th>A000150</th>\n",
" <th>...</th>\n",
" <th>A120110</th>\n",
" <th>A128940</th>\n",
" <th>A138930</th>\n",
" <th>A139480</th>\n",
" <th>A145990</th>\n",
" <th>A161390</th>\n",
" <th>A161890</th>\n",
" <th>A170900</th>\n",
" <th>A185750</th>\n",
" <th>A192820</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>20011130</th>\n",
" <td>3330</td>\n",
" <td>1940</td>\n",
" <td>865</td>\n",
" <td>16077</td>\n",
" <td>16533</td>\n",
" <td>3330</td>\n",
" <td>38878</td>\n",
" <td>4806</td>\n",
" <td>55006</td>\n",
" <td>19458</td>\n",
" <td>...</td>\n",
" <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>20011228</th>\n",
" <td>4140</td>\n",
" <td>1838</td>\n",
" <td>865</td>\n",
" <td>16149</td>\n",
" <td>17195</td>\n",
" <td>3180</td>\n",
" <td>41133</td>\n",
" <td>4102</td>\n",
" <td>57463</td>\n",
" <td>19408</td>\n",
" <td>...</td>\n",
" <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>20020131</th>\n",
" <td>5550</td>\n",
" <td>1922</td>\n",
" <td>865</td>\n",
" <td>20908</td>\n",
" <td>22485</td>\n",
" <td>3470</td>\n",
" <td>42120</td>\n",
" <td>6192</td>\n",
" <td>69319</td>\n",
" <td>22125</td>\n",
" <td>...</td>\n",
" <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>20010131</th>\n",
" <td>2685</td>\n",
" <td>1603</td>\n",
" <td>865</td>\n",
" <td>18817</td>\n",
" <td>10034</td>\n",
" <td>2900</td>\n",
" <td>24809</td>\n",
" <td>7216</td>\n",
" <td>40694</td>\n",
" <td>16989</td>\n",
" <td>...</td>\n",
" <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>20010228</th>\n",
" <td>2545</td>\n",
" <td>1661</td>\n",
" <td>865</td>\n",
" <td>18889</td>\n",
" <td>11557</td>\n",
" <td>3100</td>\n",
" <td>25374</td>\n",
" <td>7063</td>\n",
" <td>47530</td>\n",
" <td>18766</td>\n",
" <td>...</td>\n",
" <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>20010330</th>\n",
" <td>1900</td>\n",
" <td>1410</td>\n",
" <td>865</td>\n",
" <td>16005</td>\n",
" <td>11665</td>\n",
" <td>3450</td>\n",
" <td>25500</td>\n",
" <td>6360</td>\n",
" <td>46996</td>\n",
" <td>17482</td>\n",
" <td>...</td>\n",
" <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",
" </tbody>\n",
"</table>\n",
"<p>6 rows × 435 columns</p>\n",
"</div>"
],
"text/plain": [
"stk_code A000010 A000020 A000030 A000050 A000070 A000080 A000100 A000120 \\\n",
"20011130 3330 1940 865 16077 16533 3330 38878 4806 \n",
"20011228 4140 1838 865 16149 17195 3180 41133 4102 \n",
"20020131 5550 1922 865 20908 22485 3470 42120 6192 \n",
"20010131 2685 1603 865 18817 10034 2900 24809 7216 \n",
"20010228 2545 1661 865 18889 11557 3100 25374 7063 \n",
"20010330 1900 1410 865 16005 11665 3450 25500 6360 \n",
"\n",
"stk_code A000140 A000150 ... A120110 A128940 A138930 A139480 A145990 \\\n",
"20011130 55006 19458 ... NaN NaN NaN NaN NaN \n",
"20011228 57463 19408 ... NaN NaN NaN NaN NaN \n",
"20020131 69319 22125 ... NaN NaN NaN NaN NaN \n",
"20010131 40694 16989 ... NaN NaN NaN NaN NaN \n",
"20010228 47530 18766 ... NaN NaN NaN NaN NaN \n",
"20010330 46996 17482 ... NaN NaN NaN NaN NaN \n",
"\n",
"stk_code A161390 A161890 A170900 A185750 A192820 \n",
"20011130 NaN NaN NaN NaN NaN \n",
"20011228 NaN NaN NaN NaN NaN \n",
"20020131 NaN NaN NaN NaN NaN \n",
"20010131 NaN NaN NaN NaN NaN \n",
"20010228 NaN NaN NaN NaN NaN \n",
"20010330 NaN NaN NaN NaN NaN \n",
"\n",
"[6 rows x 435 columns]"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.concat([df_factors[\"수정주가\"].iloc[10:13], df_factors[\"수정주가\"].iloc[0:3]])# .sort_index()"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>stk_code</th>\n",
" <th>A000010</th>\n",
" <th>A000020</th>\n",
" <th>A000030</th>\n",
" <th>A000050</th>\n",
" <th>A000070</th>\n",
" <th>A000080</th>\n",
" <th>A000100</th>\n",
" <th>A000120</th>\n",
" <th>A000140</th>\n",
" <th>A000150</th>\n",
" <th>...</th>\n",
" <th>A120110</th>\n",
" <th>A128940</th>\n",
" <th>A138930</th>\n",
" <th>A139480</th>\n",
" <th>A145990</th>\n",
" <th>A161390</th>\n",
" <th>A161890</th>\n",
" <th>A170900</th>\n",
" <th>A185750</th>\n",
" <th>A192820</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>20020131</th>\n",
" <td>5550</td>\n",
" <td>1922</td>\n",
" <td>865</td>\n",
" <td>20908</td>\n",
" <td>22485</td>\n",
" <td>3470</td>\n",
" <td>42120</td>\n",
" <td>6192</td>\n",
" <td>69319</td>\n",
" <td>22125</td>\n",
" <td>...</td>\n",
" <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>20010131</th>\n",
" <td>2685</td>\n",
" <td>1603</td>\n",
" <td>865</td>\n",
" <td>18817</td>\n",
" <td>10034</td>\n",
" <td>2900</td>\n",
" <td>24809</td>\n",
" <td>7216</td>\n",
" <td>40694</td>\n",
" <td>16989</td>\n",
" <td>...</td>\n",
" <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>20010228</th>\n",
" <td>2545</td>\n",
" <td>1661</td>\n",
" <td>865</td>\n",
" <td>18889</td>\n",
" <td>11557</td>\n",
" <td>3100</td>\n",
" <td>25374</td>\n",
" <td>7063</td>\n",
" <td>47530</td>\n",
" <td>18766</td>\n",
" <td>...</td>\n",
" <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",
" </tbody>\n",
"</table>\n",
"<p>3 rows × 435 columns</p>\n",
"</div>"
],
"text/plain": [
"stk_code A000010 A000020 A000030 A000050 A000070 A000080 A000100 A000120 \\\n",
"20020131 5550 1922 865 20908 22485 3470 42120 6192 \n",
"20010131 2685 1603 865 18817 10034 2900 24809 7216 \n",
"20010228 2545 1661 865 18889 11557 3100 25374 7063 \n",
"\n",
"stk_code A000140 A000150 ... A120110 A128940 A138930 A139480 A145990 \\\n",
"20020131 69319 22125 ... NaN NaN NaN NaN NaN \n",
"20010131 40694 16989 ... NaN NaN NaN NaN NaN \n",
"20010228 47530 18766 ... NaN NaN NaN NaN NaN \n",
"\n",
"stk_code A161390 A161890 A170900 A185750 A192820 \n",
"20020131 NaN NaN NaN NaN NaN \n",
"20010131 NaN NaN NaN NaN NaN \n",
"20010228 NaN NaN NaN NaN NaN \n",
"\n",
"[3 rows x 435 columns]"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.concat([df_factors[\"수정주가\"].iloc[10:13], df_factors[\"수정주가\"].iloc[0:3]])[\"20020131\":\"20010228\"]"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>stk_code</th>\n",
" <th>A000010</th>\n",
" <th>A000020</th>\n",
" <th>A000030</th>\n",
" <th>A000050</th>\n",
" <th>A000070</th>\n",
" <th>A000080</th>\n",
" <th>A000100</th>\n",
" <th>A000120</th>\n",
" <th>A000140</th>\n",
" <th>A000150</th>\n",
" <th>...</th>\n",
" <th>A120110</th>\n",
" <th>A128940</th>\n",
" <th>A138930</th>\n",
" <th>A139480</th>\n",
" <th>A145990</th>\n",
" <th>A161390</th>\n",
" <th>A161890</th>\n",
" <th>A170900</th>\n",
" <th>A185750</th>\n",
" <th>A192820</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>20100226</th>\n",
" <td>NaN</td>\n",
" <td>5980</td>\n",
" <td>NaN</td>\n",
" <td>85146</td>\n",
" <td>43452</td>\n",
" <td>35550</td>\n",
" <td>155674</td>\n",
" <td>56100</td>\n",
" <td>20400</td>\n",
" <td>106672</td>\n",
" <td>...</td>\n",
" <td>40447</td>\n",
" <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>20100331</th>\n",
" <td>NaN</td>\n",
" <td>6000</td>\n",
" <td>NaN</td>\n",
" <td>92748</td>\n",
" <td>45412</td>\n",
" <td>35550</td>\n",
" <td>164747</td>\n",
" <td>66100</td>\n",
" <td>23200</td>\n",
" <td>126920</td>\n",
" <td>...</td>\n",
" <td>43393</td>\n",
" <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>20100430</th>\n",
" <td>NaN</td>\n",
" <td>5980</td>\n",
" <td>NaN</td>\n",
" <td>87807</td>\n",
" <td>45692</td>\n",
" <td>35000</td>\n",
" <td>152809</td>\n",
" <td>62400</td>\n",
" <td>24850</td>\n",
" <td>120994</td>\n",
" <td>...</td>\n",
" <td>46759</td>\n",
" <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>20100531</th>\n",
" <td>NaN</td>\n",
" <td>5400</td>\n",
" <td>NaN</td>\n",
" <td>79444</td>\n",
" <td>53083</td>\n",
" <td>33050</td>\n",
" <td>139916</td>\n",
" <td>55500</td>\n",
" <td>20950</td>\n",
" <td>94523</td>\n",
" <td>...</td>\n",
" <td>50313</td>\n",
" <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",
" </tbody>\n",
"</table>\n",
"<p>4 rows × 435 columns</p>\n",
"</div>"
],
"text/plain": [
"stk_code A000010 A000020 A000030 A000050 A000070 A000080 A000100 A000120 \\\n",
"20100226 NaN 5980 NaN 85146 43452 35550 155674 56100 \n",
"20100331 NaN 6000 NaN 92748 45412 35550 164747 66100 \n",
"20100430 NaN 5980 NaN 87807 45692 35000 152809 62400 \n",
"20100531 NaN 5400 NaN 79444 53083 33050 139916 55500 \n",
"\n",
"stk_code A000140 A000150 ... A120110 A128940 A138930 A139480 A145990 \\\n",
"20100226 20400 106672 ... 40447 NaN NaN NaN NaN \n",
"20100331 23200 126920 ... 43393 NaN NaN NaN NaN \n",
"20100430 24850 120994 ... 46759 NaN NaN NaN NaN \n",
"20100531 20950 94523 ... 50313 NaN NaN NaN NaN \n",
"\n",
"stk_code A161390 A161890 A170900 A185750 A192820 \n",
"20100226 NaN NaN NaN NaN NaN \n",
"20100331 NaN NaN NaN NaN NaN \n",
"20100430 NaN NaN NaN NaN NaN \n",
"20100531 NaN NaN NaN NaN NaN \n",
"\n",
"[4 rows x 435 columns]"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_factors[\"수정주가\"][\"20100131\":\"20100531\"].head()"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"35000.0"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_factors[\"수정주가\"][\"20100131\":\"20100531\"].loc[\"20100430\", \"A000080\"] # selection by label"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"20100430 35000\n",
"20100531 33050\n",
"Name: A000080, dtype: object"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_factors[\"수정주가\"][\"20100131\":\"20100531\"].loc[\"20100430\":\"20100531\", \"A000080\"]"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"5980.0"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_factors[\"수정주가\"][\"20100131\":\"20100531\"].iloc[0, 1] # selection by position"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Settings"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>dt</th>\n",
" <th>stk_cd</th>\n",
" <th>open_prc</th>\n",
" <th>high_prc</th>\n",
" <th>low_prc</th>\n",
" <th>close_prc</th>\n",
" <th>trd_amt</th>\n",
" <th>marketvalue</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>19900103</td>\n",
" <td>10</td>\n",
" <td>1.000000</td>\n",
" <td>49176.787946</td>\n",
" <td>45664.160236</td>\n",
" <td>49176.787946</td>\n",
" <td>2641758000</td>\n",
" <td>1540000000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>19900104</td>\n",
" <td>10</td>\n",
" <td>1.000000</td>\n",
" <td>51284.364573</td>\n",
" <td>48474.262404</td>\n",
" <td>51284.364573</td>\n",
" <td>5963383000</td>\n",
" <td>1606000000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>19900105</td>\n",
" <td>10</td>\n",
" <td>51284.364573</td>\n",
" <td>51986.890115</td>\n",
" <td>50230.576259</td>\n",
" <td>50581.839030</td>\n",
" <td>4896930000</td>\n",
" <td>1584000000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>19900106</td>\n",
" <td>10</td>\n",
" <td>50581.839030</td>\n",
" <td>51284.364573</td>\n",
" <td>49879.313488</td>\n",
" <td>50230.576259</td>\n",
" <td>2227277000</td>\n",
" <td>1573000000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>19900108</td>\n",
" <td>10</td>\n",
" <td>50230.576259</td>\n",
" <td>50581.839030</td>\n",
" <td>49528.050717</td>\n",
" <td>50230.576259</td>\n",
" <td>3057359000</td>\n",
" <td>1573000000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" dt stk_cd open_prc high_prc low_prc close_prc \\\n",
"0 19900103 10 1.000000 49176.787946 45664.160236 49176.787946 \n",
"1 19900104 10 1.000000 51284.364573 48474.262404 51284.364573 \n",
"2 19900105 10 51284.364573 51986.890115 50230.576259 50581.839030 \n",
"3 19900106 10 50581.839030 51284.364573 49879.313488 50230.576259 \n",
"4 19900108 10 50230.576259 50581.839030 49528.050717 50230.576259 \n",
"\n",
" trd_amt marketvalue \n",
"0 2641758000 1540000000000 \n",
"1 5963383000 1606000000000 \n",
"2 4896930000 1584000000000 \n",
"3 2227277000 1573000000000 \n",
"4 3057359000 1573000000000 "
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"_df_kospi200 = df_kospi200.copy()\n",
"_df_kospi200.loc[_df_kospi200.close_prc > _df_kospi200.open_prc, \"open_prc\"] = 1\n",
"_df_kospi200.head()"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>dt</th>\n",
" <th>stk_cd</th>\n",
" <th>open_prc</th>\n",
" <th>high_prc</th>\n",
" <th>low_prc</th>\n",
" <th>close_prc</th>\n",
" <th>trd_amt</th>\n",
" <th>marketvalue</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>19900103</td>\n",
" <td>10</td>\n",
" <td>1.000000</td>\n",
" <td>49176.787946</td>\n",
" <td>45664.160236</td>\n",
" <td>49176.787946</td>\n",
" <td>2641758000</td>\n",
" <td>1540000000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>19900104</td>\n",
" <td>10</td>\n",
" <td>1.000000</td>\n",
" <td>51284.364573</td>\n",
" <td>48474.262404</td>\n",
" <td>51284.364573</td>\n",
" <td>5963383000</td>\n",
" <td>1606000000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>19900105</td>\n",
" <td>10</td>\n",
" <td>51284.364573</td>\n",
" <td>1000.000000</td>\n",
" <td>50230.576259</td>\n",
" <td>50581.839030</td>\n",
" <td>4896930000</td>\n",
" <td>1584000000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>19900106</td>\n",
" <td>10</td>\n",
" <td>50581.839030</td>\n",
" <td>51284.364573</td>\n",
" <td>49879.313488</td>\n",
" <td>50230.576259</td>\n",
" <td>2227277000</td>\n",
" <td>1573000000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>19900108</td>\n",
" <td>10</td>\n",
" <td>50230.576259</td>\n",
" <td>50581.839030</td>\n",
" <td>49528.050717</td>\n",
" <td>50230.576259</td>\n",
" <td>3057359000</td>\n",
" <td>1573000000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" dt stk_cd open_prc high_prc low_prc close_prc \\\n",
"0 19900103 10 1.000000 49176.787946 45664.160236 49176.787946 \n",
"1 19900104 10 1.000000 51284.364573 48474.262404 51284.364573 \n",
"2 19900105 10 51284.364573 1000.000000 50230.576259 50581.839030 \n",
"3 19900106 10 50581.839030 51284.364573 49879.313488 50230.576259 \n",
"4 19900108 10 50230.576259 50581.839030 49528.050717 50230.576259 \n",
"\n",
" trd_amt marketvalue \n",
"0 2641758000 1540000000000 \n",
"1 5963383000 1606000000000 \n",
"2 4896930000 1584000000000 \n",
"3 2227277000 1573000000000 \n",
"4 3057359000 1573000000000 "
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"_df_kospi200.iloc[2, 3] = 1000 # 0-index\n",
"_df_kospi200.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Missing "
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>err_fy1</th>\n",
" <th>err_fy2</th>\n",
" <th>sales_12m_fwd_1m_chg</th>\n",
" <th>op_12m_fwd_1m_chg</th>\n",
" <th>eps_12m_fwd_1m_chg</th>\n",
" <th>cfo_12m_fwd_1m_chg</th>\n",
" <th>fcf_12m_fwd_1m_chg</th>\n",
" <th>sales_fy1_1m_chg</th>\n",
" <th>op_fy1_1m_chg</th>\n",
" <th>eps_fy1_1m_chg</th>\n",
" <th>...</th>\n",
" <th>WI26업종명(대)</th>\n",
" <th>WI26업종명(중)</th>\n",
" <th>WICS업종명(대)</th>\n",
" <th>WICS업종명(중)</th>\n",
" <th>KOSPI200_구성종목여부</th>\n",
" <th>시가총액규모별</th>\n",
" <th>최대주주보유보통주지분율</th>\n",
" <th>투자유의구분</th>\n",
" <th>관리종목여부</th>\n",
" <th>거래정지여부</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>20010131</th>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>3.6374e+13</td>\n",
" <td>6.05e+12</td>\n",
" <td>27253</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>3.60748e+13</td>\n",
" <td>6.02391e+12</td>\n",
" <td>27138</td>\n",
" <td>...</td>\n",
" <td>반도체</td>\n",
" <td>반도체</td>\n",
" <td>IT</td>\n",
" <td>반도체와반도체장비</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>17.64</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20010228</th>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>3.67863e+13</td>\n",
" <td>6.07566e+12</td>\n",
" <td>27287</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>3.62004e+13</td>\n",
" <td>5.97763e+12</td>\n",
" <td>26894</td>\n",
" <td>...</td>\n",
" <td>반도체</td>\n",
" <td>반도체</td>\n",
" <td>IT</td>\n",
" <td>반도체와반도체장비</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>17.64</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>2 rows × 157 columns</p>\n",
"</div>"
],
"text/plain": [
" err_fy1 err_fy2 sales_12m_fwd_1m_chg op_12m_fwd_1m_chg \\\n",
"20010131 NaN 0 3.6374e+13 6.05e+12 \n",
"20010228 NaN 0 3.67863e+13 6.07566e+12 \n",
"\n",
" eps_12m_fwd_1m_chg cfo_12m_fwd_1m_chg fcf_12m_fwd_1m_chg \\\n",
"20010131 27253 NaN NaN \n",
"20010228 27287 NaN NaN \n",
"\n",
" sales_fy1_1m_chg op_fy1_1m_chg eps_fy1_1m_chg ... WI26업종명(대) \\\n",
"20010131 3.60748e+13 6.02391e+12 27138 ... 반도체 \n",
"20010228 3.62004e+13 5.97763e+12 26894 ... 반도체 \n",
"\n",
" WI26업종명(중) WICS업종명(대) WICS업종명(중) KOSPI200_구성종목여부 시가총액규모별 \\\n",
"20010131 반도체 IT 반도체와반도체장비 1 1 \n",
"20010228 반도체 IT 반도체와반도체장비 1 1 \n",
"\n",
" 최대주주보유보통주지분율 투자유의구분 관리종목여부 거래정지여부 \n",
"20010131 17.64 0 0 0 \n",
"20010228 17.64 0 0 0 \n",
"\n",
"[2 rows x 157 columns]"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"_df_samsung = df_factors[:, :, \"A005930\"]\n",
"_df_samsung.head(2)"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>err_fy2</th>\n",
" <th>sales_12m_fwd_1m_chg</th>\n",
" <th>op_12m_fwd_1m_chg</th>\n",
" <th>eps_12m_fwd_1m_chg</th>\n",
" <th>sales_fy1_1m_chg</th>\n",
" <th>op_fy1_1m_chg</th>\n",
" <th>eps_fy1_1m_chg</th>\n",
" <th>sales_fy2_1m_chg</th>\n",
" <th>op_fy2_1m_chg</th>\n",
" <th>eps_fy2_1m_chg</th>\n",
" <th>...</th>\n",
" <th>WI26업종명(대)</th>\n",
" <th>WI26업종명(중)</th>\n",
" <th>WICS업종명(대)</th>\n",
" <th>WICS업종명(중)</th>\n",
" <th>KOSPI200_구성종목여부</th>\n",
" <th>시가총액규모별</th>\n",
" <th>최대주주보유보통주지분율</th>\n",
" <th>투자유의구분</th>\n",
" <th>관리종목여부</th>\n",
" <th>거래정지여부</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>20010131</th>\n",
" <td>0</td>\n",
" <td>3.6374e+13</td>\n",
" <td>6.05e+12</td>\n",
" <td>27253</td>\n",
" <td>3.60748e+13</td>\n",
" <td>6.02391e+12</td>\n",
" <td>27138</td>\n",
" <td>3.96642e+13</td>\n",
" <td>6.3369e+12</td>\n",
" <td>28371</td>\n",
" <td>...</td>\n",
" <td>반도체</td>\n",
" <td>반도체</td>\n",
" <td>IT</td>\n",
" <td>반도체와반도체장비</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>17.64</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20010228</th>\n",
" <td>0</td>\n",
" <td>3.67863e+13</td>\n",
" <td>6.07566e+12</td>\n",
" <td>27287</td>\n",
" <td>3.62004e+13</td>\n",
" <td>5.97763e+12</td>\n",
" <td>26894</td>\n",
" <td>3.97153e+13</td>\n",
" <td>6.56578e+12</td>\n",
" <td>29097</td>\n",
" <td>...</td>\n",
" <td>반도체</td>\n",
" <td>반도체</td>\n",
" <td>IT</td>\n",
" <td>반도체와반도체장비</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>17.64</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>2 rows × 60 columns</p>\n",
"</div>"
],
"text/plain": [
" err_fy2 sales_12m_fwd_1m_chg op_12m_fwd_1m_chg eps_12m_fwd_1m_chg \\\n",
"20010131 0 3.6374e+13 6.05e+12 27253 \n",
"20010228 0 3.67863e+13 6.07566e+12 27287 \n",
"\n",
" sales_fy1_1m_chg op_fy1_1m_chg eps_fy1_1m_chg sales_fy2_1m_chg \\\n",
"20010131 3.60748e+13 6.02391e+12 27138 3.96642e+13 \n",
"20010228 3.62004e+13 5.97763e+12 26894 3.97153e+13 \n",
"\n",
" op_fy2_1m_chg eps_fy2_1m_chg ... WI26업종명(대) WI26업종명(중) WICS업종명(대) \\\n",
"20010131 6.3369e+12 28371 ... 반도체 반도체 IT \n",
"20010228 6.56578e+12 29097 ... 반도체 반도체 IT \n",
"\n",
" WICS업종명(중) KOSPI200_구성종목여부 시가총액규모별 최대주주보유보통주지분율 투자유의구분 관리종목여부 거래정지여부 \n",
"20010131 반도체와반도체장비 1 1 17.64 0 0 0 \n",
"20010228 반도체와반도체장비 1 1 17.64 0 0 0 \n",
"\n",
"[2 rows x 60 columns]"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# thresh non-NA\n",
"_df_samsung.dropna(how=\"any\", axis=1).head(2) # looping through axis=1(columns)"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"collapsed": false
},
"outputs": [
{
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"text/plain": [
"<IPython.core.display.Image object>"
]
},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from IPython.display import Image\n",
"Image(filename='img/axis.jpg')"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"collapsed": false
},
"outputs": [
{
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" err_fy1 err_fy2 sales_12m_fwd_1m_chg op_12m_fwd_1m_chg \\\n",
"20010131 0.0 0.0 3.637395e+13 6.049996e+12 \n",
"20010228 0.0 0.0 3.678625e+13 6.075658e+12 \n",
"\n",
" eps_12m_fwd_1m_chg cfo_12m_fwd_1m_chg fcf_12m_fwd_1m_chg \\\n",
"20010131 27253.0 3.607484e+13 3.607484e+13 \n",
"20010228 27287.0 3.620044e+13 3.620044e+13 \n",
"\n",
" sales_fy1_1m_chg op_fy1_1m_chg eps_fy1_1m_chg ... WI26업종명(대) \\\n",
"20010131 3.607484e+13 6.023914e+12 27138.0 ... 반도체 \n",
"20010228 3.620044e+13 5.977635e+12 26894.0 ... 반도체 \n",
"\n",
" WI26업종명(중) WICS업종명(대) WICS업종명(중) KOSPI200_구성종목여부 시가총액규모별 \\\n",
"20010131 반도체 IT 반도체와반도체장비 1 1 \n",
"20010228 반도체 IT 반도체와반도체장비 1 1 \n",
"\n",
" 최대주주보유보통주지분율 투자유의구분 관리종목여부 거래정지여부 \n",
"20010131 17.64 0.0 0.0 0.0 \n",
"20010228 17.64 0.0 0.0 0.0 \n",
"\n",
"[2 rows x 157 columns]"
]
},
"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"_df_samsung.fillna(axis=1, method=\"backfill\", limit=3).head(2)"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"collapsed": false
},
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{
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" err_fy1 err_fy2 sales_12m_fwd_1m_chg op_12m_fwd_1m_chg \\\n",
"20010131 25.0 0.0 3.637395e+13 6.049996e+12 \n",
"20010228 25.0 0.0 3.678625e+13 6.075658e+12 \n",
"\n",
" eps_12m_fwd_1m_chg cfo_12m_fwd_1m_chg fcf_12m_fwd_1m_chg \\\n",
"20010131 27253.0 NaN 4.024543e+12 \n",
"20010228 27287.0 NaN 4.024543e+12 \n",
"\n",
" sales_fy1_1m_chg op_fy1_1m_chg eps_fy1_1m_chg ... WI26업종명(대) \\\n",
"20010131 3.607484e+13 6.023914e+12 27138.0 ... 반도체 \n",
"20010228 3.620044e+13 5.977635e+12 26894.0 ... 반도체 \n",
"\n",
" WI26업종명(중) WICS업종명(대) WICS업종명(중) KOSPI200_구성종목여부 시가총액규모별 \\\n",
"20010131 반도체 IT 반도체와반도체장비 1 1 \n",
"20010228 반도체 IT 반도체와반도체장비 1 1 \n",
"\n",
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"20010131 17.64 0.0 0.0 0.0 \n",
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"\n",
"[2 rows x 157 columns]"
]
},
"execution_count": 44,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# pad / ffill: propagate last valid observation forward to next valid\n",
"# backfill / bfill: use NEXT valid observation to fill gap\n",
"_df_samsung.fillna(axis=0, method=\"backfill\", limit=30).head(2)"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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],
"text/plain": [
" err_fy1 err_fy2 sales_12m_fwd_1m_chg op_12m_fwd_1m_chg \\\n",
"20010131 True False False False \n",
"20010228 True False False False \n",
"20010330 True False False False \n",
"20010430 True False False False \n",
"20010531 True False False False \n",
"\n",
" eps_12m_fwd_1m_chg cfo_12m_fwd_1m_chg fcf_12m_fwd_1m_chg \\\n",
"20010131 False True True \n",
"20010228 False True True \n",
"20010330 False True True \n",
"20010430 False True True \n",
"20010531 False True True \n",
"\n",
" sales_fy1_1m_chg op_fy1_1m_chg eps_fy1_1m_chg ... WI26업종명(대) \\\n",
"20010131 False False False ... False \n",
"20010228 False False False ... False \n",
"20010330 False False False ... False \n",
"20010430 False False False ... False \n",
"20010531 False False False ... False \n",
"\n",
" WI26업종명(중) WICS업종명(대) WICS업종명(중) KOSPI200_구성종목여부 시가총액규모별 \\\n",
"20010131 False False False False False \n",
"20010228 False False False False False \n",
"20010330 False False False False False \n",
"20010430 False False False False False \n",
"20010531 False False False False False \n",
"\n",
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"20010131 False False False False \n",
"20010228 False False False False \n",
"20010330 False False False False \n",
"20010430 False False False False \n",
"20010531 False False False False \n",
"\n",
"[5 rows x 157 columns]"
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.isnull(_df_samsung).head()"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"capex/depr_trailing#div#a 183\n",
"capex/depr_trailing#div#b 183\n",
"cfo_12m_fwd_1m_chg 124\n",
"공매도_비중_20일 84\n",
"공매도_비중_5일 84\n",
"dtype: int64"
]
},
"execution_count": 46,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.isnull(_df_samsung).sum().sort_values(ascending=False).iloc[:5]"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {
"collapsed": false
},
"outputs": [
{
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" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>...</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",
" <td>True</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20010228</th>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>...</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",
" <td>True</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20010330</th>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>...</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",
" <td>True</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20010430</th>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>...</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",
" <td>True</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20010531</th>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>...</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",
" <td>True</td>\n",
" <td>True</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 157 columns</p>\n",
"</div>"
],
"text/plain": [
" err_fy1 err_fy2 sales_12m_fwd_1m_chg op_12m_fwd_1m_chg \\\n",
"20010131 False True True True \n",
"20010228 False True True True \n",
"20010330 False True True True \n",
"20010430 False True True True \n",
"20010531 False True True True \n",
"\n",
" eps_12m_fwd_1m_chg cfo_12m_fwd_1m_chg fcf_12m_fwd_1m_chg \\\n",
"20010131 True False False \n",
"20010228 True False False \n",
"20010330 True False False \n",
"20010430 True False False \n",
"20010531 True False False \n",
"\n",
" sales_fy1_1m_chg op_fy1_1m_chg eps_fy1_1m_chg ... WI26업종명(대) \\\n",
"20010131 True True True ... True \n",
"20010228 True True True ... True \n",
"20010330 True True True ... True \n",
"20010430 True True True ... True \n",
"20010531 True True True ... True \n",
"\n",
" WI26업종명(중) WICS업종명(대) WICS업종명(중) KOSPI200_구성종목여부 시가총액규모별 \\\n",
"20010131 True True True True True \n",
"20010228 True True True True True \n",
"20010330 True True True True True \n",
"20010430 True True True True True \n",
"20010531 True True True True True \n",
"\n",
" 최대주주보유보통주지분율 투자유의구분 관리종목여부 거래정지여부 \n",
"20010131 True True True True \n",
"20010228 True True True True \n",
"20010330 True True True True \n",
"20010430 True True True True \n",
"20010531 True True True True \n",
"\n",
"[5 rows x 157 columns]"
]
},
"execution_count": 47,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.notnull(_df_samsung).head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Operations"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>A</th>\n",
" <th>B</th>\n",
" <th>C</th>\n",
" <th>D</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2013-01-01</th>\n",
" <td>0.067895</td>\n",
" <td>-0.986715</td>\n",
" <td>0.119895</td>\n",
" <td>1.814497</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-01-02</th>\n",
" <td>0.859147</td>\n",
" <td>-1.313583</td>\n",
" <td>-0.735025</td>\n",
" <td>-0.018618</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-01-03</th>\n",
" <td>1.263115</td>\n",
" <td>0.281992</td>\n",
" <td>1.002828</td>\n",
" <td>-1.627302</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-01-04</th>\n",
" <td>0.029249</td>\n",
" <td>0.091171</td>\n",
" <td>-1.556822</td>\n",
" <td>0.906813</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-01-05</th>\n",
" <td>1.315094</td>\n",
" <td>-0.189322</td>\n",
" <td>-0.006984</td>\n",
" <td>-0.380713</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-01-06</th>\n",
" <td>-0.984644</td>\n",
" <td>-2.086729</td>\n",
" <td>0.979141</td>\n",
" <td>-0.789810</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" A B C D\n",
"2013-01-01 0.067895 -0.986715 0.119895 1.814497\n",
"2013-01-02 0.859147 -1.313583 -0.735025 -0.018618\n",
"2013-01-03 1.263115 0.281992 1.002828 -1.627302\n",
"2013-01-04 0.029249 0.091171 -1.556822 0.906813\n",
"2013-01-05 1.315094 -0.189322 -0.006984 -0.380713\n",
"2013-01-06 -0.984644 -2.086729 0.979141 -0.789810"
]
},
"execution_count": 48,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_sample = pd.DataFrame(np.random.randn(6,4), index=pd.date_range('20130101', periods=6), columns=list('ABCD'))\n",
"df_sample"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"2012-12-31 1.0\n",
"2013-01-01 3.0\n",
"2013-01-02 NaN\n",
"2013-01-03 3.0\n",
"2013-01-04 4.0\n",
"2013-01-05 2.0\n",
"2013-01-06 1.0\n",
"2013-01-07 3.0\n",
"Freq: D, dtype: float64"
]
},
"execution_count": 49,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"series_sample = pd.Series([1,3,np.nan,3,4,2,1,3], index=pd.date_range('20121231', periods=8))\n",
"series_sample"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>A</th>\n",
" <th>B</th>\n",
" <th>C</th>\n",
" <th>D</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2012-12-31</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-01-01</th>\n",
" <td>-2.932105</td>\n",
" <td>-3.986715</td>\n",
" <td>-2.880105</td>\n",
" <td>-1.185503</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-01-02</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-01-03</th>\n",
" <td>-1.736885</td>\n",
" <td>-2.718008</td>\n",
" <td>-1.997172</td>\n",
" <td>-4.627302</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-01-04</th>\n",
" <td>-3.970751</td>\n",
" <td>-3.908829</td>\n",
" <td>-5.556822</td>\n",
" <td>-3.093187</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-01-05</th>\n",
" <td>-0.684906</td>\n",
" <td>-2.189322</td>\n",
" <td>-2.006984</td>\n",
" <td>-2.380713</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-01-06</th>\n",
" <td>-1.984644</td>\n",
" <td>-3.086729</td>\n",
" <td>-0.020859</td>\n",
" <td>-1.789810</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-01-07</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" A B C D\n",
"2012-12-31 NaN NaN NaN NaN\n",
"2013-01-01 -2.932105 -3.986715 -2.880105 -1.185503\n",
"2013-01-02 NaN NaN NaN NaN\n",
"2013-01-03 -1.736885 -2.718008 -1.997172 -4.627302\n",
"2013-01-04 -3.970751 -3.908829 -5.556822 -3.093187\n",
"2013-01-05 -0.684906 -2.189322 -2.006984 -2.380713\n",
"2013-01-06 -1.984644 -3.086729 -0.020859 -1.789810\n",
"2013-01-07 NaN NaN NaN NaN"
]
},
"execution_count": 50,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_sample.sub(series_sample, axis='index')"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>A</th>\n",
" <th>B</th>\n",
" <th>C</th>\n",
" <th>D</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2012-12-31</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-01-01</th>\n",
" <td>0.203685</td>\n",
" <td>-2.960144</td>\n",
" <td>0.359684</td>\n",
" <td>5.443492</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-01-02</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-01-03</th>\n",
" <td>3.789344</td>\n",
" <td>0.845977</td>\n",
" <td>3.008485</td>\n",
" <td>-4.881907</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-01-04</th>\n",
" <td>0.116996</td>\n",
" <td>0.364682</td>\n",
" <td>-6.227287</td>\n",
" <td>3.627252</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-01-05</th>\n",
" <td>2.630189</td>\n",
" <td>-0.378643</td>\n",
" <td>-0.013968</td>\n",
" <td>-0.761426</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-01-06</th>\n",
" <td>-0.984644</td>\n",
" <td>-2.086729</td>\n",
" <td>0.979141</td>\n",
" <td>-0.789810</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-01-07</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" A B C D\n",
"2012-12-31 NaN NaN NaN NaN\n",
"2013-01-01 0.203685 -2.960144 0.359684 5.443492\n",
"2013-01-02 NaN NaN NaN NaN\n",
"2013-01-03 3.789344 0.845977 3.008485 -4.881907\n",
"2013-01-04 0.116996 0.364682 -6.227287 3.627252\n",
"2013-01-05 2.630189 -0.378643 -0.013968 -0.761426\n",
"2013-01-06 -0.984644 -2.086729 0.979141 -0.789810\n",
"2013-01-07 NaN NaN NaN NaN"
]
},
"execution_count": 51,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_sample.mul(series_sample, axis='index')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Pivot/Unpivot(melt)"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>asset_cd</th>\n",
" <th>in_dt</th>\n",
" <th>out_dt</th>\n",
" <th>ret</th>\n",
" <th>trd_dt</th>\n",
" <th>weight</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>A</td>\n",
" <td>2011-01-01</td>\n",
" <td>2011-01-03</td>\n",
" <td>1.2</td>\n",
" <td>2011-01-01</td>\n",
" <td>0.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>A</td>\n",
" <td>2011-01-01</td>\n",
" <td>2011-01-03</td>\n",
" <td>0.8</td>\n",
" <td>2011-01-02</td>\n",
" <td>0.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>A</td>\n",
" <td>2011-01-01</td>\n",
" <td>2011-01-03</td>\n",
" <td>0.6</td>\n",
" <td>2011-01-03</td>\n",
" <td>0.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>B</td>\n",
" <td>2011-01-01</td>\n",
" <td>2011-01-03</td>\n",
" <td>0.8</td>\n",
" <td>2011-01-01</td>\n",
" <td>0.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>B</td>\n",
" <td>2011-01-01</td>\n",
" <td>2011-01-03</td>\n",
" <td>1.5</td>\n",
" <td>2011-01-02</td>\n",
" <td>0.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>B</td>\n",
" <td>2011-01-01</td>\n",
" <td>2011-01-03</td>\n",
" <td>1.8</td>\n",
" <td>2011-01-03</td>\n",
" <td>0.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>C</td>\n",
" <td>2011-01-01</td>\n",
" <td>2011-01-03</td>\n",
" <td>1.5</td>\n",
" <td>2011-01-01</td>\n",
" <td>0.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>C</td>\n",
" <td>2011-01-01</td>\n",
" <td>2011-01-03</td>\n",
" <td>1.0</td>\n",
" <td>2011-01-02</td>\n",
" <td>0.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>C</td>\n",
" <td>2011-01-01</td>\n",
" <td>2011-01-03</td>\n",
" <td>2.0</td>\n",
" <td>2011-01-03</td>\n",
" <td>0.5</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" asset_cd in_dt out_dt ret trd_dt weight\n",
"0 A 2011-01-01 2011-01-03 1.2 2011-01-01 0.2\n",
"1 A 2011-01-01 2011-01-03 0.8 2011-01-02 0.2\n",
"2 A 2011-01-01 2011-01-03 0.6 2011-01-03 0.2\n",
"3 B 2011-01-01 2011-01-03 0.8 2011-01-01 0.3\n",
"4 B 2011-01-01 2011-01-03 1.5 2011-01-02 0.3\n",
"5 B 2011-01-01 2011-01-03 1.8 2011-01-03 0.3\n",
"6 C 2011-01-01 2011-01-03 1.5 2011-01-01 0.5\n",
"7 C 2011-01-01 2011-01-03 1.0 2011-01-02 0.5\n",
"8 C 2011-01-01 2011-01-03 2.0 2011-01-03 0.5"
]
},
"execution_count": 52,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_stk_rtn = pd.DataFrame({\n",
" \"in_dt\": list(pd.date_range(start=\"2011-01-01\", end=\"2011-01-01\", freq=\"D\"))*9, \n",
" \"out_dt\": list(pd.date_range(start=\"2011-01-03\", end=\"2011-01-03\", freq=\"D\"))*9, \n",
" \"trd_dt\": list(pd.date_range(start=\"2011-01-01\", end=\"2011-01-03\", freq=\"D\")) * 3, \n",
" \"asset_cd\": list(\"A\")*3 + list(\"B\")*3 + list(\"C\")*3, \n",
" \"ret\": [1.2, 0.8, 0.6, 0.8, 1.5, 1.8, 1.5, 1, 2],\n",
" \"weight\": [0.2] * 3 + [0.3] * 3 + [0.5] * 3\n",
" })\n",
"df_stk_rtn"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>asset_cd</th>\n",
" <th>A</th>\n",
" <th>B</th>\n",
" <th>C</th>\n",
" </tr>\n",
" <tr>\n",
" <th>trd_dt</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2011-01-01</th>\n",
" <td>1.2</td>\n",
" <td>0.8</td>\n",
" <td>1.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-01-02</th>\n",
" <td>0.8</td>\n",
" <td>1.5</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-01-03</th>\n",
" <td>0.6</td>\n",
" <td>1.8</td>\n",
" <td>2.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"asset_cd A B C\n",
"trd_dt \n",
"2011-01-01 1.2 0.8 1.5\n",
"2011-01-02 0.8 1.5 1.0\n",
"2011-01-03 0.6 1.8 2.0"
]
},
"execution_count": 53,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_stk_rtn.pivot(columns=\"asset_cd\", index=\"trd_dt\", values=\"ret\")"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {
"collapsed": false
},
"outputs": [
{
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"stk_cd 10 20 30 50 60 \\\n",
"dt \n",
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"19900104 51284.364573 2475.843948 103671.614735 24652.942193 1971.250398 \n",
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"dt \n",
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"\n",
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"dt ... \n",
"19900103 ... NaN NaN NaN NaN NaN NaN NaN \n",
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"\n",
"stk_cd 170900 185750 192820 \n",
"dt \n",
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"\n",
"[5 rows x 553 columns]"
]
},
"execution_count": 54,
"metadata": {},
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}
],
"source": [
"_df_kospi200_pivot = df_kospi200.pivot(columns=\"stk_cd\", index=\"dt\", values=\"close_prc\")\n",
"_df_kospi200_pivot.head()"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {
"collapsed": false
},
"outputs": [
{
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" <td>0.839933</td>\n",
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" <td>0.869473</td>\n",
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" <td>0.852407</td>\n",
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" <td>-0.170594</td>\n",
" <td>0.735273</td>\n",
" <td>-0.557629</td>\n",
" <td>0.830865</td>\n",
" <td>0.491259</td>\n",
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" <td>0.781931</td>\n",
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" <td>0.324212</td>\n",
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" <td>-0.296099</td>\n",
" <td>-0.163580</td>\n",
" <td>-0.464950</td>\n",
" <td>0.383133</td>\n",
" <td>0.400815</td>\n",
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" <td>0.885252</td>\n",
" <td>0.850210</td>\n",
" <td>0.743568</td>\n",
" <td>1.000000</td>\n",
" <td>-0.663923</td>\n",
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" <td>0.867829</td>\n",
" <td>0.120040</td>\n",
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" <td>0.749193</td>\n",
" <td>-0.512790</td>\n",
" <td>0.802640</td>\n",
" <td>0.696252</td>\n",
" <td>0.648073</td>\n",
" <td>0.881900</td>\n",
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" <td>0.175942</td>\n",
" <td>-0.672865</td>\n",
" <td>-0.291008</td>\n",
" <td>...</td>\n",
" <td>0.087802</td>\n",
" <td>0.221759</td>\n",
" <td>-0.028779</td>\n",
" <td>-0.325763</td>\n",
" <td>0.403932</td>\n",
" <td>-0.673631</td>\n",
" <td>0.297081</td>\n",
" <td>0.608014</td>\n",
" <td>0.469304</td>\n",
" <td>0.736348</td>\n",
" </tr>\n",
" <tr>\n",
" <th>150</th>\n",
" <td>-0.221537</td>\n",
" <td>0.775545</td>\n",
" <td>-0.433234</td>\n",
" <td>0.745113</td>\n",
" <td>0.873577</td>\n",
" <td>0.705611</td>\n",
" <td>0.793304</td>\n",
" <td>0.844883</td>\n",
" <td>-0.248820</td>\n",
" <td>0.627005</td>\n",
" <td>...</td>\n",
" <td>-0.613404</td>\n",
" <td>0.060853</td>\n",
" <td>0.577100</td>\n",
" <td>0.532723</td>\n",
" <td>-0.693377</td>\n",
" <td>0.653461</td>\n",
" <td>-0.808503</td>\n",
" <td>-0.090421</td>\n",
" <td>-0.510247</td>\n",
" <td>-0.621747</td>\n",
" </tr>\n",
" <tr>\n",
" <th>160</th>\n",
" <td>0.642274</td>\n",
" <td>0.616531</td>\n",
" <td>0.421451</td>\n",
" <td>0.783833</td>\n",
" <td>0.685690</td>\n",
" <td>0.719433</td>\n",
" <td>0.845196</td>\n",
" <td>0.420190</td>\n",
" <td>0.506083</td>\n",
" <td>0.934610</td>\n",
" <td>...</td>\n",
" <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>170</th>\n",
" <td>0.611898</td>\n",
" <td>0.597875</td>\n",
" <td>0.449274</td>\n",
" <td>0.740640</td>\n",
" <td>0.684633</td>\n",
" <td>0.392131</td>\n",
" <td>0.628631</td>\n",
" <td>0.370227</td>\n",
" <td>0.447526</td>\n",
" <td>0.667140</td>\n",
" <td>...</td>\n",
" <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>180</th>\n",
" <td>0.574288</td>\n",
" <td>0.281059</td>\n",
" <td>0.315596</td>\n",
" <td>0.084973</td>\n",
" <td>0.012652</td>\n",
" <td>0.054642</td>\n",
" <td>0.202014</td>\n",
" <td>-0.075128</td>\n",
" <td>0.454198</td>\n",
" <td>0.328178</td>\n",
" <td>...</td>\n",
" <td>0.770652</td>\n",
" <td>0.041065</td>\n",
" <td>-0.441121</td>\n",
" <td>-0.525813</td>\n",
" <td>0.844431</td>\n",
" <td>-0.597002</td>\n",
" <td>0.824228</td>\n",
" <td>0.591372</td>\n",
" <td>0.562570</td>\n",
" <td>0.751206</td>\n",
" </tr>\n",
" <tr>\n",
" <th>200</th>\n",
" <td>0.836672</td>\n",
" <td>0.030877</td>\n",
" <td>0.814642</td>\n",
" <td>0.446562</td>\n",
" <td>0.504207</td>\n",
" <td>0.181459</td>\n",
" <td>0.588250</td>\n",
" <td>-0.500687</td>\n",
" <td>0.852940</td>\n",
" <td>0.507002</td>\n",
" <td>...</td>\n",
" <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>210</th>\n",
" <td>-0.050844</td>\n",
" <td>0.745129</td>\n",
" <td>-0.196449</td>\n",
" <td>0.768934</td>\n",
" <td>0.801862</td>\n",
" <td>0.726872</td>\n",
" <td>0.828939</td>\n",
" <td>0.834626</td>\n",
" <td>-0.196972</td>\n",
" <td>0.511044</td>\n",
" <td>...</td>\n",
" <td>-0.441278</td>\n",
" <td>0.004161</td>\n",
" <td>0.560049</td>\n",
" <td>0.554232</td>\n",
" <td>-0.484971</td>\n",
" <td>0.523403</td>\n",
" <td>-0.558288</td>\n",
" <td>0.169205</td>\n",
" <td>0.015261</td>\n",
" <td>-0.200324</td>\n",
" </tr>\n",
" <tr>\n",
" <th>230</th>\n",
" <td>0.274094</td>\n",
" <td>0.629258</td>\n",
" <td>-0.245747</td>\n",
" <td>0.892584</td>\n",
" <td>0.836493</td>\n",
" <td>0.895703</td>\n",
" <td>0.548280</td>\n",
" <td>0.809735</td>\n",
" <td>0.167808</td>\n",
" <td>0.868249</td>\n",
" <td>...</td>\n",
" <td>0.836132</td>\n",
" <td>0.121556</td>\n",
" <td>-0.549369</td>\n",
" <td>-0.439494</td>\n",
" <td>0.830387</td>\n",
" <td>-0.568555</td>\n",
" <td>0.941121</td>\n",
" <td>0.393324</td>\n",
" <td>0.501220</td>\n",
" <td>0.910035</td>\n",
" </tr>\n",
" <tr>\n",
" <th>240</th>\n",
" <td>-0.436268</td>\n",
" <td>0.489435</td>\n",
" <td>-0.389158</td>\n",
" <td>0.640418</td>\n",
" <td>0.813713</td>\n",
" <td>0.716527</td>\n",
" <td>0.760339</td>\n",
" <td>0.759627</td>\n",
" <td>-0.458725</td>\n",
" <td>0.567309</td>\n",
" <td>...</td>\n",
" <td>-0.494051</td>\n",
" <td>-0.352638</td>\n",
" <td>0.099419</td>\n",
" <td>0.646697</td>\n",
" <td>-0.392856</td>\n",
" <td>0.747090</td>\n",
" <td>-0.432399</td>\n",
" <td>-0.511463</td>\n",
" <td>-0.604285</td>\n",
" <td>-0.812709</td>\n",
" </tr>\n",
" <tr>\n",
" <th>270</th>\n",
" <td>0.951605</td>\n",
" <td>-0.195897</td>\n",
" <td>0.935177</td>\n",
" <td>-0.257188</td>\n",
" <td>-0.196613</td>\n",
" <td>-0.232164</td>\n",
" <td>0.045787</td>\n",
" <td>-0.451956</td>\n",
" <td>0.937659</td>\n",
" <td>0.106976</td>\n",
" <td>...</td>\n",
" <td>-0.389880</td>\n",
" <td>-0.334658</td>\n",
" <td>0.282270</td>\n",
" <td>0.553689</td>\n",
" <td>-0.565378</td>\n",
" <td>0.602702</td>\n",
" <td>-0.677582</td>\n",
" <td>-0.155636</td>\n",
" <td>-0.232103</td>\n",
" <td>-0.597021</td>\n",
" </tr>\n",
" <tr>\n",
" <th>280</th>\n",
" <td>0.843241</td>\n",
" <td>0.491517</td>\n",
" <td>0.734772</td>\n",
" <td>0.725860</td>\n",
" <td>0.821948</td>\n",
" <td>0.590551</td>\n",
" <td>0.879312</td>\n",
" <td>-0.070430</td>\n",
" <td>0.749197</td>\n",
" <td>0.892182</td>\n",
" <td>...</td>\n",
" <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>310</th>\n",
" <td>0.912863</td>\n",
" <td>0.310874</td>\n",
" <td>0.882915</td>\n",
" <td>0.745582</td>\n",
" <td>0.649919</td>\n",
" <td>0.560888</td>\n",
" <td>0.838322</td>\n",
" <td>-0.480670</td>\n",
" <td>0.915834</td>\n",
" <td>0.743260</td>\n",
" <td>...</td>\n",
" <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>320</th>\n",
" <td>-0.151438</td>\n",
" <td>0.570147</td>\n",
" <td>-0.320489</td>\n",
" <td>0.909022</td>\n",
" <td>0.856130</td>\n",
" <td>0.858524</td>\n",
" <td>0.494974</td>\n",
" <td>0.816618</td>\n",
" <td>-0.273917</td>\n",
" <td>0.844972</td>\n",
" <td>...</td>\n",
" <td>0.633019</td>\n",
" <td>0.391256</td>\n",
" <td>-0.373411</td>\n",
" <td>-0.438720</td>\n",
" <td>0.707359</td>\n",
" <td>-0.227731</td>\n",
" <td>0.782154</td>\n",
" <td>-0.000440</td>\n",
" <td>0.169545</td>\n",
" <td>0.732866</td>\n",
" </tr>\n",
" <tr>\n",
" <th>360</th>\n",
" <td>0.889090</td>\n",
" <td>0.460868</td>\n",
" <td>0.814027</td>\n",
" <td>0.181262</td>\n",
" <td>-0.139455</td>\n",
" <td>0.014196</td>\n",
" <td>0.007845</td>\n",
" <td>-0.012348</td>\n",
" <td>0.824345</td>\n",
" <td>-0.068287</td>\n",
" <td>...</td>\n",
" <td>-0.498075</td>\n",
" <td>-0.269293</td>\n",
" <td>0.125208</td>\n",
" <td>0.610590</td>\n",
" <td>-0.547000</td>\n",
" <td>0.326852</td>\n",
" <td>-0.798585</td>\n",
" <td>0.554555</td>\n",
" <td>0.519342</td>\n",
" <td>-0.732228</td>\n",
" </tr>\n",
" <tr>\n",
" <th>390</th>\n",
" <td>-0.199251</td>\n",
" <td>0.341617</td>\n",
" <td>-0.284384</td>\n",
" <td>0.766462</td>\n",
" <td>0.733045</td>\n",
" <td>0.730867</td>\n",
" <td>0.366510</td>\n",
" <td>0.660740</td>\n",
" <td>-0.201322</td>\n",
" <td>0.799061</td>\n",
" <td>...</td>\n",
" <td>0.555089</td>\n",
" <td>0.428778</td>\n",
" <td>-0.293414</td>\n",
" <td>-0.365681</td>\n",
" <td>0.612509</td>\n",
" <td>-0.070418</td>\n",
" <td>0.662721</td>\n",
" <td>-0.166527</td>\n",
" <td>0.026482</td>\n",
" <td>0.298921</td>\n",
" </tr>\n",
" <tr>\n",
" <th>400</th>\n",
" <td>0.722700</td>\n",
" <td>-0.137402</td>\n",
" <td>0.610154</td>\n",
" <td>-0.416238</td>\n",
" <td>-0.431208</td>\n",
" <td>-0.435654</td>\n",
" <td>-0.247213</td>\n",
" <td>-0.596061</td>\n",
" <td>0.642913</td>\n",
" <td>-0.098905</td>\n",
" <td>...</td>\n",
" <td>-0.391949</td>\n",
" <td>-0.319876</td>\n",
" <td>0.129641</td>\n",
" <td>0.652250</td>\n",
" <td>-0.317342</td>\n",
" <td>-0.043299</td>\n",
" <td>-0.118309</td>\n",
" <td>0.265272</td>\n",
" <td>0.127573</td>\n",
" <td>-0.042230</td>\n",
" </tr>\n",
" <tr>\n",
" <th>420</th>\n",
" <td>0.646725</td>\n",
" <td>-0.094729</td>\n",
" <td>0.422641</td>\n",
" <td>-0.366693</td>\n",
" <td>-0.421552</td>\n",
" <td>-0.424564</td>\n",
" <td>-0.167144</td>\n",
" <td>-0.549192</td>\n",
" <td>0.554960</td>\n",
" <td>-0.032519</td>\n",
" <td>...</td>\n",
" <td>0.221857</td>\n",
" <td>-0.064771</td>\n",
" <td>0.360502</td>\n",
" <td>-0.135774</td>\n",
" <td>-0.523407</td>\n",
" <td>-0.101986</td>\n",
" <td>-0.740498</td>\n",
" <td>0.724421</td>\n",
" <td>0.302423</td>\n",
" <td>-0.205642</td>\n",
" </tr>\n",
" <tr>\n",
" <th>430</th>\n",
" <td>0.746253</td>\n",
" <td>0.436915</td>\n",
" <td>0.026726</td>\n",
" <td>0.669186</td>\n",
" <td>0.897019</td>\n",
" <td>0.738081</td>\n",
" <td>0.760008</td>\n",
" <td>0.700809</td>\n",
" <td>0.655202</td>\n",
" <td>0.826396</td>\n",
" <td>...</td>\n",
" <td>-0.160493</td>\n",
" <td>0.412211</td>\n",
" <td>0.591414</td>\n",
" <td>-0.284754</td>\n",
" <td>-0.482777</td>\n",
" <td>0.316075</td>\n",
" <td>-0.838611</td>\n",
" <td>-0.176740</td>\n",
" <td>-0.475027</td>\n",
" <td>-0.873552</td>\n",
" </tr>\n",
" <tr>\n",
" <th>480</th>\n",
" <td>0.279602</td>\n",
" <td>0.786883</td>\n",
" <td>-0.250947</td>\n",
" <td>0.917447</td>\n",
" <td>0.902490</td>\n",
" <td>0.822080</td>\n",
" <td>0.732688</td>\n",
" <td>0.923339</td>\n",
" <td>0.068253</td>\n",
" <td>0.783763</td>\n",
" <td>...</td>\n",
" <td>0.376402</td>\n",
" <td>0.544087</td>\n",
" <td>-0.180587</td>\n",
" <td>-0.395688</td>\n",
" <td>0.575187</td>\n",
" <td>-0.072548</td>\n",
" <td>0.524007</td>\n",
" <td>-0.400037</td>\n",
" <td>-0.228255</td>\n",
" <td>-0.382556</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>90430</th>\n",
" <td>NaN</td>\n",
" <td>-0.019463</td>\n",
" <td>-0.194428</td>\n",
" <td>0.774821</td>\n",
" <td>0.649755</td>\n",
" <td>0.797318</td>\n",
" <td>-0.493826</td>\n",
" <td>0.698780</td>\n",
" <td>NaN</td>\n",
" <td>0.903382</td>\n",
" <td>...</td>\n",
" <td>0.835827</td>\n",
" <td>-0.019167</td>\n",
" <td>-0.702426</td>\n",
" <td>-0.365378</td>\n",
" <td>0.942194</td>\n",
" <td>-0.656614</td>\n",
" <td>0.968282</td>\n",
" <td>0.240477</td>\n",
" <td>0.395464</td>\n",
" <td>0.919525</td>\n",
" </tr>\n",
" <tr>\n",
" <th>91090</th>\n",
" <td>NaN</td>\n",
" <td>0.676161</td>\n",
" <td>-0.103943</td>\n",
" <td>-0.027337</td>\n",
" <td>-0.563279</td>\n",
" <td>-0.293176</td>\n",
" <td>0.693714</td>\n",
" <td>-0.024537</td>\n",
" <td>NaN</td>\n",
" <td>-0.454546</td>\n",
" <td>...</td>\n",
" <td>0.204829</td>\n",
" <td>-0.169351</td>\n",
" <td>-0.306678</td>\n",
" <td>0.065674</td>\n",
" <td>0.531948</td>\n",
" <td>-0.707943</td>\n",
" <td>0.666654</td>\n",
" <td>0.593415</td>\n",
" <td>0.342180</td>\n",
" <td>0.821949</td>\n",
" </tr>\n",
" <tr>\n",
" <th>93050</th>\n",
" <td>NaN</td>\n",
" <td>-0.180273</td>\n",
" <td>0.471330</td>\n",
" <td>0.083315</td>\n",
" <td>0.515956</td>\n",
" <td>0.426409</td>\n",
" <td>-0.033694</td>\n",
" <td>-0.194675</td>\n",
" <td>NaN</td>\n",
" <td>0.250762</td>\n",
" <td>...</td>\n",
" <td>-0.248462</td>\n",
" <td>-0.199951</td>\n",
" <td>0.018393</td>\n",
" <td>0.689745</td>\n",
" <td>0.004843</td>\n",
" <td>-0.414592</td>\n",
" <td>0.298846</td>\n",
" <td>-0.007520</td>\n",
" <td>-0.235357</td>\n",
" <td>0.372748</td>\n",
" </tr>\n",
" <tr>\n",
" <th>93370</th>\n",
" <td>NaN</td>\n",
" <td>-0.082759</td>\n",
" <td>-0.220671</td>\n",
" <td>-0.202666</td>\n",
" <td>0.269528</td>\n",
" <td>0.213177</td>\n",
" <td>0.149395</td>\n",
" <td>-0.335390</td>\n",
" <td>NaN</td>\n",
" <td>-0.055865</td>\n",
" <td>...</td>\n",
" <td>-0.216591</td>\n",
" <td>-0.477316</td>\n",
" <td>0.016007</td>\n",
" <td>0.550546</td>\n",
" <td>-0.186338</td>\n",
" <td>-0.316780</td>\n",
" <td>-0.021790</td>\n",
" <td>0.838614</td>\n",
" <td>0.632507</td>\n",
" <td>0.693671</td>\n",
" </tr>\n",
" <tr>\n",
" <th>96770</th>\n",
" <td>NaN</td>\n",
" <td>-0.154972</td>\n",
" <td>-0.145478</td>\n",
" <td>-0.232523</td>\n",
" <td>0.285162</td>\n",
" <td>0.144350</td>\n",
" <td>0.337063</td>\n",
" <td>-0.360971</td>\n",
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" <th>185750</th>\n",
" <td>NaN</td>\n",
" <td>0.555697</td>\n",
" <td>-0.339007</td>\n",
" <td>0.264686</td>\n",
" <td>0.404975</td>\n",
" <td>0.477338</td>\n",
" <td>0.400815</td>\n",
" <td>0.648073</td>\n",
" <td>NaN</td>\n",
" <td>0.219125</td>\n",
" <td>...</td>\n",
" <td>0.642441</td>\n",
" <td>-0.587586</td>\n",
" <td>-0.301917</td>\n",
" <td>-0.320005</td>\n",
" <td>0.459409</td>\n",
" <td>-0.281023</td>\n",
" <td>0.452008</td>\n",
" <td>0.738900</td>\n",
" <td>1.000000</td>\n",
" <td>0.472659</td>\n",
" </tr>\n",
" <tr>\n",
" <th>192820</th>\n",
" <td>NaN</td>\n",
" <td>0.799625</td>\n",
" <td>-0.302625</td>\n",
" <td>0.761573</td>\n",
" <td>0.834929</td>\n",
" <td>0.842206</td>\n",
" <td>-0.117730</td>\n",
" <td>0.881900</td>\n",
" <td>NaN</td>\n",
" <td>0.590474</td>\n",
" <td>...</td>\n",
" <td>0.809774</td>\n",
" <td>-0.510471</td>\n",
" <td>-0.670108</td>\n",
" <td>-0.120521</td>\n",
" <td>0.830518</td>\n",
" <td>-0.886902</td>\n",
" <td>0.984835</td>\n",
" <td>0.777170</td>\n",
" <td>0.472659</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>553 rows × 553 columns</p>\n",
"</div>"
],
"text/plain": [
"stk_cd 10 20 30 50 60 70 80 \\\n",
"stk_cd \n",
"10 1.000000 0.391050 0.968940 0.575346 0.620409 0.253415 0.849252 \n",
"20 0.391050 1.000000 -0.133859 0.750710 0.644468 0.603909 0.739691 \n",
"30 0.968940 -0.133859 1.000000 -0.213912 -0.236129 -0.240313 0.422423 \n",
"50 0.575346 0.750710 -0.213912 1.000000 0.852407 0.879450 0.660103 \n",
"60 0.620409 0.644468 -0.236129 0.852407 1.000000 0.874370 0.738316 \n",
"70 0.253415 0.603909 -0.240313 0.879450 0.874370 1.000000 0.673176 \n",
"80 0.849252 0.739691 0.422423 0.660103 0.738316 0.673176 1.000000 \n",
"100 -0.639750 0.723955 -0.393762 0.886348 0.885252 0.850210 0.743568 \n",
"110 0.971733 0.277941 0.960999 0.406028 0.504677 0.070549 0.781931 \n",
"120 0.770458 0.555178 -0.051783 0.815739 0.838123 0.805577 0.692886 \n",
"130 0.978469 -0.013403 0.970634 0.439778 0.591560 0.379992 0.680432 \n",
"140 -0.644182 0.236020 -0.555343 0.168440 -0.082095 0.063150 -0.322399 \n",
"150 -0.221537 0.775545 -0.433234 0.745113 0.873577 0.705611 0.793304 \n",
"160 0.642274 0.616531 0.421451 0.783833 0.685690 0.719433 0.845196 \n",
"170 0.611898 0.597875 0.449274 0.740640 0.684633 0.392131 0.628631 \n",
"180 0.574288 0.281059 0.315596 0.084973 0.012652 0.054642 0.202014 \n",
"200 0.836672 0.030877 0.814642 0.446562 0.504207 0.181459 0.588250 \n",
"210 -0.050844 0.745129 -0.196449 0.768934 0.801862 0.726872 0.828939 \n",
"230 0.274094 0.629258 -0.245747 0.892584 0.836493 0.895703 0.548280 \n",
"240 -0.436268 0.489435 -0.389158 0.640418 0.813713 0.716527 0.760339 \n",
"270 0.951605 -0.195897 0.935177 -0.257188 -0.196613 -0.232164 0.045787 \n",
"280 0.843241 0.491517 0.734772 0.725860 0.821948 0.590551 0.879312 \n",
"310 0.912863 0.310874 0.882915 0.745582 0.649919 0.560888 0.838322 \n",
"320 -0.151438 0.570147 -0.320489 0.909022 0.856130 0.858524 0.494974 \n",
"360 0.889090 0.460868 0.814027 0.181262 -0.139455 0.014196 0.007845 \n",
"390 -0.199251 0.341617 -0.284384 0.766462 0.733045 0.730867 0.366510 \n",
"400 0.722700 -0.137402 0.610154 -0.416238 -0.431208 -0.435654 -0.247213 \n",
"420 0.646725 -0.094729 0.422641 -0.366693 -0.421552 -0.424564 -0.167144 \n",
"430 0.746253 0.436915 0.026726 0.669186 0.897019 0.738081 0.760008 \n",
"480 0.279602 0.786883 -0.250947 0.917447 0.902490 0.822080 0.732688 \n",
"... ... ... ... ... ... ... ... \n",
"90430 NaN -0.019463 -0.194428 0.774821 0.649755 0.797318 -0.493826 \n",
"91090 NaN 0.676161 -0.103943 -0.027337 -0.563279 -0.293176 0.693714 \n",
"93050 NaN -0.180273 0.471330 0.083315 0.515956 0.426409 -0.033694 \n",
"93370 NaN -0.082759 -0.220671 -0.202666 0.269528 0.213177 0.149395 \n",
"96770 NaN -0.154972 -0.145478 -0.232523 0.285162 0.144350 0.337063 \n",
"97230 NaN 0.622683 0.529729 -0.176667 -0.494536 -0.370005 0.727674 \n",
"97950 NaN -0.021810 -0.028356 0.628231 0.828055 0.658500 -0.667189 \n",
"100840 NaN 0.523446 -0.285533 0.092619 -0.344531 -0.136713 0.341129 \n",
"103130 NaN -0.485113 -0.251767 -0.458859 -0.806568 -0.214952 0.769085 \n",
"103140 NaN -0.437883 0.478352 0.017954 0.319584 0.297187 0.365440 \n",
"103150 NaN 0.546948 NaN -0.004562 -0.525580 -0.733668 0.687202 \n",
"103590 NaN 0.020346 0.319406 0.061548 -0.343140 -0.181709 0.477355 \n",
"104700 NaN 0.743200 -0.229430 0.403598 -0.197429 0.229967 0.074311 \n",
"105560 NaN -0.132785 0.774941 -0.199577 -0.389720 -0.232363 0.790875 \n",
"105630 NaN 0.375788 -0.457382 0.878018 0.694407 0.700201 -0.617490 \n",
"108670 NaN 0.367805 0.007223 0.765587 0.381420 0.324249 -0.492081 \n",
"111770 NaN 0.387201 0.364879 0.751899 0.803151 0.521151 -0.757745 \n",
"114090 NaN 0.320855 0.572054 0.529028 0.581814 0.105298 -0.586432 \n",
"115390 NaN -0.615601 -0.150896 -0.767821 -0.579445 -0.312748 0.647516 \n",
"120110 NaN -0.260134 0.025015 -0.201702 -0.018461 0.186750 0.196447 \n",
"128940 NaN 0.797810 -0.316768 0.694978 0.536057 0.747570 -0.297530 \n",
"138930 NaN 0.024842 0.626800 0.311203 0.151421 -0.069755 -0.030104 \n",
"139130 NaN -0.440734 0.704883 -0.475924 -0.212312 -0.511782 0.324212 \n",
"139480 NaN -0.555677 0.403269 -0.449577 -0.330125 -0.170594 -0.019498 \n",
"145990 NaN 0.770400 0.078759 0.839933 0.498679 0.735273 -0.296099 \n",
"161390 NaN -0.680447 0.253775 -0.492051 -0.204662 -0.557629 -0.163580 \n",
"161890 NaN 0.727097 -0.331195 0.869473 0.511275 0.830865 -0.464950 \n",
"170900 NaN 0.694202 -0.206014 0.125224 0.378989 0.491259 0.383133 \n",
"185750 NaN 0.555697 -0.339007 0.264686 0.404975 0.477338 0.400815 \n",
"192820 NaN 0.799625 -0.302625 0.761573 0.834929 0.842206 -0.117730 \n",
"\n",
"stk_cd 100 110 120 ... 128940 138930 139130 \\\n",
"stk_cd ... \n",
"10 -0.639750 0.971733 0.770458 ... NaN NaN NaN \n",
"20 0.723955 0.277941 0.555178 ... 0.797810 0.024842 -0.440734 \n",
"30 -0.393762 0.960999 -0.051783 ... -0.316768 0.626800 0.704883 \n",
"50 0.886348 0.406028 0.815739 ... 0.694978 0.311203 -0.475924 \n",
"60 0.885252 0.504677 0.838123 ... 0.536057 0.151421 -0.212312 \n",
"70 0.850210 0.070549 0.805577 ... 0.747570 -0.069755 -0.511782 \n",
"80 0.743568 0.781931 0.692886 ... -0.297530 -0.030104 0.324212 \n",
"100 1.000000 -0.663923 0.710423 ... 0.867829 0.120040 -0.386834 \n",
"110 -0.663923 1.000000 0.716701 ... NaN NaN NaN \n",
"120 0.710423 0.716701 1.000000 ... 0.663720 0.229397 -0.541199 \n",
"130 -0.255133 0.955701 0.487274 ... NaN NaN NaN \n",
"140 0.175942 -0.672865 -0.291008 ... 0.087802 0.221759 -0.028779 \n",
"150 0.844883 -0.248820 0.627005 ... -0.613404 0.060853 0.577100 \n",
"160 0.420190 0.506083 0.934610 ... NaN NaN NaN \n",
"170 0.370227 0.447526 0.667140 ... NaN NaN NaN \n",
"180 -0.075128 0.454198 0.328178 ... 0.770652 0.041065 -0.441121 \n",
"200 -0.500687 0.852940 0.507002 ... NaN NaN NaN \n",
"210 0.834626 -0.196972 0.511044 ... -0.441278 0.004161 0.560049 \n",
"230 0.809735 0.167808 0.868249 ... 0.836132 0.121556 -0.549369 \n",
"240 0.759627 -0.458725 0.567309 ... -0.494051 -0.352638 0.099419 \n",
"270 -0.451956 0.937659 0.106976 ... -0.389880 -0.334658 0.282270 \n",
"280 -0.070430 0.749197 0.892182 ... NaN NaN NaN \n",
"310 -0.480670 0.915834 0.743260 ... NaN NaN NaN \n",
"320 0.816618 -0.273917 0.844972 ... 0.633019 0.391256 -0.373411 \n",
"360 -0.012348 0.824345 -0.068287 ... -0.498075 -0.269293 0.125208 \n",
"390 0.660740 -0.201322 0.799061 ... 0.555089 0.428778 -0.293414 \n",
"400 -0.596061 0.642913 -0.098905 ... -0.391949 -0.319876 0.129641 \n",
"420 -0.549192 0.554960 -0.032519 ... 0.221857 -0.064771 0.360502 \n",
"430 0.700809 0.655202 0.826396 ... -0.160493 0.412211 0.591414 \n",
"480 0.923339 0.068253 0.783763 ... 0.376402 0.544087 -0.180587 \n",
"... ... ... ... ... ... ... ... \n",
"90430 0.698780 NaN 0.903382 ... 0.835827 -0.019167 -0.702426 \n",
"91090 -0.024537 NaN -0.454546 ... 0.204829 -0.169351 -0.306678 \n",
"93050 -0.194675 NaN 0.250762 ... -0.248462 -0.199951 0.018393 \n",
"93370 -0.335390 NaN -0.055865 ... -0.216591 -0.477316 0.016007 \n",
"96770 -0.360971 NaN -0.093771 ... -0.345842 -0.298287 0.325275 \n",
"97230 -0.272680 NaN -0.610996 ... -0.461406 -0.072396 0.425526 \n",
"97950 0.350838 NaN 0.746936 ... 0.515308 -0.004834 -0.477965 \n",
"100840 0.205142 NaN -0.205262 ... 0.745475 -0.359396 -0.477605 \n",
"103130 -0.438264 NaN -0.505009 ... -0.349476 -0.106515 0.238557 \n",
"103140 -0.236319 NaN 0.191806 ... -0.322754 -0.039085 0.281407 \n",
"103150 0.740659 NaN -0.683815 ... 0.609960 0.754632 0.760602 \n",
"103590 -0.148793 NaN -0.102823 ... -0.208271 0.497105 0.049350 \n",
"104700 0.461245 NaN 0.170594 ... 0.721407 0.033764 -0.561171 \n",
"105560 -0.326013 NaN -0.326531 ... -0.361656 0.260659 0.406835 \n",
"105630 0.781885 NaN 0.916668 ... 0.769802 0.120515 -0.594423 \n",
"108670 0.616037 NaN 0.661714 ... 0.414219 0.519145 -0.120862 \n",
"111770 0.585010 NaN 0.839979 ... 0.485055 0.361407 -0.324007 \n",
"114090 0.452695 NaN 0.521801 ... 0.127720 0.660168 0.261308 \n",
"115390 -0.676513 NaN -0.769195 ... -0.524968 -0.306952 0.314183 \n",
"120110 -0.297447 NaN -0.110216 ... -0.149263 -0.107290 0.138741 \n",
"128940 0.867829 NaN 0.663720 ... 1.000000 -0.238344 -0.601140 \n",
"138930 0.120040 NaN 0.229397 ... -0.238344 1.000000 0.547383 \n",
"139130 -0.386834 NaN -0.541199 ... -0.601140 0.547383 1.000000 \n",
"139480 -0.594465 NaN -0.558001 ... -0.420956 -0.169789 0.141203 \n",
"145990 0.749193 NaN 0.832907 ... 0.804481 -0.044806 -0.631229 \n",
"161390 -0.512790 NaN -0.582546 ... -0.521148 0.411545 0.658033 \n",
"161890 0.802640 NaN 0.843941 ... 0.804882 -0.302178 -0.767545 \n",
"170900 0.696252 NaN -0.031223 ... 0.621677 -0.477041 -0.142124 \n",
"185750 0.648073 NaN 0.219125 ... 0.642441 -0.587586 -0.301917 \n",
"192820 0.881900 NaN 0.590474 ... 0.809774 -0.510471 -0.670108 \n",
"\n",
"stk_cd 139480 145990 161390 161890 170900 185750 192820 \n",
"stk_cd \n",
"10 NaN NaN NaN NaN NaN NaN NaN \n",
"20 -0.555677 0.770400 -0.680447 0.727097 0.694202 0.555697 0.799625 \n",
"30 0.403269 0.078759 0.253775 -0.331195 -0.206014 -0.339007 -0.302625 \n",
"50 -0.449577 0.839933 -0.492051 0.869473 0.125224 0.264686 0.761573 \n",
"60 -0.330125 0.498679 -0.204662 0.511275 0.378989 0.404975 0.834929 \n",
"70 -0.170594 0.735273 -0.557629 0.830865 0.491259 0.477338 0.842206 \n",
"80 -0.019498 -0.296099 -0.163580 -0.464950 0.383133 0.400815 -0.117730 \n",
"100 -0.594465 0.749193 -0.512790 0.802640 0.696252 0.648073 0.881900 \n",
"110 NaN NaN NaN NaN NaN NaN NaN \n",
"120 -0.558001 0.832907 -0.582546 0.843941 -0.031223 0.219125 0.590474 \n",
"130 NaN NaN NaN NaN NaN NaN NaN \n",
"140 -0.325763 0.403932 -0.673631 0.297081 0.608014 0.469304 0.736348 \n",
"150 0.532723 -0.693377 0.653461 -0.808503 -0.090421 -0.510247 -0.621747 \n",
"160 NaN NaN NaN NaN NaN NaN NaN \n",
"170 NaN NaN NaN NaN NaN NaN NaN \n",
"180 -0.525813 0.844431 -0.597002 0.824228 0.591372 0.562570 0.751206 \n",
"200 NaN NaN NaN NaN NaN NaN NaN \n",
"210 0.554232 -0.484971 0.523403 -0.558288 0.169205 0.015261 -0.200324 \n",
"230 -0.439494 0.830387 -0.568555 0.941121 0.393324 0.501220 0.910035 \n",
"240 0.646697 -0.392856 0.747090 -0.432399 -0.511463 -0.604285 -0.812709 \n",
"270 0.553689 -0.565378 0.602702 -0.677582 -0.155636 -0.232103 -0.597021 \n",
"280 NaN NaN NaN NaN NaN NaN NaN \n",
"310 NaN NaN NaN NaN NaN NaN NaN \n",
"320 -0.438720 0.707359 -0.227731 0.782154 -0.000440 0.169545 0.732866 \n",
"360 0.610590 -0.547000 0.326852 -0.798585 0.554555 0.519342 -0.732228 \n",
"390 -0.365681 0.612509 -0.070418 0.662721 -0.166527 0.026482 0.298921 \n",
"400 0.652250 -0.317342 -0.043299 -0.118309 0.265272 0.127573 -0.042230 \n",
"420 -0.135774 -0.523407 -0.101986 -0.740498 0.724421 0.302423 -0.205642 \n",
"430 -0.284754 -0.482777 0.316075 -0.838611 -0.176740 -0.475027 -0.873552 \n",
"480 -0.395688 0.575187 -0.072548 0.524007 -0.400037 -0.228255 -0.382556 \n",
"... ... ... ... ... ... ... ... \n",
"90430 -0.365378 0.942194 -0.656614 0.968282 0.240477 0.395464 0.919525 \n",
"91090 0.065674 0.531948 -0.707943 0.666654 0.593415 0.342180 0.821949 \n",
"93050 0.689745 0.004843 -0.414592 0.298846 -0.007520 -0.235357 0.372748 \n",
"93370 0.550546 -0.186338 -0.316780 -0.021790 0.838614 0.632507 0.693671 \n",
"96770 0.401627 -0.474895 0.082737 -0.520369 0.558578 0.374477 0.357587 \n",
"97230 0.631845 -0.664153 0.655362 -0.793791 -0.012353 -0.170136 -0.578623 \n",
"97950 -0.268102 0.779740 -0.781626 0.773932 0.146840 0.330114 0.673396 \n",
"100840 -0.312031 0.623207 -0.397764 0.703989 0.607613 0.534929 0.769494 \n",
"103130 0.568749 -0.398827 0.469423 -0.582556 -0.057312 -0.153841 -0.571583 \n",
"103140 0.451074 -0.116847 -0.138004 -0.159503 0.038327 0.000073 -0.062803 \n",
"103150 -0.741048 NaN NaN NaN NaN NaN NaN \n",
"103590 0.090394 0.128785 0.368855 0.103421 -0.696525 -0.542997 -0.845037 \n",
"104700 -0.305579 0.766066 -0.634254 0.892545 0.320802 0.278981 0.876216 \n",
"105560 0.438465 -0.137501 0.216513 -0.225637 -0.366923 -0.483401 -0.299035 \n",
"105630 -0.564068 0.820064 -0.555117 0.919831 0.090133 0.325086 0.821981 \n",
"108670 -0.388987 0.464355 0.157719 0.451378 -0.489783 -0.371429 -0.465319 \n",
"111770 -0.563462 0.708934 -0.510752 0.720449 -0.128413 -0.024574 0.328609 \n",
"114090 -0.368455 0.057100 0.530600 -0.280092 -0.505512 -0.507867 -0.683540 \n",
"115390 0.608825 -0.598245 0.193839 -0.662418 0.339608 0.002553 0.214477 \n",
"120110 0.450600 0.142930 -0.111826 0.355386 0.200103 0.265085 0.100788 \n",
"128940 -0.420956 0.804481 -0.521148 0.804882 0.621677 0.642441 0.809774 \n",
"138930 -0.169789 -0.044806 0.411545 -0.302178 -0.477041 -0.587586 -0.510471 \n",
"139130 0.141203 -0.631229 0.658033 -0.767545 -0.142124 -0.301917 -0.670108 \n",
"139480 1.000000 -0.294309 0.323544 -0.177246 -0.231302 -0.320005 -0.120521 \n",
"145990 -0.294309 1.000000 -0.677749 0.924747 0.353253 0.459409 0.830518 \n",
"161390 0.323544 -0.677749 1.000000 -0.681352 -0.356205 -0.281023 -0.886902 \n",
"161890 -0.177246 0.924747 -0.681352 1.000000 0.311737 0.452008 0.984835 \n",
"170900 -0.231302 0.353253 -0.356205 0.311737 1.000000 0.738900 0.777170 \n",
"185750 -0.320005 0.459409 -0.281023 0.452008 0.738900 1.000000 0.472659 \n",
"192820 -0.120521 0.830518 -0.886902 0.984835 0.777170 0.472659 1.000000 \n",
"\n",
"[553 rows x 553 columns]"
]
},
"execution_count": 55,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"_kospi200_corr = _df_kospi200_pivot.corr()\n",
"_kospi200_corr"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>stk_cd</th>\n",
" <th>10</th>\n",
" <th>20</th>\n",
" <th>30</th>\n",
" <th>50</th>\n",
" <th>60</th>\n",
" <th>70</th>\n",
" <th>80</th>\n",
" <th>100</th>\n",
" <th>110</th>\n",
" <th>120</th>\n",
" <th>...</th>\n",
" <th>128940</th>\n",
" <th>138930</th>\n",
" <th>139130</th>\n",
" <th>139480</th>\n",
" <th>145990</th>\n",
" <th>161390</th>\n",
" <th>161890</th>\n",
" <th>170900</th>\n",
" <th>185750</th>\n",
" <th>192820</th>\n",
" </tr>\n",
" <tr>\n",
" <th>stk_cd</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>1.000000</td>\n",
" <td>0.391050</td>\n",
" <td>0.968940</td>\n",
" <td>0.575346</td>\n",
" <td>0.620409</td>\n",
" <td>0.253415</td>\n",
" <td>0.849252</td>\n",
" <td>-0.639750</td>\n",
" <td>0.971733</td>\n",
" <td>0.770458</td>\n",
" <td>...</td>\n",
" <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>20</th>\n",
" <td>0.391050</td>\n",
" <td>1.000000</td>\n",
" <td>-0.133859</td>\n",
" <td>0.750710</td>\n",
" <td>0.644468</td>\n",
" <td>0.603909</td>\n",
" <td>0.739691</td>\n",
" <td>0.723955</td>\n",
" <td>0.277941</td>\n",
" <td>0.555178</td>\n",
" <td>...</td>\n",
" <td>0.797810</td>\n",
" <td>0.024842</td>\n",
" <td>-0.440734</td>\n",
" <td>-0.555677</td>\n",
" <td>0.770400</td>\n",
" <td>-0.680447</td>\n",
" <td>0.727097</td>\n",
" <td>0.694202</td>\n",
" <td>0.555697</td>\n",
" <td>0.799625</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30</th>\n",
" <td>0.968940</td>\n",
" <td>-0.133859</td>\n",
" <td>1.000000</td>\n",
" <td>-0.213912</td>\n",
" <td>-0.236129</td>\n",
" <td>-0.240313</td>\n",
" <td>0.422423</td>\n",
" <td>-0.393762</td>\n",
" <td>0.960999</td>\n",
" <td>-0.051783</td>\n",
" <td>...</td>\n",
" <td>-0.316768</td>\n",
" <td>0.626800</td>\n",
" <td>0.704883</td>\n",
" <td>0.403269</td>\n",
" <td>0.078759</td>\n",
" <td>0.253775</td>\n",
" <td>-0.331195</td>\n",
" <td>-0.206014</td>\n",
" <td>-0.339007</td>\n",
" <td>-0.302625</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50</th>\n",
" <td>0.575346</td>\n",
" <td>0.750710</td>\n",
" <td>-0.213912</td>\n",
" <td>1.000000</td>\n",
" <td>0.852407</td>\n",
" <td>0.879450</td>\n",
" <td>0.660103</td>\n",
" <td>0.886348</td>\n",
" <td>0.406028</td>\n",
" <td>0.815739</td>\n",
" <td>...</td>\n",
" <td>0.694978</td>\n",
" <td>0.311203</td>\n",
" <td>-0.475924</td>\n",
" <td>-0.449577</td>\n",
" <td>0.839933</td>\n",
" <td>-0.492051</td>\n",
" <td>0.869473</td>\n",
" <td>0.125224</td>\n",
" <td>0.264686</td>\n",
" <td>0.761573</td>\n",
" </tr>\n",
" <tr>\n",
" <th>60</th>\n",
" <td>0.620409</td>\n",
" <td>0.644468</td>\n",
" <td>-0.236129</td>\n",
" <td>0.852407</td>\n",
" <td>1.000000</td>\n",
" <td>0.874370</td>\n",
" <td>0.738316</td>\n",
" <td>0.885252</td>\n",
" <td>0.504677</td>\n",
" <td>0.838123</td>\n",
" <td>...</td>\n",
" <td>0.536057</td>\n",
" <td>0.151421</td>\n",
" <td>-0.212312</td>\n",
" <td>-0.330125</td>\n",
" <td>0.498679</td>\n",
" <td>-0.204662</td>\n",
" <td>0.511275</td>\n",
" <td>0.378989</td>\n",
" <td>0.404975</td>\n",
" <td>0.834929</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 553 columns</p>\n",
"</div>"
],
"text/plain": [
"stk_cd 10 20 30 50 60 70 80 \\\n",
"stk_cd \n",
"10 1.000000 0.391050 0.968940 0.575346 0.620409 0.253415 0.849252 \n",
"20 0.391050 1.000000 -0.133859 0.750710 0.644468 0.603909 0.739691 \n",
"30 0.968940 -0.133859 1.000000 -0.213912 -0.236129 -0.240313 0.422423 \n",
"50 0.575346 0.750710 -0.213912 1.000000 0.852407 0.879450 0.660103 \n",
"60 0.620409 0.644468 -0.236129 0.852407 1.000000 0.874370 0.738316 \n",
"\n",
"stk_cd 100 110 120 ... 128940 138930 139130 \\\n",
"stk_cd ... \n",
"10 -0.639750 0.971733 0.770458 ... NaN NaN NaN \n",
"20 0.723955 0.277941 0.555178 ... 0.797810 0.024842 -0.440734 \n",
"30 -0.393762 0.960999 -0.051783 ... -0.316768 0.626800 0.704883 \n",
"50 0.886348 0.406028 0.815739 ... 0.694978 0.311203 -0.475924 \n",
"60 0.885252 0.504677 0.838123 ... 0.536057 0.151421 -0.212312 \n",
"\n",
"stk_cd 139480 145990 161390 161890 170900 185750 192820 \n",
"stk_cd \n",
"10 NaN NaN NaN NaN NaN NaN NaN \n",
"20 -0.555677 0.770400 -0.680447 0.727097 0.694202 0.555697 0.799625 \n",
"30 0.403269 0.078759 0.253775 -0.331195 -0.206014 -0.339007 -0.302625 \n",
"50 -0.449577 0.839933 -0.492051 0.869473 0.125224 0.264686 0.761573 \n",
"60 -0.330125 0.498679 -0.204662 0.511275 0.378989 0.404975 0.834929 \n",
"\n",
"[5 rows x 553 columns]"
]
},
"execution_count": 56,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"_kospi200_corr.head()"
]
},
{
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"metadata": {
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{
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" </tr>\n",
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"text/plain": [
"stk_cd stk_cd 10 20 30 50 60 70 \\\n",
"0 10 1.000000 0.391050 0.968940 0.575346 0.620409 0.253415 \n",
"1 20 0.391050 1.000000 -0.133859 0.750710 0.644468 0.603909 \n",
"2 30 0.968940 -0.133859 1.000000 -0.213912 -0.236129 -0.240313 \n",
"3 50 0.575346 0.750710 -0.213912 1.000000 0.852407 0.879450 \n",
"4 60 0.620409 0.644468 -0.236129 0.852407 1.000000 0.874370 \n",
"\n",
"stk_cd 80 100 110 ... 128940 138930 139130 \\\n",
"0 0.849252 -0.639750 0.971733 ... NaN NaN NaN \n",
"1 0.739691 0.723955 0.277941 ... 0.797810 0.024842 -0.440734 \n",
"2 0.422423 -0.393762 0.960999 ... -0.316768 0.626800 0.704883 \n",
"3 0.660103 0.886348 0.406028 ... 0.694978 0.311203 -0.475924 \n",
"4 0.738316 0.885252 0.504677 ... 0.536057 0.151421 -0.212312 \n",
"\n",
"stk_cd 139480 145990 161390 161890 170900 185750 192820 \n",
"0 NaN NaN NaN NaN NaN NaN NaN \n",
"1 -0.555677 0.770400 -0.680447 0.727097 0.694202 0.555697 0.799625 \n",
"2 0.403269 0.078759 0.253775 -0.331195 -0.206014 -0.339007 -0.302625 \n",
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"\n",
"[5 rows x 554 columns]"
]
},
"execution_count": 57,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"_kospi200_corr.reset_index().head()"
]
},
{
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"metadata": {
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},
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{
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"</div>"
],
"text/plain": [
"stk_cd_2 stk_cd_1 10 20 30 50 60 \\\n",
"0 10 1.000000 0.391050 0.968940 0.575346 0.620409 \n",
"1 20 0.391050 1.000000 -0.133859 0.750710 0.644468 \n",
"2 30 0.968940 -0.133859 1.000000 -0.213912 -0.236129 \n",
"3 50 0.575346 0.750710 -0.213912 1.000000 0.852407 \n",
"4 60 0.620409 0.644468 -0.236129 0.852407 1.000000 \n",
"\n",
"stk_cd_2 70 80 100 110 ... 128940 \\\n",
"0 0.253415 0.849252 -0.639750 0.971733 ... NaN \n",
"1 0.603909 0.739691 0.723955 0.277941 ... 0.797810 \n",
"2 -0.240313 0.422423 -0.393762 0.960999 ... -0.316768 \n",
"3 0.879450 0.660103 0.886348 0.406028 ... 0.694978 \n",
"4 0.874370 0.738316 0.885252 0.504677 ... 0.536057 \n",
"\n",
"stk_cd_2 138930 139130 139480 145990 161390 161890 \\\n",
"0 NaN NaN NaN NaN NaN NaN \n",
"1 0.024842 -0.440734 -0.555677 0.770400 -0.680447 0.727097 \n",
"2 0.626800 0.704883 0.403269 0.078759 0.253775 -0.331195 \n",
"3 0.311203 -0.475924 -0.449577 0.839933 -0.492051 0.869473 \n",
"4 0.151421 -0.212312 -0.330125 0.498679 -0.204662 0.511275 \n",
"\n",
"stk_cd_2 170900 185750 192820 \n",
"0 NaN NaN NaN \n",
"1 0.694202 0.555697 0.799625 \n",
"2 -0.206014 -0.339007 -0.302625 \n",
"3 0.125224 0.264686 0.761573 \n",
"4 0.378989 0.404975 0.834929 \n",
"\n",
"[5 rows x 554 columns]"
]
},
"execution_count": 58,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"__kospi200_corr = _kospi200_corr.reset_index()\n",
"__kospi200_corr.columns.name = \"stk_cd_2\"\n",
"__kospi200_corr.rename(columns={\"stk_cd\": \"stk_cd_1\"}, inplace=True)\n",
"__kospi200_corr.head()"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>stk_cd_1</th>\n",
" <th>stk_cd_2</th>\n",
" <th>value</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>10</td>\n",
" <td>10</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>20</td>\n",
" <td>10</td>\n",
" <td>0.391050</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>30</td>\n",
" <td>10</td>\n",
" <td>0.968940</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>50</td>\n",
" <td>10</td>\n",
" <td>0.575346</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>60</td>\n",
" <td>10</td>\n",
" <td>0.620409</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" stk_cd_1 stk_cd_2 value\n",
"0 10 10 1.000000\n",
"1 20 10 0.391050\n",
"2 30 10 0.968940\n",
"3 50 10 0.575346\n",
"4 60 10 0.620409"
]
},
"execution_count": 59,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 주의. column.name 과 index.name 이 달라야함\n",
"pd.melt(__kospi200_corr, id_vars=\"stk_cd_1\").head() # id_vars 를 제외한 나머지를 unpivot 함"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>stk_cd_1</th>\n",
" <th>stk_cd_2</th>\n",
" <th>value</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>553</th>\n",
" <td>10</td>\n",
" <td>20</td>\n",
" <td>0.391050</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1106</th>\n",
" <td>10</td>\n",
" <td>30</td>\n",
" <td>0.968940</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1107</th>\n",
" <td>20</td>\n",
" <td>30</td>\n",
" <td>-0.133859</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1659</th>\n",
" <td>10</td>\n",
" <td>50</td>\n",
" <td>0.575346</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1660</th>\n",
" <td>20</td>\n",
" <td>50</td>\n",
" <td>0.750710</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" stk_cd_1 stk_cd_2 value\n",
"553 10 20 0.391050\n",
"1106 10 30 0.968940\n",
"1107 20 30 -0.133859\n",
"1659 10 50 0.575346\n",
"1660 20 50 0.750710"
]
},
"execution_count": 60,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"_kospi200_corr_melt = pd.melt(__kospi200_corr, id_vars=\"stk_cd_1\")\n",
"_kospi200_corr_melt[_kospi200_corr_melt.stk_cd_1 < _kospi200_corr_melt.stk_cd_2].head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Groupby / Apply"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>dt</th>\n",
" <th>stk_cd</th>\n",
" <th>open_prc</th>\n",
" <th>high_prc</th>\n",
" <th>low_prc</th>\n",
" <th>close_prc</th>\n",
" <th>trd_amt</th>\n",
" <th>marketvalue</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>19900103</td>\n",
" <td>10</td>\n",
" <td>45664.160236</td>\n",
" <td>49176.787946</td>\n",
" <td>45664.160236</td>\n",
" <td>49176.787946</td>\n",
" <td>2641758000</td>\n",
" <td>1540000000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>19900104</td>\n",
" <td>10</td>\n",
" <td>49176.787946</td>\n",
" <td>51284.364573</td>\n",
" <td>48474.262404</td>\n",
" <td>51284.364573</td>\n",
" <td>5963383000</td>\n",
" <td>1606000000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>19900105</td>\n",
" <td>10</td>\n",
" <td>51284.364573</td>\n",
" <td>51986.890115</td>\n",
" <td>50230.576259</td>\n",
" <td>50581.839030</td>\n",
" <td>4896930000</td>\n",
" <td>1584000000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>19900106</td>\n",
" <td>10</td>\n",
" <td>50581.839030</td>\n",
" <td>51284.364573</td>\n",
" <td>49879.313488</td>\n",
" <td>50230.576259</td>\n",
" <td>2227277000</td>\n",
" <td>1573000000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>19900108</td>\n",
" <td>10</td>\n",
" <td>50230.576259</td>\n",
" <td>50581.839030</td>\n",
" <td>49528.050717</td>\n",
" <td>50230.576259</td>\n",
" <td>3057359000</td>\n",
" <td>1573000000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" dt stk_cd open_prc high_prc low_prc close_prc \\\n",
"0 19900103 10 45664.160236 49176.787946 45664.160236 49176.787946 \n",
"1 19900104 10 49176.787946 51284.364573 48474.262404 51284.364573 \n",
"2 19900105 10 51284.364573 51986.890115 50230.576259 50581.839030 \n",
"3 19900106 10 50581.839030 51284.364573 49879.313488 50230.576259 \n",
"4 19900108 10 50230.576259 50581.839030 49528.050717 50230.576259 \n",
"\n",
" trd_amt marketvalue \n",
"0 2641758000 1540000000000 \n",
"1 5963383000 1606000000000 \n",
"2 4896930000 1584000000000 \n",
"3 2227277000 1573000000000 \n",
"4 3057359000 1573000000000 "
]
},
"execution_count": 61,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_kospi200.head()"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0 A000010\n",
"1 A000010\n",
"2 A000010\n",
"3 A000010\n",
"4 A000010\n",
"Name: stk_cd, dtype: object"
]
},
"execution_count": 62,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_kospi200.head()[\"stk_cd\"].apply(lambda x: \"A\" + str(x).zfill(6))"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>asset_cd</th>\n",
" <th>in_dt</th>\n",
" <th>out_dt</th>\n",
" <th>ret</th>\n",
" <th>trd_dt</th>\n",
" <th>weight</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>A</td>\n",
" <td>2011-01-01</td>\n",
" <td>2011-01-03</td>\n",
" <td>1.2</td>\n",
" <td>2011-01-01</td>\n",
" <td>0.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>A</td>\n",
" <td>2011-01-01</td>\n",
" <td>2011-01-03</td>\n",
" <td>0.8</td>\n",
" <td>2011-01-02</td>\n",
" <td>0.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>A</td>\n",
" <td>2011-01-01</td>\n",
" <td>2011-01-03</td>\n",
" <td>0.6</td>\n",
" <td>2011-01-03</td>\n",
" <td>0.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>B</td>\n",
" <td>2011-01-01</td>\n",
" <td>2011-01-03</td>\n",
" <td>0.8</td>\n",
" <td>2011-01-01</td>\n",
" <td>0.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>B</td>\n",
" <td>2011-01-01</td>\n",
" <td>2011-01-03</td>\n",
" <td>1.5</td>\n",
" <td>2011-01-02</td>\n",
" <td>0.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>B</td>\n",
" <td>2011-01-01</td>\n",
" <td>2011-01-03</td>\n",
" <td>1.8</td>\n",
" <td>2011-01-03</td>\n",
" <td>0.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>C</td>\n",
" <td>2011-01-01</td>\n",
" <td>2011-01-03</td>\n",
" <td>1.5</td>\n",
" <td>2011-01-01</td>\n",
" <td>0.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>C</td>\n",
" <td>2011-01-01</td>\n",
" <td>2011-01-03</td>\n",
" <td>1.0</td>\n",
" <td>2011-01-02</td>\n",
" <td>0.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>C</td>\n",
" <td>2011-01-01</td>\n",
" <td>2011-01-03</td>\n",
" <td>2.0</td>\n",
" <td>2011-01-03</td>\n",
" <td>0.5</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" asset_cd in_dt out_dt ret trd_dt weight\n",
"0 A 2011-01-01 2011-01-03 1.2 2011-01-01 0.2\n",
"1 A 2011-01-01 2011-01-03 0.8 2011-01-02 0.2\n",
"2 A 2011-01-01 2011-01-03 0.6 2011-01-03 0.2\n",
"3 B 2011-01-01 2011-01-03 0.8 2011-01-01 0.3\n",
"4 B 2011-01-01 2011-01-03 1.5 2011-01-02 0.3\n",
"5 B 2011-01-01 2011-01-03 1.8 2011-01-03 0.3\n",
"6 C 2011-01-01 2011-01-03 1.5 2011-01-01 0.5\n",
"7 C 2011-01-01 2011-01-03 1.0 2011-01-02 0.5\n",
"8 C 2011-01-01 2011-01-03 2.0 2011-01-03 0.5"
]
},
"execution_count": 63,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_stk_rtn"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>asset_cd</th>\n",
" <th>in_dt</th>\n",
" <th>out_dt</th>\n",
" <th>ret</th>\n",
" <th>trd_dt</th>\n",
" <th>weight</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>B</td>\n",
" <td>2011-01-01</td>\n",
" <td>2011-01-03</td>\n",
" <td>1.5</td>\n",
" <td>2011-01-02</td>\n",
" <td>0.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>C</td>\n",
" <td>2011-01-01</td>\n",
" <td>2011-01-03</td>\n",
" <td>1.5</td>\n",
" <td>2011-01-01</td>\n",
" <td>0.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>C</td>\n",
" <td>2011-01-01</td>\n",
" <td>2011-01-03</td>\n",
" <td>2.0</td>\n",
" <td>2011-01-03</td>\n",
" <td>0.5</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" asset_cd in_dt out_dt ret trd_dt weight\n",
"4 B 2011-01-01 2011-01-03 1.5 2011-01-02 0.3\n",
"6 C 2011-01-01 2011-01-03 1.5 2011-01-01 0.5\n",
"8 C 2011-01-01 2011-01-03 2.0 2011-01-03 0.5"
]
},
"execution_count": 64,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# ret가 가장 큰 행만 보고 싶다면..\n",
"df_stk_rtn.sort_values(by=\"ret\").groupby(\"trd_dt\").tail(1)"
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>asset_cd</th>\n",
" <th>in_dt</th>\n",
" <th>out_dt</th>\n",
" <th>ret</th>\n",
" <th>weight</th>\n",
" </tr>\n",
" <tr>\n",
" <th>trd_dt</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2011-01-01</th>\n",
" <td>C</td>\n",
" <td>2011-01-01</td>\n",
" <td>2011-01-03</td>\n",
" <td>1.5</td>\n",
" <td>0.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-01-02</th>\n",
" <td>C</td>\n",
" <td>2011-01-01</td>\n",
" <td>2011-01-03</td>\n",
" <td>1.5</td>\n",
" <td>0.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-01-03</th>\n",
" <td>C</td>\n",
" <td>2011-01-01</td>\n",
" <td>2011-01-03</td>\n",
" <td>2.0</td>\n",
" <td>0.5</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" asset_cd in_dt out_dt ret weight\n",
"trd_dt \n",
"2011-01-01 C 2011-01-01 2011-01-03 1.5 0.5\n",
"2011-01-02 C 2011-01-01 2011-01-03 1.5 0.5\n",
"2011-01-03 C 2011-01-01 2011-01-03 2.0 0.5"
]
},
"execution_count": 65,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_stk_rtn.groupby(\"trd_dt\").max()"
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th>ret</th>\n",
" <th>weight</th>\n",
" </tr>\n",
" <tr>\n",
" <th>in_dt</th>\n",
" <th>out_dt</th>\n",
" <th>trd_dt</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th rowspan=\"3\" valign=\"top\">2011-01-01</th>\n",
" <th rowspan=\"3\" valign=\"top\">2011-01-03</th>\n",
" <th>2011-01-01</th>\n",
" <td>3.5</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-01-02</th>\n",
" <td>3.3</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-01-03</th>\n",
" <td>4.4</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" ret weight\n",
"in_dt out_dt trd_dt \n",
"2011-01-01 2011-01-03 2011-01-01 3.5 1.0\n",
" 2011-01-02 3.3 1.0\n",
" 2011-01-03 4.4 1.0"
]
},
"execution_count": 66,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_stk_rtn.groupby([\"in_dt\", \"out_dt\", \"trd_dt\"]).sum() # MultiIndex ! "
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th></th>\n",
" <th>ret</th>\n",
" <th>weight</th>\n",
" </tr>\n",
" <tr>\n",
" <th>trd_dt</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th rowspan=\"5\" valign=\"top\">2011-01-01</th>\n",
" <th>count</th>\n",
" <td>3.000000</td>\n",
" <td>3.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>1.166667</td>\n",
" <td>0.333333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>0.351188</td>\n",
" <td>0.152753</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>0.800000</td>\n",
" <td>0.200000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>1.000000</td>\n",
" <td>0.250000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" ret weight\n",
"trd_dt \n",
"2011-01-01 count 3.000000 3.000000\n",
" mean 1.166667 0.333333\n",
" std 0.351188 0.152753\n",
" min 0.800000 0.200000\n",
" 25% 1.000000 0.250000"
]
},
"execution_count": 67,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_stk_rtn.groupby(\"trd_dt\").describe().head()"
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr>\n",
" <th></th>\n",
" <th colspan=\"8\" halign=\"left\">ret</th>\n",
" <th colspan=\"8\" halign=\"left\">weight</th>\n",
" </tr>\n",
" <tr>\n",
" <th></th>\n",
" <th>count</th>\n",
" <th>mean</th>\n",
" <th>std</th>\n",
" <th>min</th>\n",
" <th>25%</th>\n",
" <th>50%</th>\n",
" <th>75%</th>\n",
" <th>max</th>\n",
" <th>count</th>\n",
" <th>mean</th>\n",
" <th>std</th>\n",
" <th>min</th>\n",
" <th>25%</th>\n",
" <th>50%</th>\n",
" <th>75%</th>\n",
" <th>max</th>\n",
" </tr>\n",
" <tr>\n",
" <th>trd_dt</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2011-01-01</th>\n",
" <td>3.0</td>\n",
" <td>1.166667</td>\n",
" <td>0.351188</td>\n",
" <td>0.8</td>\n",
" <td>1.0</td>\n",
" <td>1.2</td>\n",
" <td>1.35</td>\n",
" <td>1.5</td>\n",
" <td>3.0</td>\n",
" <td>0.333333</td>\n",
" <td>0.152753</td>\n",
" <td>0.2</td>\n",
" <td>0.25</td>\n",
" <td>0.3</td>\n",
" <td>0.4</td>\n",
" <td>0.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-01-02</th>\n",
" <td>3.0</td>\n",
" <td>1.100000</td>\n",
" <td>0.360555</td>\n",
" <td>0.8</td>\n",
" <td>0.9</td>\n",
" <td>1.0</td>\n",
" <td>1.25</td>\n",
" <td>1.5</td>\n",
" <td>3.0</td>\n",
" <td>0.333333</td>\n",
" <td>0.152753</td>\n",
" <td>0.2</td>\n",
" <td>0.25</td>\n",
" <td>0.3</td>\n",
" <td>0.4</td>\n",
" <td>0.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-01-03</th>\n",
" <td>3.0</td>\n",
" <td>1.466667</td>\n",
" <td>0.757188</td>\n",
" <td>0.6</td>\n",
" <td>1.2</td>\n",
" <td>1.8</td>\n",
" <td>1.90</td>\n",
" <td>2.0</td>\n",
" <td>3.0</td>\n",
" <td>0.333333</td>\n",
" <td>0.152753</td>\n",
" <td>0.2</td>\n",
" <td>0.25</td>\n",
" <td>0.3</td>\n",
" <td>0.4</td>\n",
" <td>0.5</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" ret weight \\\n",
" count mean std min 25% 50% 75% max count \n",
"trd_dt \n",
"2011-01-01 3.0 1.166667 0.351188 0.8 1.0 1.2 1.35 1.5 3.0 \n",
"2011-01-02 3.0 1.100000 0.360555 0.8 0.9 1.0 1.25 1.5 3.0 \n",
"2011-01-03 3.0 1.466667 0.757188 0.6 1.2 1.8 1.90 2.0 3.0 \n",
"\n",
" \n",
" mean std min 25% 50% 75% max \n",
"trd_dt \n",
"2011-01-01 0.333333 0.152753 0.2 0.25 0.3 0.4 0.5 \n",
"2011-01-02 0.333333 0.152753 0.2 0.25 0.3 0.4 0.5 \n",
"2011-01-03 0.333333 0.152753 0.2 0.25 0.3 0.4 0.5 "
]
},
"execution_count": 68,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_stk_rtn.groupby(\"trd_dt\").describe().unstack(1)"
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr>\n",
" <th></th>\n",
" <th></th>\n",
" <th colspan=\"3\" halign=\"left\">ret</th>\n",
" <th colspan=\"3\" halign=\"left\">weight</th>\n",
" </tr>\n",
" <tr>\n",
" <th></th>\n",
" <th>trd_dt</th>\n",
" <th>2011-01-01</th>\n",
" <th>2011-01-02</th>\n",
" <th>2011-01-03</th>\n",
" <th>2011-01-01</th>\n",
" <th>2011-01-02</th>\n",
" <th>2011-01-03</th>\n",
" </tr>\n",
" <tr>\n",
" <th>in_dt</th>\n",
" <th>out_dt</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2011-01-01</th>\n",
" <th>2011-01-03</th>\n",
" <td>3.5</td>\n",
" <td>3.3</td>\n",
" <td>4.4</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" ret weight \\\n",
"trd_dt 2011-01-01 2011-01-02 2011-01-03 2011-01-01 2011-01-02 \n",
"in_dt out_dt \n",
"2011-01-01 2011-01-03 3.5 3.3 4.4 1.0 1.0 \n",
"\n",
" \n",
"trd_dt 2011-01-03 \n",
"in_dt out_dt \n",
"2011-01-01 2011-01-03 1.0 "
]
},
"execution_count": 69,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_stk_rtn.groupby([\"in_dt\", \"out_dt\", \"trd_dt\"]).sum().unstack(2) # MultiIndex ! "
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>asset_cd</th>\n",
" <th>A</th>\n",
" <th>B</th>\n",
" <th>C</th>\n",
" </tr>\n",
" <tr>\n",
" <th>trd_dt</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2011-01-01</th>\n",
" <td>1.2</td>\n",
" <td>0.8</td>\n",
" <td>1.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-01-02</th>\n",
" <td>0.8</td>\n",
" <td>1.5</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-01-03</th>\n",
" <td>0.6</td>\n",
" <td>1.8</td>\n",
" <td>2.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"asset_cd A B C\n",
"trd_dt \n",
"2011-01-01 1.2 0.8 1.5\n",
"2011-01-02 0.8 1.5 1.0\n",
"2011-01-03 0.6 1.8 2.0"
]
},
"execution_count": 70,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_stk_rtn.pivot(columns=\"asset_cd\", index=\"trd_dt\", values=\"ret\")"
]
},
{
"cell_type": "code",
"execution_count": 71,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>asset_cd</th>\n",
" <th>A</th>\n",
" <th>B</th>\n",
" <th>C</th>\n",
" </tr>\n",
" <tr>\n",
" <th>trd_dt</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2011-01-01</th>\n",
" <td>0.2</td>\n",
" <td>0.3</td>\n",
" <td>0.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-01-02</th>\n",
" <td>0.2</td>\n",
" <td>0.3</td>\n",
" <td>0.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-01-03</th>\n",
" <td>0.2</td>\n",
" <td>0.3</td>\n",
" <td>0.5</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"asset_cd A B C\n",
"trd_dt \n",
"2011-01-01 0.2 0.3 0.5\n",
"2011-01-02 0.2 0.3 0.5\n",
"2011-01-03 0.2 0.3 0.5"
]
},
"execution_count": 71,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_stk_rtn.pivot(columns=\"asset_cd\", index=\"trd_dt\", values=\"weight\")"
]
},
{
"cell_type": "code",
"execution_count": 72,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>asset_cd</th>\n",
" <th>in_dt</th>\n",
" <th>out_dt</th>\n",
" <th>ret</th>\n",
" <th>trd_dt</th>\n",
" <th>weight</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>A</td>\n",
" <td>2011-01-01</td>\n",
" <td>2011-01-03</td>\n",
" <td>1.2</td>\n",
" <td>2011-01-01</td>\n",
" <td>0.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>B</td>\n",
" <td>2011-01-01</td>\n",
" <td>2011-01-03</td>\n",
" <td>0.8</td>\n",
" <td>2011-01-01</td>\n",
" <td>0.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>C</td>\n",
" <td>2011-01-01</td>\n",
" <td>2011-01-03</td>\n",
" <td>1.5</td>\n",
" <td>2011-01-01</td>\n",
" <td>0.5</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" asset_cd in_dt out_dt ret trd_dt weight\n",
"0 A 2011-01-01 2011-01-03 1.2 2011-01-01 0.2\n",
"3 B 2011-01-01 2011-01-03 0.8 2011-01-01 0.3\n",
"6 C 2011-01-01 2011-01-03 1.5 2011-01-01 0.5"
]
},
"execution_count": 72,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_stk_rtn[df_stk_rtn.trd_dt == '20110101'].groupby(\"trd_dt\").apply(lambda x: x)"
]
},
{
"cell_type": "code",
"execution_count": 73,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>long_rtn</th>\n",
" <th>short_rtn</th>\n",
" </tr>\n",
" <tr>\n",
" <th>trd_dt</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2011-01-01</th>\n",
" <td>1.23</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-01-02</th>\n",
" <td>1.11</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-01-03</th>\n",
" <td>1.66</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" long_rtn short_rtn\n",
"trd_dt \n",
"2011-01-01 1.23 0.0\n",
"2011-01-02 1.11 0.0\n",
"2011-01-03 1.66 0.0"
]
},
"execution_count": 73,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"def const_rebal_portfolio(base_group): # base_group 이 dataframe\n",
" long_rtn = (base_group[base_group.weight > 0][\"weight\"] * base_group[base_group.weight > 0][\"ret\"]).sum()\n",
" short_rtn = (base_group[base_group.weight < 0][\"weight\"] * base_group[base_group.weight < 0][\"ret\"]).sum()\n",
" return pd.Series([long_rtn, short_rtn], index=[\"long_rtn\", \"short_rtn\"])\n",
"df_stk_rtn.groupby(\"trd_dt\").apply(const_rebal_portfolio)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## SQL like operations"
]
},
{
"cell_type": "code",
"execution_count": 76,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>index</th>\n",
" <th>stk_code</th>\n",
" <th>value</th>\n",
" <th>Code</th>\n",
" <th>Name</th>\n",
" <th>결산월</th>\n",
" <th>주식코드</th>\n",
" <th>WICS업종명(대)</th>\n",
" <th>WICS업종코드(대)</th>\n",
" <th>WICS업종명(중)</th>\n",
" <th>...</th>\n",
" <th>WICS업종명(소)</th>\n",
" <th>WICS업종코드(소)</th>\n",
" <th>WI26업종명(대)</th>\n",
" <th>WI26업종코드(대)</th>\n",
" <th>WI26업종명(중)</th>\n",
" <th>KOSPI200 구성종목여부(1:해당, 0:미해당)</th>\n",
" <th>WI26업종코드(중)</th>\n",
" <th>WMI500 시가총액규모별(1:대형주,2:중형주,3:소형주,0:WMI500미해당)</th>\n",
" <th>시가총액</th>\n",
" <th>시가총액(전체)</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>79602</th>\n",
" <td>20160331</td>\n",
" <td>A003000</td>\n",
" <td>28600</td>\n",
" <td>A003000</td>\n",
" <td>부광약품</td>\n",
" <td>12.0</td>\n",
" <td>A003000</td>\n",
" <td>건강관리</td>\n",
" <td>G35</td>\n",
" <td>제약과생물공학</td>\n",
" <td>...</td>\n",
" <td>제약</td>\n",
" <td>G352020</td>\n",
" <td>건강관리</td>\n",
" <td>WI410</td>\n",
" <td>제약</td>\n",
" <td>1.0</td>\n",
" <td>WI41010</td>\n",
" <td>2.0</td>\n",
" <td>9.749605e+11</td>\n",
" <td>9.749605e+11</td>\n",
" </tr>\n",
" <tr>\n",
" <th>79603</th>\n",
" <td>20160331</td>\n",
" <td>A036570</td>\n",
" <td>253500</td>\n",
" <td>A036570</td>\n",
" <td>엔씨소프트</td>\n",
" <td>12.0</td>\n",
" <td>A036570</td>\n",
" <td>IT</td>\n",
" <td>G45</td>\n",
" <td>소프트웨어와서비스</td>\n",
" <td>...</td>\n",
" <td>게임소프트웨어와서비스</td>\n",
" <td>G451035</td>\n",
" <td>소프트웨어</td>\n",
" <td>WI600</td>\n",
" <td>게임소프트웨어</td>\n",
" <td>1.0</td>\n",
" <td>WI60020</td>\n",
" <td>1.0</td>\n",
" <td>5.559007e+12</td>\n",
" <td>5.559007e+12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>79604</th>\n",
" <td>20160331</td>\n",
" <td>A192820</td>\n",
" <td>125500</td>\n",
" <td>A192820</td>\n",
" <td>코스맥스</td>\n",
" <td>12.0</td>\n",
" <td>A192820</td>\n",
" <td>경기관련소비재</td>\n",
" <td>G25</td>\n",
" <td>내구소비재와의류</td>\n",
" <td>...</td>\n",
" <td>화장품</td>\n",
" <td>G252065</td>\n",
" <td>화장품,의류</td>\n",
" <td>WI310</td>\n",
" <td>화장품</td>\n",
" <td>1.0</td>\n",
" <td>WI31010</td>\n",
" <td>2.0</td>\n",
" <td>1.129438e+12</td>\n",
" <td>1.129438e+12</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>3 rows × 21 columns</p>\n",
"</div>"
],
"text/plain": [
" index stk_code value Code Name 결산월 주식코드 WICS업종명(대) \\\n",
"79602 20160331 A003000 28600 A003000 부광약품 12.0 A003000 건강관리 \n",
"79603 20160331 A036570 253500 A036570 엔씨소프트 12.0 A036570 IT \n",
"79604 20160331 A192820 125500 A192820 코스맥스 12.0 A192820 경기관련소비재 \n",
"\n",
" WICS업종코드(대) WICS업종명(중) ... WICS업종명(소) WICS업종코드(소) \\\n",
"79602 G35 제약과생물공학 ... 제약 G352020 \n",
"79603 G45 소프트웨어와서비스 ... 게임소프트웨어와서비스 G451035 \n",
"79604 G25 내구소비재와의류 ... 화장품 G252065 \n",
"\n",
" WI26업종명(대) WI26업종코드(대) WI26업종명(중) KOSPI200 구성종목여부(1:해당, 0:미해당) \\\n",
"79602 건강관리 WI410 제약 1.0 \n",
"79603 소프트웨어 WI600 게임소프트웨어 1.0 \n",
"79604 화장품,의류 WI310 화장품 1.0 \n",
"\n",
" WI26업종코드(중) WMI500 시가총액규모별(1:대형주,2:중형주,3:소형주,0:WMI500미해당) \\\n",
"79602 WI41010 2.0 \n",
"79603 WI60020 1.0 \n",
"79604 WI31010 2.0 \n",
"\n",
" 시가총액 시가총액(전체) \n",
"79602 9.749605e+11 9.749605e+11 \n",
"79603 5.559007e+12 5.559007e+12 \n",
"79604 1.129438e+12 1.129438e+12 \n",
"\n",
"[3 rows x 21 columns]"
]
},
"execution_count": 76,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"_df_adj_prc_with_wics = pd.merge(_df_adj_prc, df_wics, left_on=\"stk_code\", right_on=\"Code\", how=\"left\").tail(3)\n",
"_df_adj_prc_with_wics"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Rolling, Etc.."
]
},
{
"cell_type": "code",
"execution_count": 77,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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"<div>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th>10</th>\n",
" <th>20</th>\n",
" <th>30</th>\n",
" <th>50</th>\n",
" <th>60</th>\n",
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" <th>80</th>\n",
" <th>100</th>\n",
" <th>110</th>\n",
" <th>120</th>\n",
" <th>...</th>\n",
" <th>128940</th>\n",
" <th>138930</th>\n",
" <th>139130</th>\n",
" <th>139480</th>\n",
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" <th>185750</th>\n",
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" <tr>\n",
" <th>dt</th>\n",
" <th></th>\n",
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" <th></th>\n",
" <th></th>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>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>19900104</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>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>19900105</th>\n",
" <td>50347.663850</td>\n",
" <td>2439.962152</td>\n",
" <td>101778.069261</td>\n",
" <td>24652.942193</td>\n",
" <td>1936.587738</td>\n",
" <td>10127.195431</td>\n",
" <td>10792.385276</td>\n",
" <td>7313.001060</td>\n",
" <td>94074.252480</td>\n",
" <td>36610.020775</td>\n",
" <td>...</td>\n",
" <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>19900106</th>\n",
" <td>50698.926621</td>\n",
" <td>2429.197613</td>\n",
" <td>102488.148814</td>\n",
" <td>24652.942193</td>\n",
" <td>1944.123099</td>\n",
" <td>10216.500506</td>\n",
" <td>11006.237032</td>\n",
" <td>7428.376077</td>\n",
" <td>94721.552382</td>\n",
" <td>35865.914662</td>\n",
" <td>...</td>\n",
" <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>19900108</th>\n",
" <td>50347.663850</td>\n",
" <td>2418.433074</td>\n",
" <td>101541.376076</td>\n",
" <td>24652.942193</td>\n",
" <td>1920.009944</td>\n",
" <td>10305.805580</td>\n",
" <td>11091.777734</td>\n",
" <td>7508.251089</td>\n",
" <td>93858.485845</td>\n",
" <td>35419.450994</td>\n",
" <td>...</td>\n",
" <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>19900109</th>\n",
" <td>50113.488669</td>\n",
" <td>2425.609433</td>\n",
" <td>101067.989708</td>\n",
" <td>24652.942193</td>\n",
" <td>1912.474583</td>\n",
" <td>10555.859788</td>\n",
" <td>11020.493815</td>\n",
" <td>7588.126100</td>\n",
" <td>93426.952577</td>\n",
" <td>35717.093439</td>\n",
" <td>...</td>\n",
" <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",
" </tbody>\n",
"</table>\n",
"<p>6 rows × 553 columns</p>\n",
"</div>"
],
"text/plain": [
"stk_cd 10 20 30 50 60 \\\n",
"dt \n",
"19900103 NaN NaN NaN NaN NaN \n",
"19900104 NaN NaN NaN NaN NaN \n",
"19900105 50347.663850 2439.962152 101778.069261 24652.942193 1936.587738 \n",
"19900106 50698.926621 2429.197613 102488.148814 24652.942193 1944.123099 \n",
"19900108 50347.663850 2418.433074 101541.376076 24652.942193 1920.009944 \n",
"19900109 50113.488669 2425.609433 101067.989708 24652.942193 1912.474583 \n",
"\n",
"stk_cd 70 80 100 110 120 \\\n",
"dt \n",
"19900103 NaN NaN NaN NaN NaN \n",
"19900104 NaN NaN NaN NaN NaN \n",
"19900105 10127.195431 10792.385276 7313.001060 94074.252480 36610.020775 \n",
"19900106 10216.500506 11006.237032 7428.376077 94721.552382 35865.914662 \n",
"19900108 10305.805580 11091.777734 7508.251089 93858.485845 35419.450994 \n",
"19900109 10555.859788 11020.493815 7588.126100 93426.952577 35717.093439 \n",
"\n",
"stk_cd ... 128940 138930 139130 139480 145990 161390 161890 \\\n",
"dt ... \n",
"19900103 ... NaN NaN NaN NaN NaN NaN NaN \n",
"19900104 ... NaN NaN NaN NaN NaN NaN NaN \n",
"19900105 ... NaN NaN NaN NaN NaN NaN NaN \n",
"19900106 ... NaN NaN NaN NaN NaN NaN NaN \n",
"19900108 ... NaN NaN NaN NaN NaN NaN NaN \n",
"19900109 ... NaN NaN NaN NaN NaN NaN NaN \n",
"\n",
"stk_cd 170900 185750 192820 \n",
"dt \n",
"19900103 NaN NaN NaN \n",
"19900104 NaN NaN NaN \n",
"19900105 NaN NaN NaN \n",
"19900106 NaN NaN NaN \n",
"19900108 NaN NaN NaN \n",
"19900109 NaN NaN NaN \n",
"\n",
"[6 rows x 553 columns]"
]
},
"execution_count": 77,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"_df_kospi200_pivot.rolling(window=3).apply(func=np.mean).head(6) # custom function !"
]
},
{
"cell_type": "code",
"execution_count": 78,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
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" <th>20</th>\n",
" <th>30</th>\n",
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" <th>dt</th>\n",
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" <tr>\n",
" <th>20160119</th>\n",
" <td>NaN</td>\n",
" <td>-0.015909</td>\n",
" <td>-0.014371</td>\n",
" <td>0.000000</td>\n",
" <td>-0.054422</td>\n",
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" <td>-0.052121</td>\n",
" <td>-0.039216</td>\n",
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" <td>-0.016892</td>\n",
" <td>-0.023663</td>\n",
" <td>0.006250</td>\n",
" <td>-0.048048</td>\n",
" <td>-0.016216</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20160120</th>\n",
" <td>NaN</td>\n",
" <td>0.006928</td>\n",
" <td>0.013366</td>\n",
" <td>0.018088</td>\n",
" <td>0.028777</td>\n",
" <td>-0.015337</td>\n",
" <td>-0.030875</td>\n",
" <td>-0.040625</td>\n",
" <td>NaN</td>\n",
" <td>-0.036408</td>\n",
" <td>...</td>\n",
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" <td>-0.014739</td>\n",
" <td>0.024465</td>\n",
" <td>-0.133333</td>\n",
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" <td>-0.010989</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20160121</th>\n",
" <td>NaN</td>\n",
" <td>-0.005734</td>\n",
" <td>0.007194</td>\n",
" <td>0.020305</td>\n",
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" <td>-0.015929</td>\n",
" <td>0.011401</td>\n",
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" <td>0.035264</td>\n",
" <td>...</td>\n",
" <td>-0.002759</td>\n",
" <td>0.008761</td>\n",
" <td>0.003452</td>\n",
" <td>0.023881</td>\n",
" <td>0.022624</td>\n",
" <td>0.029446</td>\n",
" <td>-0.002099</td>\n",
" <td>0.034591</td>\n",
" <td>0.008982</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20160122</th>\n",
" <td>NaN</td>\n",
" <td>0.035755</td>\n",
" <td>0.005952</td>\n",
" <td>-0.014925</td>\n",
" <td>0.000000</td>\n",
" <td>0.015106</td>\n",
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" <td>0.019324</td>\n",
" <td>NaN</td>\n",
" <td>0.024331</td>\n",
" <td>...</td>\n",
" <td>0.006916</td>\n",
" <td>0.038462</td>\n",
" <td>0.008028</td>\n",
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" <td>0.017699</td>\n",
" <td>0.033181</td>\n",
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" <td>-0.011869</td>\n",
" <td>0.033333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20160125</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>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",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 553 columns</p>\n",
"</div>"
],
"text/plain": [
"stk_cd 10 20 30 50 60 70 80 \\\n",
"dt \n",
"20160119 NaN -0.015909 -0.014371 0.000000 -0.054422 -0.049563 0.010399 \n",
"20160120 NaN 0.006928 0.013366 0.018088 0.028777 -0.015337 -0.030875 \n",
"20160121 NaN -0.005734 0.007194 0.020305 0.017483 0.031153 -0.015929 \n",
"20160122 NaN 0.035755 0.005952 -0.014925 0.000000 0.015106 0.012590 \n",
"20160125 NaN NaN NaN NaN NaN NaN NaN \n",
"\n",
"stk_cd 100 110 120 ... 128940 138930 139130 \\\n",
"dt ... \n",
"20160119 0.015873 NaN 0.009804 ... -0.033201 -0.052121 -0.039216 \n",
"20160120 -0.040625 NaN -0.036408 ... -0.004121 0.021739 -0.014739 \n",
"20160121 0.011401 NaN 0.035264 ... -0.002759 0.008761 0.003452 \n",
"20160122 0.019324 NaN 0.024331 ... 0.006916 0.038462 0.008028 \n",
"20160125 NaN NaN NaN ... NaN NaN NaN \n",
"\n",
"stk_cd 139480 145990 161390 161890 170900 185750 192820 \n",
"dt \n",
"20160119 -0.038235 0.049383 -0.016892 -0.023663 0.006250 -0.048048 -0.016216 \n",
"20160120 0.024465 -0.133333 -0.027491 0.004215 -0.012422 0.053628 -0.010989 \n",
"20160121 0.023881 0.022624 0.029446 -0.002099 0.034591 0.008982 0.000000 \n",
"20160122 0.002915 0.017699 0.033181 0.043113 0.039514 -0.011869 0.033333 \n",
"20160125 NaN NaN NaN NaN NaN NaN NaN \n",
"\n",
"[5 rows x 553 columns]"
]
},
"execution_count": 78,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"(_df_kospi200_pivot.shift(-1) / _df_kospi200_pivot - 1).tail()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## ML"
]
},
{
"cell_type": "code",
"execution_count": 79,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0 45664.160236\n",
"1 49176.787946\n",
"2 51284.364573\n",
"3 50581.839030\n",
"4 50230.576259\n",
"Name: open_prc, dtype: float64"
]
},
"execution_count": 79,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_kospi200[\"open_prc\"].head()"
]
},
{
"cell_type": "code",
"execution_count": 80,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0 (30000, 50000]\n",
"1 (30000, 50000]\n",
"2 (50000, inf]\n",
"3 (50000, inf]\n",
"4 (50000, inf]\n",
"Name: open_prc, dtype: category\n",
"Categories (5, object): [(0, 10000] < (10000, 20000] < (20000, 30000] < (30000, 50000] < (50000, inf]]"
]
},
"execution_count": 80,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.cut(df_kospi200[\"open_prc\"], bins=[0, 10000, 20000, 30000, 50000, np.inf]).head()"
]
},
{
"cell_type": "code",
"execution_count": 81,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>open_prc_cut_(0, 10000]</th>\n",
" <th>open_prc_cut_(10000, 20000]</th>\n",
" <th>open_prc_cut_(20000, 30000]</th>\n",
" <th>open_prc_cut_(30000, inf]</th>\n",
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" <th>0</th>\n",
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" <tr>\n",
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" <tr>\n",
" <th>2</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" open_prc_cut_(0, 10000] open_prc_cut_(10000, 20000] \\\n",
"0 0.0 0.0 \n",
"1 0.0 0.0 \n",
"2 0.0 0.0 \n",
"3 0.0 0.0 \n",
"4 0.0 0.0 \n",
"\n",
" open_prc_cut_(20000, 30000] open_prc_cut_(30000, inf] \n",
"0 0.0 1.0 \n",
"1 0.0 1.0 \n",
"2 0.0 1.0 \n",
"3 0.0 1.0 \n",
"4 0.0 1.0 "
]
},
"execution_count": 81,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.get_dummies(pd.cut(df_kospi200[\"open_prc\"], bins=[0, 10000, 20000, 30000, np.inf]), prefix=\"open_prc_cut\").head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Visualization"
]
},
{
"cell_type": "code",
"execution_count": 94,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x7fd1e0bb97f0>"
]
},
"execution_count": 94,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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mMEVt/fr1OHfuHF5++WW3jn327Fmrk3YyJ1Z+hYWF4fDhw5o8Zn19PQDg0qVL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\noru7G8888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ZqamjTvx9q1a1Xt+5YvXy40X2PIkCE4ffo0qqur/d6L9vUhQ4YIfSdUhJRl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"text/plain": [
"<matplotlib.figure.Figure at 0x7fd1e0f2e080>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"_samsung_elec_rtn = df_factors[\"수정주가\"][\"A005930\"]\n",
"ax = _samsung_elec_rtn.pct_change().plot(linestyle=\"-\", marker=\"o\", color=\"k\", figsize=(20, 4)) # various options\n",
"ax.set_title(\"삼성전자 수정주가 수익률\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.plot.html\n",
"http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.angle_spectrum"
]
},
{
"cell_type": "code",
"execution_count": 95,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fd1e0ba7b38>"
]
},
"execution_count": 95,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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kSROXMa9cuZKaNWvi7+/vXObp6enS7D2jcYqIiIiIiIiIiIiI5AW3CIyAEaSoVy/r248d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\nCioepFbVW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"text/plain": [
"<matplotlib.figure.Figure at 0x7fd1e0bae470>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"_samsung_lg = df_factors[\"수정주가\"][[\"A066570\", \"A005930\"]].dropna()\n",
"_samsung_lg.div(_samsung_lg.iloc[0]).plot(linestyle=\"-\", marker=\"o\", markersize=4, figsize=(20, 4), logy=True) # various options"
]
},
{
"cell_type": "code",
"execution_count": 96,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fd1e0c72240>"
]
},
"execution_count": 96,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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nkJHpyqYJsgCKGEEWAAok5mvKPh7MjqwkhXcHWTqyAIoZQRYACiTudy7IhozM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\nRURERCQk/R9j67/DnodJQgAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fd1e1ab2438>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ax = _samsung_elec_rtn.pct_change().dropna().plot(kind=\"hist\", normed=True, bins=np.linspace(-1, 1, 100), alpha=0.8)\n",
"sns.kdeplot(_samsung_elec_rtn.pct_change().dropna())"
]
},
{
"cell_type": "code",
"execution_count": 85,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>stk_cd</th>\n",
" <th>810</th>\n",
" <th>5930</th>\n",
" <th>6400</th>\n",
" <th>6660</th>\n",
" <th>9150</th>\n",
" <th>10140</th>\n",
" <th>16360</th>\n",
" <th>18260</th>\n",
" <th>28050</th>\n",
" <th>28260</th>\n",
" <th>29780</th>\n",
" <th>32830</th>\n",
" </tr>\n",
" <tr>\n",
" <th>stk_cd</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>810</th>\n",
" <td>1.000000</td>\n",
" <td>0.493717</td>\n",
" <td>-0.457301</td>\n",
" <td>0.834910</td>\n",
" <td>-0.769578</td>\n",
" <td>-0.522601</td>\n",
" <td>-0.389057</td>\n",
" <td>0.066792</td>\n",
" <td>-0.668668</td>\n",
" <td>-0.125594</td>\n",
" <td>-0.390577</td>\n",
" <td>0.460712</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5930</th>\n",
" <td>0.493717</td>\n",
" <td>1.000000</td>\n",
" <td>-0.180531</td>\n",
" <td>0.430457</td>\n",
" <td>-0.495024</td>\n",
" <td>-0.011939</td>\n",
" <td>-0.588922</td>\n",
" <td>-0.060523</td>\n",
" <td>-0.461748</td>\n",
" <td>-0.095622</td>\n",
" <td>-0.740641</td>\n",
" <td>0.180235</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6400</th>\n",
" <td>-0.457301</td>\n",
" <td>-0.180531</td>\n",
" <td>1.000000</td>\n",
" <td>-0.405049</td>\n",
" <td>0.558063</td>\n",
" <td>0.688590</td>\n",
" <td>0.355272</td>\n",
" <td>0.201452</td>\n",
" <td>0.398907</td>\n",
" <td>-0.117175</td>\n",
" <td>0.391417</td>\n",
" <td>-0.037960</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6660</th>\n",
" <td>0.834910</td>\n",
" <td>0.430457</td>\n",
" <td>-0.405049</td>\n",
" <td>1.000000</td>\n",
" <td>-0.661506</td>\n",
" <td>-0.581676</td>\n",
" <td>-0.345160</td>\n",
" <td>0.251194</td>\n",
" <td>-0.677461</td>\n",
" <td>0.080908</td>\n",
" <td>-0.255924</td>\n",
" <td>0.459023</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9150</th>\n",
" <td>-0.769578</td>\n",
" <td>-0.495024</td>\n",
" <td>0.558063</td>\n",
" <td>-0.661506</td>\n",
" <td>1.000000</td>\n",
" <td>0.407774</td>\n",
" <td>0.577665</td>\n",
" <td>-0.159009</td>\n",
" <td>0.541659</td>\n",
" <td>-0.331796</td>\n",
" <td>0.507033</td>\n",
" <td>-0.085677</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10140</th>\n",
" <td>-0.522601</td>\n",
" <td>-0.011939</td>\n",
" <td>0.688590</td>\n",
" <td>-0.581676</td>\n",
" <td>0.407774</td>\n",
" <td>1.000000</td>\n",
" <td>0.386585</td>\n",
" <td>0.489088</td>\n",
" <td>0.702629</td>\n",
" <td>0.130470</td>\n",
" <td>0.225896</td>\n",
" <td>-0.310678</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16360</th>\n",
" <td>-0.389057</td>\n",
" <td>-0.588922</td>\n",
" <td>0.355272</td>\n",
" <td>-0.345160</td>\n",
" <td>0.577665</td>\n",
" <td>0.386585</td>\n",
" <td>1.000000</td>\n",
" <td>0.097156</td>\n",
" <td>0.600859</td>\n",
" <td>0.499402</td>\n",
" <td>0.784001</td>\n",
" <td>-0.057024</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18260</th>\n",
" <td>0.066792</td>\n",
" <td>-0.060523</td>\n",
" <td>0.201452</td>\n",
" <td>0.251194</td>\n",
" <td>-0.159009</td>\n",
" <td>0.489088</td>\n",
" <td>0.097156</td>\n",
" <td>1.000000</td>\n",
" <td>0.543388</td>\n",
" <td>0.411986</td>\n",
" <td>0.618979</td>\n",
" <td>0.447596</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28050</th>\n",
" <td>-0.668668</td>\n",
" <td>-0.461748</td>\n",
" <td>0.398907</td>\n",
" <td>-0.677461</td>\n",
" <td>0.541659</td>\n",
" <td>0.702629</td>\n",
" <td>0.600859</td>\n",
" <td>0.543388</td>\n",
" <td>1.000000</td>\n",
" <td>0.201831</td>\n",
" <td>0.422355</td>\n",
" <td>-0.593164</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28260</th>\n",
" <td>-0.125594</td>\n",
" <td>-0.095622</td>\n",
" <td>-0.117175</td>\n",
" <td>0.080908</td>\n",
" <td>-0.331796</td>\n",
" <td>0.130470</td>\n",
" <td>0.499402</td>\n",
" <td>0.411986</td>\n",
" <td>0.201831</td>\n",
" <td>1.000000</td>\n",
" <td>0.226106</td>\n",
" <td>-0.169635</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29780</th>\n",
" <td>-0.390577</td>\n",
" <td>-0.740641</td>\n",
" <td>0.391417</td>\n",
" <td>-0.255924</td>\n",
" <td>0.507033</td>\n",
" <td>0.225896</td>\n",
" <td>0.784001</td>\n",
" <td>0.618979</td>\n",
" <td>0.422355</td>\n",
" <td>0.226106</td>\n",
" <td>1.000000</td>\n",
" <td>0.113516</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32830</th>\n",
" <td>0.460712</td>\n",
" <td>0.180235</td>\n",
" <td>-0.037960</td>\n",
" <td>0.459023</td>\n",
" <td>-0.085677</td>\n",
" <td>-0.310678</td>\n",
" <td>-0.057024</td>\n",
" <td>0.447596</td>\n",
" <td>-0.593164</td>\n",
" <td>-0.169635</td>\n",
" <td>0.113516</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"stk_cd 810 5930 6400 6660 9150 10140 16360 \\\n",
"stk_cd \n",
"810 1.000000 0.493717 -0.457301 0.834910 -0.769578 -0.522601 -0.389057 \n",
"5930 0.493717 1.000000 -0.180531 0.430457 -0.495024 -0.011939 -0.588922 \n",
"6400 -0.457301 -0.180531 1.000000 -0.405049 0.558063 0.688590 0.355272 \n",
"6660 0.834910 0.430457 -0.405049 1.000000 -0.661506 -0.581676 -0.345160 \n",
"9150 -0.769578 -0.495024 0.558063 -0.661506 1.000000 0.407774 0.577665 \n",
"10140 -0.522601 -0.011939 0.688590 -0.581676 0.407774 1.000000 0.386585 \n",
"16360 -0.389057 -0.588922 0.355272 -0.345160 0.577665 0.386585 1.000000 \n",
"18260 0.066792 -0.060523 0.201452 0.251194 -0.159009 0.489088 0.097156 \n",
"28050 -0.668668 -0.461748 0.398907 -0.677461 0.541659 0.702629 0.600859 \n",
"28260 -0.125594 -0.095622 -0.117175 0.080908 -0.331796 0.130470 0.499402 \n",
"29780 -0.390577 -0.740641 0.391417 -0.255924 0.507033 0.225896 0.784001 \n",
"32830 0.460712 0.180235 -0.037960 0.459023 -0.085677 -0.310678 -0.057024 \n",
"\n",
"stk_cd 18260 28050 28260 29780 32830 \n",
"stk_cd \n",
"810 0.066792 -0.668668 -0.125594 -0.390577 0.460712 \n",
"5930 -0.060523 -0.461748 -0.095622 -0.740641 0.180235 \n",
"6400 0.201452 0.398907 -0.117175 0.391417 -0.037960 \n",
"6660 0.251194 -0.677461 0.080908 -0.255924 0.459023 \n",
"9150 -0.159009 0.541659 -0.331796 0.507033 -0.085677 \n",
"10140 0.489088 0.702629 0.130470 0.225896 -0.310678 \n",
"16360 0.097156 0.600859 0.499402 0.784001 -0.057024 \n",
"18260 1.000000 0.543388 0.411986 0.618979 0.447596 \n",
"28050 0.543388 1.000000 0.201831 0.422355 -0.593164 \n",
"28260 0.411986 0.201831 1.000000 0.226106 -0.169635 \n",
"29780 0.618979 0.422355 0.226106 1.000000 0.113516 \n",
"32830 0.447596 -0.593164 -0.169635 0.113516 1.000000 "
]
},
"execution_count": 85,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Advanced ! \n",
"_samsung_group_corr = df_kospi200[(df_kospi200.stk_cd.isin(df_wics[df_wics.Name.str.contains(\"삼성\")][\"Code\"].apply(lambda x: int(x[1:])).values)) & (df_kospi200.dt > 20100101)].pivot(columns=\"stk_cd\", index=\"dt\", values=\"close_prc\").corr()\n",
"_samsung_group_corr"
]
},
{
"cell_type": "code",
"execution_count": 97,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fd1e0b75588>"
]
},
"execution_count": 97,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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FrapUJEmic4/e+kGTZ5GdnWXQoZUkyeCukfrh552cmMi50yfo3L3XC8f5uH/bPplXoGbl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\nly5TlqC0kDP7g/dKLGNRyOVypFLzaUEmk
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