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FinanceData/시가총액 분석 02 - 기업순위.ipynb

Last active June 22, 2017 21:55
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시가총액 분석 02 - 기업순위
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시가총액 분석 02 - 기업순위.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# 시가총액 분석 02 - 기업순위\n",
"\n",
"<img width=\"320\" src=\"http://i.imgur.com/XquT22Y.jpg\" >\n",
"\n",
"\n",
"\n",
"#### (c) 2016 이승준 fb.com/plusjune"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"앞선 글 \"시가총액 분석 01 - 시가총액 데이터 만들기\"에서 만들어진 1995년~2015년 시가총액 데이터를 가지고 분석을 해본다. 주로 다음과 같은 내용을 살펴볼 것이다.\n",
"\n",
"1. 특정 조건에 해당하는 데이터 추출 (DataFrame.ix[] 활용법)\n",
"1. 연도별 시가총액 1위 기업의 변화 살펴보기\n",
"1. 관심종목을 선별적으로 처리하는 방법들\n",
"1. 시가총액의 변화 시각화 (matplotlib 차트)\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true,
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [],
"source": [
"# Jupyter Notebook 내에 출력하기 위해\n",
"from IPython.display import display"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## matplotlib 기본 설정"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"\n",
"import matplotlib as mpl\n",
"mpl.rcParams['lines.linewidth'] = 2 \n",
"mpl.rcParams['lines.color'] = 'r'"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"[%matplotlib inline](https://ipython.org/ipython-doc/3/notebook/notebook.html#plotting) 은 의 실행 결과를 Jupyter Notebook 내에서 보기 위한 매직 커멘드이다.\n",
"\n",
"matplotlib.rcParams 은 matplotlib의 기본값들을 지정한다. 일종의 matplotlib에 대한 설정이라고 할 수 있다. 여기서는 그래프 선의 두께를 2, 색깔을 r(red)를 기본값으로 지정하였다."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## 데이터 읽고 확인"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"시가총액 순위 데이터를 일부 가공하고, 1995년~2015년까지의 CSV 파일 전체를 묶어서 krx-marcap.csv 라는 데이터 파일을 만들었었다. 먼저 이 파일을 DataFrame으로 읽는다. "
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [],
"source": [
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>rank</th>\n",
" <th>code</th>\n",
" <th>corp_name</th>\n",
" <th>marcap</th>\n",
" <th>marcap_pct</th>\n",
" <th>year</th>\n",
" </tr>\n",
" <tr>\n",
" <th>code</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>015760</th>\n",
" <td>1</td>\n",
" <td>015760</td>\n",
" <td>한국전력</td>\n",
" <td>18.994194</td>\n",
" <td>0.134566</td>\n",
" <td>1995-12-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>005930</th>\n",
" <td>2</td>\n",
" <td>005930</td>\n",
" <td>삼성전자</td>\n",
" <td>7.665979</td>\n",
" <td>0.054310</td>\n",
" <td>1995-12-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>005490</th>\n",
" <td>3</td>\n",
" <td>005490</td>\n",
" <td>POSCO</td>\n",
" <td>4.760822</td>\n",
" <td>0.033728</td>\n",
" <td>1995-12-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>017670</th>\n",
" <td>4</td>\n",
" <td>017670</td>\n",
" <td>SK텔레콤</td>\n",
" <td>3.229820</td>\n",
" <td>0.022882</td>\n",
" <td>1995-12-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>000200</th>\n",
" <td>5</td>\n",
" <td>000200</td>\n",
" <td>대우중공업</td>\n",
" <td>3.019233</td>\n",
" <td>0.021390</td>\n",
" <td>1995-12-01</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" rank code corp_name marcap marcap_pct year\n",
"code \n",
"015760 1 015760 한국전력 18.994194 0.134566 1995-12-01\n",
"005930 2 005930 삼성전자 7.665979 0.054310 1995-12-01\n",
"005490 3 005490 POSCO 4.760822 0.033728 1995-12-01\n",
"017670 4 017670 SK텔레콤 3.229820 0.022882 1995-12-01\n",
"000200 5 000200 대우중공업 3.019233 0.021390 1995-12-01"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_master = pd.read_csv('data/krx-marcap.csv')\n",
"df_master.set_index('code', inplace=True, drop=False)\n",
"df_master.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "notes"
}
},
"source": [
"DataFrame.ix[] 로 다양한 검색을 할 수 있다. ([]에 다양한 조건을 지정할 수 있다)\n",
"\n",
"종목명 '삼성전자'인 것만 DataFrame으로 추출하려면 다음과 같이 한다."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"21 rows\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/local/lib/python3.5/dist-packages/ipykernel_launcher.py:1: DeprecationWarning: \n",
".ix is deprecated. Please use\n",
".loc for label based indexing or\n",
".iloc for positional indexing\n",
"\n",
"See the documentation here:\n",
"http://pandas.pydata.org/pandas-docs/stable/indexing.html#deprecate_ix\n",
" \"\"\"Entry point for launching an IPython kernel.\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>rank</th>\n",
" <th>code</th>\n",
" <th>corp_name</th>\n",
" <th>marcap</th>\n",
" <th>marcap_pct</th>\n",
" <th>year</th>\n",
" </tr>\n",
" <tr>\n",
" <th>code</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>005930</th>\n",
" <td>2</td>\n",
" <td>005930</td>\n",
" <td>삼성전자</td>\n",
" <td>7.665979</td>\n",
" <td>0.054310</td>\n",
" <td>1995-12-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>005930</th>\n",
" <td>3</td>\n",
" <td>005930</td>\n",
" <td>삼성전자</td>\n",
" <td>3.192433</td>\n",
" <td>0.025545</td>\n",
" <td>1996-12-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>005930</th>\n",
" <td>3</td>\n",
" <td>005930</td>\n",
" <td>삼성전자</td>\n",
" <td>3.719746</td>\n",
" <td>0.047654</td>\n",
" <td>1997-12-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>005930</th>\n",
" <td>3</td>\n",
" <td>005930</td>\n",
" <td>삼성전자</td>\n",
" <td>9.972258</td>\n",
" <td>0.068448</td>\n",
" <td>1998-12-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>005930</th>\n",
" <td>2</td>\n",
" <td>005930</td>\n",
" <td>삼성전자</td>\n",
" <td>39.857115</td>\n",
" <td>0.087447</td>\n",
" <td>1999-12-01</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" rank code corp_name marcap marcap_pct year\n",
"code \n",
"005930 2 005930 삼성전자 7.665979 0.054310 1995-12-01\n",
"005930 3 005930 삼성전자 3.192433 0.025545 1996-12-01\n",
"005930 3 005930 삼성전자 3.719746 0.047654 1997-12-01\n",
"005930 3 005930 삼성전자 9.972258 0.068448 1998-12-01\n",
"005930 2 005930 삼성전자 39.857115 0.087447 1999-12-01"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"t = df_master[df_master['corp_name'] == '삼성전자']\n",
"print( len(t), \"rows\" )\n",
"\n",
"t.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "notes"
}
},
"source": [
"물론 코드('code')로도 검색(추출)할 수 있다."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"21 rows\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>rank</th>\n",
" <th>code</th>\n",
" <th>corp_name</th>\n",
" <th>marcap</th>\n",
" <th>marcap_pct</th>\n",
" <th>year</th>\n",
" </tr>\n",
" <tr>\n",
" <th>code</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
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" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>005930</th>\n",
" <td>2</td>\n",
" <td>005930</td>\n",
" <td>삼성전자</td>\n",
" <td>7.665979</td>\n",
" <td>0.054310</td>\n",
" <td>1995-12-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>005930</th>\n",
" <td>3</td>\n",
" <td>005930</td>\n",
" <td>삼성전자</td>\n",
" <td>3.192433</td>\n",
" <td>0.025545</td>\n",
" <td>1996-12-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>005930</th>\n",
" <td>3</td>\n",
" <td>005930</td>\n",
" <td>삼성전자</td>\n",
" <td>3.719746</td>\n",
" <td>0.047654</td>\n",
" <td>1997-12-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>005930</th>\n",
" <td>3</td>\n",
" <td>005930</td>\n",
" <td>삼성전자</td>\n",
" <td>9.972258</td>\n",
" <td>0.068448</td>\n",
" <td>1998-12-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>005930</th>\n",
" <td>2</td>\n",
" <td>005930</td>\n",
" <td>삼성전자</td>\n",
" <td>39.857115</td>\n",
" <td>0.087447</td>\n",
" <td>1999-12-01</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" rank code corp_name marcap marcap_pct year\n",
"code \n",
"005930 2 005930 삼성전자 7.665979 0.054310 1995-12-01\n",
"005930 3 005930 삼성전자 3.192433 0.025545 1996-12-01\n",
"005930 3 005930 삼성전자 3.719746 0.047654 1997-12-01\n",
"005930 3 005930 삼성전자 9.972258 0.068448 1998-12-01\n",
"005930 2 005930 삼성전자 39.857115 0.087447 1999-12-01"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"t = df_master.ix[df_master['code'] == '005930']\n",
"print( len(t), \"rows\" )\n",
"\n",
"t.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "notes"
}
},
"source": [
"연도를 지정하려면 'year' 컬럼이 '2015'로 시작하는 로우(row)들을 추출하면 된다."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2133 rows\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>rank</th>\n",
" <th>code</th>\n",
" <th>corp_name</th>\n",
" <th>marcap</th>\n",
" <th>marcap_pct</th>\n",
" <th>year</th>\n",
" </tr>\n",
" <tr>\n",
" <th>code</th>\n",
" <th></th>\n",
" <th></th>\n",
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" <th></th>\n",
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" <tbody>\n",
" <tr>\n",
" <th>005930</th>\n",
" <td>1</td>\n",
" <td>005930</td>\n",
" <td>삼성전자</td>\n",
" <td>190.752641</td>\n",
" <td>0.130428</td>\n",
" <td>2015-12-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>005380</th>\n",
" <td>2</td>\n",
" <td>005380</td>\n",
" <td>현대차</td>\n",
" <td>33.371887</td>\n",
" <td>0.022818</td>\n",
" <td>2015-12-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>015760</th>\n",
" <td>3</td>\n",
" <td>015760</td>\n",
" <td>한국전력</td>\n",
" <td>32.290793</td>\n",
" <td>0.022079</td>\n",
" <td>2015-12-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>028260</th>\n",
" <td>4</td>\n",
" <td>028260</td>\n",
" <td>삼성물산</td>\n",
" <td>27.979281</td>\n",
" <td>0.019131</td>\n",
" <td>2015-12-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>005935</th>\n",
" <td>5</td>\n",
" <td>005935</td>\n",
" <td>삼성전자우</td>\n",
" <td>24.888435</td>\n",
" <td>0.017018</td>\n",
" <td>2015-12-01</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" rank code corp_name marcap marcap_pct year\n",
"code \n",
"005930 1 005930 삼성전자 190.752641 0.130428 2015-12-01\n",
"005380 2 005380 현대차 33.371887 0.022818 2015-12-01\n",
"015760 3 015760 한국전력 32.290793 0.022079 2015-12-01\n",
"028260 4 028260 삼성물산 27.979281 0.019131 2015-12-01\n",
"005935 5 005935 삼성전자우 24.888435 0.017018 2015-12-01"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"t = df_master.ix[df_master['year'].str.startswith('2015')]\n",
"print( len(t), \"rows\" )\n",
"\n",
"t.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "notes"
}
},
"source": [
"간단하지만 유용한 팁 하나. str.startswith() 함수에 여러개의 여러 문자열 지정하려면 문자열 튜플을 지정한다."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"6084 rows\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>rank</th>\n",
" <th>code</th>\n",
" <th>corp_name</th>\n",
" <th>marcap</th>\n",
" <th>marcap_pct</th>\n",
" <th>year</th>\n",
" </tr>\n",
" <tr>\n",
" <th>code</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>092630</th>\n",
" <td>2129</td>\n",
" <td>092630</td>\n",
" <td>바다로3호</td>\n",
" <td>0.002426</td>\n",
" <td>0.000002</td>\n",
" <td>2015-12-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>000327</th>\n",
" <td>2130</td>\n",
" <td>000327</td>\n",
" <td>노루홀딩스2우B</td>\n",
" <td>0.002272</td>\n",
" <td>0.000002</td>\n",
" <td>2015-12-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>002787</th>\n",
" <td>2131</td>\n",
" <td>002787</td>\n",
" <td>진흥기업2우B</td>\n",
" <td>0.002264</td>\n",
" <td>0.000002</td>\n",
" <td>2015-12-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>183410</th>\n",
" <td>2132</td>\n",
" <td>183410</td>\n",
" <td>데카시스템</td>\n",
" <td>0.002242</td>\n",
" <td>0.000002</td>\n",
" <td>2015-12-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>004987</th>\n",
" <td>2133</td>\n",
" <td>004987</td>\n",
" <td>성신양회2우B</td>\n",
" <td>0.002196</td>\n",
" <td>0.000002</td>\n",
" <td>2015-12-01</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" rank code corp_name marcap marcap_pct year\n",
"code \n",
"092630 2129 092630 바다로3호 0.002426 0.000002 2015-12-01\n",
"000327 2130 000327 노루홀딩스2우B 0.002272 0.000002 2015-12-01\n",
"002787 2131 002787 진흥기업2우B 0.002264 0.000002 2015-12-01\n",
"183410 2132 183410 데카시스템 0.002242 0.000002 2015-12-01\n",
"004987 2133 004987 성신양회2우B 0.002196 0.000002 2015-12-01"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"t = df_master.ix[df_master['year'].str.startswith(('2011', '2013', '2015'))]\n",
"print( len(t), \"rows\" )\n",
"\n",
"t.tail()"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "notes"
}
},
"source": [
"종목명이 '삼성전자', '현대차', 'KB금융'인 것을 추출하고자 한다면,"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"50 rows\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>rank</th>\n",
" <th>code</th>\n",
" <th>corp_name</th>\n",
" <th>marcap</th>\n",
" <th>marcap_pct</th>\n",
" <th>year</th>\n",
" </tr>\n",
" <tr>\n",
" <th>code</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>005380</th>\n",
" <td>2</td>\n",
" <td>005380</td>\n",
" <td>현대차</td>\n",
" <td>37.226725</td>\n",
" <td>0.027848</td>\n",
" <td>2014-12-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>105560</th>\n",
" <td>15</td>\n",
" <td>105560</td>\n",
" <td>KB금융</td>\n",
" <td>13.966614</td>\n",
" <td>0.010448</td>\n",
" <td>2014-12-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>005930</th>\n",
" <td>1</td>\n",
" <td>005930</td>\n",
" <td>삼성전자</td>\n",
" <td>190.752641</td>\n",
" <td>0.130428</td>\n",
" <td>2015-12-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>005380</th>\n",
" <td>2</td>\n",
" <td>005380</td>\n",
" <td>현대차</td>\n",
" <td>33.371887</td>\n",
" <td>0.022818</td>\n",
" <td>2015-12-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>105560</th>\n",
" <td>21</td>\n",
" <td>105560</td>\n",
" <td>KB금융</td>\n",
" <td>13.213228</td>\n",
" <td>0.009035</td>\n",
" <td>2015-12-01</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" rank code corp_name marcap marcap_pct year\n",
"code \n",
"005380 2 005380 현대차 37.226725 0.027848 2014-12-01\n",
"105560 15 105560 KB금융 13.966614 0.010448 2014-12-01\n",
"005930 1 005930 삼성전자 190.752641 0.130428 2015-12-01\n",
"005380 2 005380 현대차 33.371887 0.022818 2015-12-01\n",
"105560 21 105560 KB금융 13.213228 0.009035 2015-12-01"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"t = df_master.ix[df_master['corp_name'].isin(['삼성전자', '현대차', 'KB금융'])]\n",
"print( len(t), \"rows\" )\n",
"\n",
"t.tail()"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "notes"
}
},
"source": [
"컬럼 값으로 크기 비교를 통해 로우(row)들을 추출할 수 있다.\n",
"예를 들어, 시가총액 컬럼(marcap)이 30조원을 넘는 모든 행을 추출하려면,"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"36 rows\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>rank</th>\n",
" <th>code</th>\n",
" <th>corp_name</th>\n",
" <th>marcap</th>\n",
" <th>marcap_pct</th>\n",
" <th>year</th>\n",
" </tr>\n",
" <tr>\n",
" <th>code</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>005380</th>\n",
" <td>2</td>\n",
" <td>005380</td>\n",
" <td>현대차</td>\n",
" <td>37.226725</td>\n",
" <td>0.027848</td>\n",
" <td>2014-12-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>000660</th>\n",
" <td>3</td>\n",
" <td>000660</td>\n",
" <td>SK하이닉스</td>\n",
" <td>34.762113</td>\n",
" <td>0.026005</td>\n",
" <td>2014-12-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>005930</th>\n",
" <td>1</td>\n",
" <td>005930</td>\n",
" <td>삼성전자</td>\n",
" <td>190.752641</td>\n",
" <td>0.130428</td>\n",
" <td>2015-12-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>005380</th>\n",
" <td>2</td>\n",
" <td>005380</td>\n",
" <td>현대차</td>\n",
" <td>33.371887</td>\n",
" <td>0.022818</td>\n",
" <td>2015-12-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>015760</th>\n",
" <td>3</td>\n",
" <td>015760</td>\n",
" <td>한국전력</td>\n",
" <td>32.290793</td>\n",
" <td>0.022079</td>\n",
" <td>2015-12-01</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" rank code corp_name marcap marcap_pct year\n",
"code \n",
"005380 2 005380 현대차 37.226725 0.027848 2014-12-01\n",
"000660 3 000660 SK하이닉스 34.762113 0.026005 2014-12-01\n",
"005930 1 005930 삼성전자 190.752641 0.130428 2015-12-01\n",
"005380 2 005380 현대차 33.371887 0.022818 2015-12-01\n",
"015760 3 015760 한국전력 32.290793 0.022079 2015-12-01"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"t = df_master.ix[df_master['marcap'] > 30.]\n",
"print( len(t), \"rows\" )\n",
"\n",
"t.tail()"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## 연도별 시가총액 1위 종목"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"순위('rank')가 1인 종목만 추출해 보자"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>rank</th>\n",
" <th>code</th>\n",
" <th>corp_name</th>\n",
" <th>marcap</th>\n",
" <th>marcap_pct</th>\n",
" <th>year</th>\n",
" </tr>\n",
" <tr>\n",
" <th>code</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>015760</th>\n",
" <td>1</td>\n",
" <td>015760</td>\n",
" <td>한국전력</td>\n",
" <td>18.994194</td>\n",
" <td>0.134566</td>\n",
" <td>1995-12-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>015760</th>\n",
" <td>1</td>\n",
" <td>015760</td>\n",
" <td>한국전력</td>\n",
" <td>15.440259</td>\n",
" <td>0.123548</td>\n",
" <td>1996-12-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>015760</th>\n",
" <td>1</td>\n",
" <td>015760</td>\n",
" <td>한국전력</td>\n",
" <td>9.863011</td>\n",
" <td>0.126356</td>\n",
" <td>1997-12-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>015760</th>\n",
" <td>1</td>\n",
" <td>015760</td>\n",
" <td>한국전력</td>\n",
" <td>18.720913</td>\n",
" <td>0.128498</td>\n",
" <td>1998-12-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>030200</th>\n",
" <td>1</td>\n",
" <td>030200</td>\n",
" <td>KT</td>\n",
" <td>55.883739</td>\n",
" <td>0.122610</td>\n",
" <td>1999-12-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>022875</th>\n",
" <td>1</td>\n",
" <td>022875</td>\n",
" <td>평화은행우선</td>\n",
" <td>44.044000</td>\n",
" <td>0.168510</td>\n",
" <td>2000-12-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>005930</th>\n",
" <td>1</td>\n",
" <td>005930</td>\n",
" <td>삼성전자</td>\n",
" <td>42.220610</td>\n",
" <td>0.137106</td>\n",
" <td>2001-12-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>005930</th>\n",
" <td>1</td>\n",
" <td>005930</td>\n",
" <td>삼성전자</td>\n",
" <td>47.958513</td>\n",
" <td>0.161827</td>\n",
" <td>2002-12-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>005930</th>\n",
" <td>1</td>\n",
" <td>005930</td>\n",
" <td>삼성전자</td>\n",
" <td>68.034797</td>\n",
" <td>0.173112</td>\n",
" <td>2003-12-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>005930</th>\n",
" <td>1</td>\n",
" <td>005930</td>\n",
" <td>삼성전자</td>\n",
" <td>66.358351</td>\n",
" <td>0.149453</td>\n",
" <td>2004-12-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>005930</th>\n",
" <td>1</td>\n",
" <td>005930</td>\n",
" <td>삼성전자</td>\n",
" <td>97.070263</td>\n",
" <td>0.133661</td>\n",
" <td>2005-12-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>005930</th>\n",
" <td>1</td>\n",
" <td>005930</td>\n",
" <td>삼성전자</td>\n",
" <td>90.294494</td>\n",
" <td>0.116250</td>\n",
" <td>2006-12-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>005930</th>\n",
" <td>1</td>\n",
" <td>005930</td>\n",
" <td>삼성전자</td>\n",
" <td>81.898431</td>\n",
" <td>0.077858</td>\n",
" <td>2007-12-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>005930</th>\n",
" <td>1</td>\n",
" <td>005930</td>\n",
" <td>삼성전자</td>\n",
" <td>66.432001</td>\n",
" <td>0.106613</td>\n",
" <td>2008-12-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>005930</th>\n",
" <td>1</td>\n",
" <td>005930</td>\n",
" <td>삼성전자</td>\n",
" <td>117.692170</td>\n",
" <td>0.120829</td>\n",
" <td>2009-12-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>005930</th>\n",
" <td>1</td>\n",
" <td>005930</td>\n",
" <td>삼성전자</td>\n",
" <td>139.787071</td>\n",
" <td>0.112744</td>\n",
" <td>2010-12-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>005930</th>\n",
" <td>1</td>\n",
" <td>005930</td>\n",
" <td>삼성전자</td>\n",
" <td>156.284597</td>\n",
" <td>0.136402</td>\n",
" <td>2011-12-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>005930</th>\n",
" <td>1</td>\n",
" <td>005930</td>\n",
" <td>삼성전자</td>\n",
" <td>224.189591</td>\n",
" <td>0.177447</td>\n",
" <td>2012-12-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>005930</th>\n",
" <td>1</td>\n",
" <td>005930</td>\n",
" <td>삼성전자</td>\n",
" <td>202.094690</td>\n",
" <td>0.154721</td>\n",
" <td>2013-12-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>005930</th>\n",
" <td>1</td>\n",
" <td>005930</td>\n",
" <td>삼성전자</td>\n",
" <td>195.466220</td>\n",
" <td>0.146223</td>\n",
" <td>2014-12-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>005930</th>\n",
" <td>1</td>\n",
" <td>005930</td>\n",
" <td>삼성전자</td>\n",
" <td>190.752641</td>\n",
" <td>0.130428</td>\n",
" <td>2015-12-01</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" rank code corp_name marcap marcap_pct year\n",
"code \n",
"015760 1 015760 한국전력 18.994194 0.134566 1995-12-01\n",
"015760 1 015760 한국전력 15.440259 0.123548 1996-12-01\n",
"015760 1 015760 한국전력 9.863011 0.126356 1997-12-01\n",
"015760 1 015760 한국전력 18.720913 0.128498 1998-12-01\n",
"030200 1 030200 KT 55.883739 0.122610 1999-12-01\n",
"022875 1 022875 평화은행우선 44.044000 0.168510 2000-12-01\n",
"005930 1 005930 삼성전자 42.220610 0.137106 2001-12-01\n",
"005930 1 005930 삼성전자 47.958513 0.161827 2002-12-01\n",
"005930 1 005930 삼성전자 68.034797 0.173112 2003-12-01\n",
"005930 1 005930 삼성전자 66.358351 0.149453 2004-12-01\n",
"005930 1 005930 삼성전자 97.070263 0.133661 2005-12-01\n",
"005930 1 005930 삼성전자 90.294494 0.116250 2006-12-01\n",
"005930 1 005930 삼성전자 81.898431 0.077858 2007-12-01\n",
"005930 1 005930 삼성전자 66.432001 0.106613 2008-12-01\n",
"005930 1 005930 삼성전자 117.692170 0.120829 2009-12-01\n",
"005930 1 005930 삼성전자 139.787071 0.112744 2010-12-01\n",
"005930 1 005930 삼성전자 156.284597 0.136402 2011-12-01\n",
"005930 1 005930 삼성전자 224.189591 0.177447 2012-12-01\n",
"005930 1 005930 삼성전자 202.094690 0.154721 2013-12-01\n",
"005930 1 005930 삼성전자 195.466220 0.146223 2014-12-01\n",
"005930 1 005930 삼성전자 190.752641 0.130428 2015-12-01"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_master.ix[df_master['rank'] == 1]"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true,
"slideshow": {
"slide_type": "notes"
}
},
"source": [
"1998년 이전에 한국전력이 시가총액 1위 였다가 2001년 이후는 줄 곧 삼성전자가 1위를 하고 있다. 시가총액 1위에 대한 데이터만 가지고 섣불리 판단하기는 어렵지만, 10년 이상의 기간을 놓고 볼 때, 확실히 한국전력(기간산업)에서 삼성전자(전기, 전자 제조 산업)으로 큰 이동이 있음을 추정할 수 있다. 산업의 변화는 나중에 좀 더 자세히 살펴보자.\n",
"\n",
"데이터를 보니 약간의 궁금한 점이 생긴다. 딱 한 해씩 시가총액 1위를 한 종목이 있는데, \"KT\"와 \"평화은행우선\"이다.\n",
"\n",
"\"KT\" 부터 살펴보자."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
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"\n",
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" text-align: left;\n",
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" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>rank</th>\n",
" <th>code</th>\n",
" <th>corp_name</th>\n",
" <th>marcap</th>\n",
" <th>marcap_pct</th>\n",
" <th>year</th>\n",
" </tr>\n",
" <tr>\n",
" <th>code</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>030200</th>\n",
" <td>2</td>\n",
" <td>030200</td>\n",
" <td>KT</td>\n",
" <td>10.940864</td>\n",
" <td>0.075097</td>\n",
" <td>1998-12-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>030200</th>\n",
" <td>1</td>\n",
" <td>030200</td>\n",
" <td>KT</td>\n",
" <td>55.883739</td>\n",
" <td>0.122610</td>\n",
" <td>1999-12-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>030200</th>\n",
" <td>4</td>\n",
" <td>030200</td>\n",
" <td>KT</td>\n",
" <td>20.917377</td>\n",
" <td>0.080029</td>\n",
" <td>2000-12-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>030200</th>\n",
" <td>3</td>\n",
" <td>030200</td>\n",
" <td>KT</td>\n",
" <td>15.594373</td>\n",
" <td>0.050641</td>\n",
" <td>2001-12-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>030200</th>\n",
" <td>3</td>\n",
" <td>030200</td>\n",
" <td>KT</td>\n",
" <td>15.670237</td>\n",
" <td>0.052876</td>\n",
" <td>2002-12-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>030200</th>\n",
" <td>6</td>\n",
" <td>030200</td>\n",
" <td>KT</td>\n",
" <td>12.704283</td>\n",
" <td>0.032326</td>\n",
" <td>2003-12-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>030200</th>\n",
" <td>8</td>\n",
" <td>030200</td>\n",
" <td>KT</td>\n",
" <td>11.821250</td>\n",
" <td>0.026624</td>\n",
" <td>2004-12-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>030200</th>\n",
" <td>12</td>\n",
" <td>030200</td>\n",
" <td>KT</td>\n",
" <td>11.636098</td>\n",
" <td>0.016022</td>\n",
" <td>2005-12-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>030200</th>\n",
" <td>10</td>\n",
" <td>030200</td>\n",
" <td>KT</td>\n",
" <td>13.002674</td>\n",
" <td>0.016740</td>\n",
" <td>2006-12-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>030200</th>\n",
" <td>14</td>\n",
" <td>030200</td>\n",
" <td>KT</td>\n",
" <td>13.573144</td>\n",
" <td>0.012903</td>\n",
" <td>2007-12-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>030200</th>\n",
" <td>10</td>\n",
" <td>030200</td>\n",
" <td>KT</td>\n",
" <td>10.257589</td>\n",
" <td>0.016462</td>\n",
" <td>2008-12-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>030200</th>\n",
" <td>18</td>\n",
" <td>030200</td>\n",
" <td>KT</td>\n",
" <td>10.209472</td>\n",
" <td>0.010482</td>\n",
" <td>2009-12-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>030200</th>\n",
" <td>22</td>\n",
" <td>030200</td>\n",
" <td>KT</td>\n",
" <td>12.076421</td>\n",
" <td>0.009740</td>\n",
" <td>2010-12-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>030200</th>\n",
" <td>25</td>\n",
" <td>030200</td>\n",
" <td>KT</td>\n",
" <td>9.360858</td>\n",
" <td>0.008170</td>\n",
" <td>2011-12-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>030200</th>\n",
" <td>27</td>\n",
" <td>030200</td>\n",
" <td>KT</td>\n",
" <td>9.269469</td>\n",
" <td>0.007337</td>\n",
" <td>2012-12-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>030200</th>\n",
" <td>31</td>\n",
" <td>030200</td>\n",
" <td>KT</td>\n",
" <td>8.238078</td>\n",
" <td>0.006307</td>\n",
" <td>2013-12-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>030200</th>\n",
" <td>30</td>\n",
" <td>030200</td>\n",
" <td>KT</td>\n",
" <td>8.159744</td>\n",
" <td>0.006104</td>\n",
" <td>2014-12-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>030200</th>\n",
" <td>35</td>\n",
" <td>030200</td>\n",
" <td>KT</td>\n",
" <td>7.559187</td>\n",
" <td>0.005169</td>\n",
" <td>2015-12-01</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" rank code corp_name marcap marcap_pct year\n",
"code \n",
"030200 2 030200 KT 10.940864 0.075097 1998-12-01\n",
"030200 1 030200 KT 55.883739 0.122610 1999-12-01\n",
"030200 4 030200 KT 20.917377 0.080029 2000-12-01\n",
"030200 3 030200 KT 15.594373 0.050641 2001-12-01\n",
"030200 3 030200 KT 15.670237 0.052876 2002-12-01\n",
"030200 6 030200 KT 12.704283 0.032326 2003-12-01\n",
"030200 8 030200 KT 11.821250 0.026624 2004-12-01\n",
"030200 12 030200 KT 11.636098 0.016022 2005-12-01\n",
"030200 10 030200 KT 13.002674 0.016740 2006-12-01\n",
"030200 14 030200 KT 13.573144 0.012903 2007-12-01\n",
"030200 10 030200 KT 10.257589 0.016462 2008-12-01\n",
"030200 18 030200 KT 10.209472 0.010482 2009-12-01\n",
"030200 22 030200 KT 12.076421 0.009740 2010-12-01\n",
"030200 25 030200 KT 9.360858 0.008170 2011-12-01\n",
"030200 27 030200 KT 9.269469 0.007337 2012-12-01\n",
"030200 31 030200 KT 8.238078 0.006307 2013-12-01\n",
"030200 30 030200 KT 8.159744 0.006104 2014-12-01\n",
"030200 35 030200 KT 7.559187 0.005169 2015-12-01"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_kt = df_master.ix[df_master['corp_name'] == 'KT']\n",
"df_kt"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "notes"
}
},
"source": [
"시가총액 순위면에서 \"KT\"는 1999년을 제외하고 지속적으로 하락세이다. (기업이 하락세라는 이야기는 아니다. 단지, 주식시장의 시가총액 순위 즉, 시가총액의 비중이 다른 종목에 비해 상대적으로 낮아졌다는 이야기이므로 종목 자체가 하락세라고 읽어서는 안된다)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f841c84a780>"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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s62jQep8o7dMP2BbYEvgIuDsiHk0p3dv6DceMGfPf5bq6Ourq6iosU7Vu4MB8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\ntdjnSGDjlNJREXEgsE9K6aC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"text/plain": [
"<matplotlib.figure.Figure at 0x7f841c84a588>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"df_kt.set_index('year')['marcap_pct'].plot(figsize=(16,6))"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true,
"slideshow": {
"slide_type": "notes"
}
},
"source": [
"2000년 말, 시가총액 1위 종목은 \"평화은행우선\"(평화은행 우선주) 이다."
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>rank</th>\n",
" <th>code</th>\n",
" <th>corp_name</th>\n",
" <th>marcap</th>\n",
" <th>marcap_pct</th>\n",
" <th>year</th>\n",
" </tr>\n",
" <tr>\n",
" <th>code</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>022875</th>\n",
" <td>9</td>\n",
" <td>022875</td>\n",
" <td>평화은행우선</td>\n",
" <td>7.304</td>\n",
" <td>0.016025</td>\n",
" <td>1999-12-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>022875</th>\n",
" <td>1</td>\n",
" <td>022875</td>\n",
" <td>평화은행우선</td>\n",
" <td>44.044</td>\n",
" <td>0.168510</td>\n",
" <td>2000-12-01</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" rank code corp_name marcap marcap_pct year\n",
"code \n",
"022875 9 022875 평화은행우선 7.304 0.016025 1999-12-01\n",
"022875 1 022875 평화은행우선 44.044 0.168510 2000-12-01"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_peace = df_master.ix[df_master['corp_name'] == '평화은행우선']\n",
"df_peace"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "notes"
}
},
"source": [
"평화은행은 1992년부터 2001년까지 있던 시중은행이다. 외환위기 이후 부실이 심화되어 1998년말에는 퇴출까지 검토되었다. \n",
"평화은행 우선주는 실제 시장에서 거래가 전혀 안되는 주식이었다(예금보험공사가 평화은행의 경영정상화를 위해 우선주 형식으로 출자).\n",
"\n",
"<strike>평화은행주 절반만 팔아도 시중은행 전부 살수 있다는 우스갯 소리까지 나돌았다.</strike>\n",
"\n",
"평화은행 우선주는 시가총액을 엄청나게 왜곡하는 결과를 초래했기 때문에 결국 시가총액 산정에서 제외된다. http://goo.gl/PCKUr7"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## 관심종목\n",
"\n",
"특정 종목들을 관찰하고 싶을 때가 많기 때문에 관심 종목(favorite stocks)의 목록을 관리 할 필요가 있다. \n",
"\n",
"다음 20개 종목을 관심종목이라고 가정하고, 이 목록을 가지고 DataFrame을 만든다."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
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" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>code</th>\n",
" <th>corp_name</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>005930</td>\n",
" <td>삼성전자</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>005380</td>\n",
" <td>현대차</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>015760</td>\n",
" <td>한국전력</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>028260</td>\n",
" <td>삼성물산</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>012330</td>\n",
" <td>현대모비스</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>090430</td>\n",
" <td>아모레</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>000660</td>\n",
" <td>하이닉스</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>051910</td>\n",
" <td>LG화학</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>032830</td>\n",
" <td>삼성생명</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>000270</td>\n",
" <td>기아차</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>035420</td>\n",
" <td>NAVER</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>018260</td>\n",
" <td>삼성SDS</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>055550</td>\n",
" <td>신한지주</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>017670</td>\n",
" <td>SK텔레콤</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>034730</td>\n",
" <td>SK</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>051900</td>\n",
" <td>LG생활건강</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>005490</td>\n",
" <td>POSCO</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>033780</td>\n",
" <td>KT&amp;G</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>000810</td>\n",
" <td>삼성화재</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>105560</td>\n",
" <td>KB금융</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" code corp_name\n",
"0 005930 삼성전자\n",
"1 005380 현대차\n",
"2 015760 한국전력\n",
"3 028260 삼성물산\n",
"4 012330 현대모비스\n",
"5 090430 아모레\n",
"6 000660 하이닉스\n",
"7 051910 LG화학\n",
"8 032830 삼성생명\n",
"9 000270 기아차\n",
"10 035420 NAVER\n",
"11 018260 삼성SDS\n",
"12 055550 신한지주\n",
"13 017670 SK텔레콤\n",
"14 034730 SK\n",
"15 051900 LG생활건강\n",
"16 005490 POSCO\n",
"17 033780 KT&G\n",
"18 000810 삼성화재\n",
"19 105560 KB금융"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"fav_code_corp = [\n",
" ('005930', '삼성전자'),\n",
" ('005380', '현대차'),\n",
" ('015760', '한국전력'),\n",
" ('028260', '삼성물산'),\n",
" ('012330', '현대모비스'),\n",
" ('090430', '아모레'),\n",
" ('000660', '하이닉스'),\n",
" ('051910', 'LG화학'),\n",
" ('032830', '삼성생명'),\n",
" ('000270', '기아차'),\n",
" ('035420', 'NAVER'),\n",
" ('018260', '삼성SDS'),\n",
" ('055550', '신한지주'),\n",
" ('017670', 'SK텔레콤'),\n",
" ('034730', 'SK'),\n",
" ('051900', 'LG생활건강'),\n",
" ('005490', 'POSCO'),\n",
" ('033780', 'KT&G'),\n",
" ('000810', '삼성화재'),\n",
" ('105560', 'KB금융'),\n",
"]\n",
"\n",
"fav_stocks = pd.DataFrame(data=fav_code_corp , columns=['code', 'corp_name'])\n",
"fav_stocks"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "notes"
}
},
"source": [
"관심종목(fav_stocks)목록을 관리하는 이유 중의 하나는 이름을 다르게 부여하거나, 간략하게 표기하기 위해서도 사용한다. 예를 들어, 종목코드 005490의 종목명이 'POSCO'인데, 한글 명칭 '포스코'로 바꾸어 사용할 수도 있을 것이다.\n",
"\n",
"df_master의 종목명을 fav_stocks(관심종목)에서 지정한 종목명을 기준으로 바꾼다."
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [],
"source": [
"for index, row in fav_stocks.iterrows():\n",
" df_master.loc[row['code'], 'corp_name'] = row['corp_name']"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true,
"slideshow": {
"slide_type": "notes"
}
},
"source": [
"df_master 의 종목명 전체를에서 관심종목 fav_stocks 에 해당하는 종목만 추출 한다."
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"299 rows\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>rank</th>\n",
" <th>code</th>\n",
" <th>corp_name</th>\n",
" <th>marcap</th>\n",
" <th>marcap_pct</th>\n",
" <th>year</th>\n",
" </tr>\n",
" <tr>\n",
" <th>code</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>015760</th>\n",
" <td>1</td>\n",
" <td>015760</td>\n",
" <td>한국전력</td>\n",
" <td>18.994194</td>\n",
" <td>0.134566</td>\n",
" <td>1995-12-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>005930</th>\n",
" <td>2</td>\n",
" <td>005930</td>\n",
" <td>삼성전자</td>\n",
" <td>7.665979</td>\n",
" <td>0.054310</td>\n",
" <td>1995-12-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>005490</th>\n",
" <td>3</td>\n",
" <td>005490</td>\n",
" <td>POSCO</td>\n",
" <td>4.760822</td>\n",
" <td>0.033728</td>\n",
" <td>1995-12-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>017670</th>\n",
" <td>4</td>\n",
" <td>017670</td>\n",
" <td>SK텔레콤</td>\n",
" <td>3.229820</td>\n",
" <td>0.022882</td>\n",
" <td>1995-12-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>005380</th>\n",
" <td>13</td>\n",
" <td>005380</td>\n",
" <td>현대차</td>\n",
" <td>1.408595</td>\n",
" <td>0.009979</td>\n",
" <td>1995-12-01</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" rank code corp_name marcap marcap_pct year\n",
"code \n",
"015760 1 015760 한국전력 18.994194 0.134566 1995-12-01\n",
"005930 2 005930 삼성전자 7.665979 0.054310 1995-12-01\n",
"005490 3 005490 POSCO 4.760822 0.033728 1995-12-01\n",
"017670 4 017670 SK텔레콤 3.229820 0.022882 1995-12-01\n",
"005380 13 005380 현대차 1.408595 0.009979 1995-12-01"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_fav = df_master.loc[df_master['code'].isin(fav_stocks['code'])]\n",
"\n",
"print (len(df_fav), 'rows')\n",
"df_fav.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## 피봇(pivot)\n",
"\n",
"DataFrame.pivot() 함수는 DataFrame의 형태를 바꾸는 함수이다. 즉, 축을 바꾸어 재정렬(reshape)하는 함수다.\n",
"순서대로 index, column, value 세개의 인자를 지정하며, 각 index와 column 조합에 대해 고유한 value 한 개를 가지고 있어야 한다.\n",
"\n",
"연도(year)를 인덱스로, 종목명(corp_name)을 컬럼으로 하고, 순위(rank)를 value로 지정하면, 연도별 종목의 순위를 아래와 같이 나열해 볼 수 있다. "
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>corp_name</th>\n",
" <th>KB금융</th>\n",
" <th>KT&amp;G</th>\n",
" <th>LG생활건강</th>\n",
" <th>LG화학</th>\n",
" <th>NAVER</th>\n",
" <th>POSCO</th>\n",
" <th>SK</th>\n",
" <th>SK텔레콤</th>\n",
" <th>기아차</th>\n",
" <th>삼성SDS</th>\n",
" <th>삼성물산</th>\n",
" <th>삼성생명</th>\n",
" <th>삼성전자</th>\n",
" <th>삼성화재</th>\n",
" <th>신한지주</th>\n",
" <th>아모레</th>\n",
" <th>하이닉스</th>\n",
" <th>한국전력</th>\n",
" <th>현대모비스</th>\n",
" <th>현대차</th>\n",
" </tr>\n",
" <tr>\n",
" <th>year</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>1995-12-01</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>3.0</td>\n",
" <td>NaN</td>\n",
" <td>4.0</td>\n",
" <td>15.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2.0</td>\n",
" <td>29.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>1.0</td>\n",
" <td>57.0</td>\n",
" <td>13.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1996-12-01</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2.0</td>\n",
" <td>NaN</td>\n",
" <td>4.0</td>\n",
" <td>10.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>3.0</td>\n",
" <td>28.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>16.0</td>\n",
" <td>1.0</td>\n",
" <td>49.0</td>\n",
" <td>24.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1997-12-01</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2.0</td>\n",
" <td>NaN</td>\n",
" <td>4.0</td>\n",
" <td>26.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>3.0</td>\n",
" <td>29.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>6.0</td>\n",
" <td>1.0</td>\n",
" <td>73.0</td>\n",
" <td>16.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1998-12-01</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>4.0</td>\n",
" <td>NaN</td>\n",
" <td>5.0</td>\n",
" <td>146.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>3.0</td>\n",
" <td>23.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>10.0</td>\n",
" <td>1.0</td>\n",
" <td>92.0</td>\n",
" <td>24.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1999-12-01</th>\n",
" <td>NaN</td>\n",
" <td>15.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>7.0</td>\n",
" <td>NaN</td>\n",
" <td>4.0</td>\n",
" <td>22.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2.0</td>\n",
" <td>47.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>8.0</td>\n",
" <td>5.0</td>\n",
" <td>101.0</td>\n",
" <td>18.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000-12-01</th>\n",
" <td>NaN</td>\n",
" <td>9.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>6.0</td>\n",
" <td>NaN</td>\n",
" <td>3.0</td>\n",
" <td>11.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2.0</td>\n",
" <td>28.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>19.0</td>\n",
" <td>5.0</td>\n",
" <td>62.0</td>\n",
" <td>13.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2001-12-01</th>\n",
" <td>NaN</td>\n",
" <td>11.0</td>\n",
" <td>80.0</td>\n",
" <td>35.0</td>\n",
" <td>NaN</td>\n",
" <td>6.0</td>\n",
" <td>NaN</td>\n",
" <td>2.0</td>\n",
" <td>13.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>1.0</td>\n",
" <td>17.0</td>\n",
" <td>9.0</td>\n",
" <td>NaN</td>\n",
" <td>22.0</td>\n",
" <td>5.0</td>\n",
" <td>33.0</td>\n",
" <td>8.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2002-12-01</th>\n",
" <td>NaN</td>\n",
" <td>18.0</td>\n",
" <td>68.0</td>\n",
" <td>20.0</td>\n",
" <td>112.0</td>\n",
" <td>6.0</td>\n",
" <td>NaN</td>\n",
" <td>2.0</td>\n",
" <td>13.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>1.0</td>\n",
" <td>16.0</td>\n",
" <td>10.0</td>\n",
" <td>NaN</td>\n",
" <td>32.0</td>\n",
" <td>5.0</td>\n",
" <td>25.0</td>\n",
" <td>7.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2003-12-01</th>\n",
" <td>NaN</td>\n",
" <td>19.0</td>\n",
" <td>99.0</td>\n",
" <td>21.0</td>\n",
" <td>58.0</td>\n",
" <td>4.0</td>\n",
" <td>NaN</td>\n",
" <td>2.0</td>\n",
" <td>17.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>1.0</td>\n",
" <td>23.0</td>\n",
" <td>11.0</td>\n",
" <td>NaN</td>\n",
" <td>32.0</td>\n",
" <td>5.0</td>\n",
" <td>12.0</td>\n",
" <td>7.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2004-12-01</th>\n",
" <td>NaN</td>\n",
" <td>18.0</td>\n",
" <td>116.0</td>\n",
" <td>31.0</td>\n",
" <td>57.0</td>\n",
" <td>3.0</td>\n",
" <td>NaN</td>\n",
" <td>4.0</td>\n",
" <td>26.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>1.0</td>\n",
" <td>25.0</td>\n",
" <td>13.0</td>\n",
" <td>NaN</td>\n",
" <td>20.0</td>\n",
" <td>2.0</td>\n",
" <td>16.0</td>\n",
" <td>7.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2005-12-01</th>\n",
" <td>NaN</td>\n",
" <td>20.0</td>\n",
" <td>113.0</td>\n",
" <td>38.0</td>\n",
" <td>33.0</td>\n",
" <td>5.0</td>\n",
" <td>NaN</td>\n",
" <td>9.0</td>\n",
" <td>15.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>1.0</td>\n",
" <td>24.0</td>\n",
" <td>10.0</td>\n",
" <td>NaN</td>\n",
" <td>7.0</td>\n",
" <td>3.0</td>\n",
" <td>19.0</td>\n",
" <td>4.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2006-12-01</th>\n",
" <td>NaN</td>\n",
" <td>19.0</td>\n",
" <td>71.0</td>\n",
" <td>52.0</td>\n",
" <td>31.0</td>\n",
" <td>3.0</td>\n",
" <td>NaN</td>\n",
" <td>6.0</td>\n",
" <td>35.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>1.0</td>\n",
" <td>22.0</td>\n",
" <td>5.0</td>\n",
" <td>44.0</td>\n",
" <td>8.0</td>\n",
" <td>2.0</td>\n",
" <td>25.0</td>\n",
" <td>9.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2007-12-01</th>\n",
" <td>NaN</td>\n",
" <td>20.0</td>\n",
" <td>72.0</td>\n",
" <td>37.0</td>\n",
" <td>22.0</td>\n",
" <td>2.0</td>\n",
" <td>NaN</td>\n",
" <td>7.0</td>\n",
" <td>65.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>1.0</td>\n",
" <td>18.0</td>\n",
" <td>6.0</td>\n",
" <td>58.0</td>\n",
" <td>19.0</td>\n",
" <td>4.0</td>\n",
" <td>33.0</td>\n",
" <td>10.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2008-12-01</th>\n",
" <td>6.0</td>\n",
" <td>8.0</td>\n",
" <td>43.0</td>\n",
" <td>26.0</td>\n",
" <td>20.0</td>\n",
" <td>2.0</td>\n",
" <td>NaN</td>\n",
" <td>4.0</td>\n",
" <td>61.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>1.0</td>\n",
" <td>12.0</td>\n",
" <td>7.0</td>\n",
" <td>37.0</td>\n",
" <td>41.0</td>\n",
" <td>3.0</td>\n",
" <td>25.0</td>\n",
" <td>13.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2009-12-01</th>\n",
" <td>4.0</td>\n",
" <td>24.0</td>\n",
" <td>41.0</td>\n",
" <td>9.0</td>\n",
" <td>23.0</td>\n",
" <td>2.0</td>\n",
" <td>81.0</td>\n",
" <td>11.0</td>\n",
" <td>29.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>1.0</td>\n",
" <td>21.0</td>\n",
" <td>6.0</td>\n",
" <td>38.0</td>\n",
" <td>12.0</td>\n",
" <td>5.0</td>\n",
" <td>8.0</td>\n",
" <td>3.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2010-12-01</th>\n",
" <td>8.0</td>\n",
" <td>33.0</td>\n",
" <td>45.0</td>\n",
" <td>6.0</td>\n",
" <td>24.0</td>\n",
" <td>2.0</td>\n",
" <td>61.0</td>\n",
" <td>18.0</td>\n",
" <td>10.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>9.0</td>\n",
" <td>1.0</td>\n",
" <td>25.0</td>\n",
" <td>7.0</td>\n",
" <td>43.0</td>\n",
" <td>17.0</td>\n",
" <td>11.0</td>\n",
" <td>5.0</td>\n",
" <td>3.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-12-01</th>\n",
" <td>12.0</td>\n",
" <td>17.0</td>\n",
" <td>30.0</td>\n",
" <td>6.0</td>\n",
" <td>21.0</td>\n",
" <td>3.0</td>\n",
" <td>44.0</td>\n",
" <td>15.0</td>\n",
" <td>5.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>10.0</td>\n",
" <td>1.0</td>\n",
" <td>22.0</td>\n",
" <td>8.0</td>\n",
" <td>41.0</td>\n",
" <td>14.0</td>\n",
" <td>9.0</td>\n",
" <td>4.0</td>\n",
" <td>2.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012-12-01</th>\n",
" <td>14.0</td>\n",
" <td>20.0</td>\n",
" <td>24.0</td>\n",
" <td>6.0</td>\n",
" <td>22.0</td>\n",
" <td>3.0</td>\n",
" <td>49.0</td>\n",
" <td>15.0</td>\n",
" <td>5.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>9.0</td>\n",
" <td>1.0</td>\n",
" <td>23.0</td>\n",
" <td>10.0</td>\n",
" <td>37.0</td>\n",
" <td>12.0</td>\n",
" <td>7.0</td>\n",
" <td>4.0</td>\n",
" <td>2.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-12-01</th>\n",
" <td>15.0</td>\n",
" <td>23.0</td>\n",
" <td>29.0</td>\n",
" <td>12.0</td>\n",
" <td>6.0</td>\n",
" <td>4.0</td>\n",
" <td>38.0</td>\n",
" <td>14.0</td>\n",
" <td>8.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>11.0</td>\n",
" <td>1.0</td>\n",
" <td>19.0</td>\n",
" <td>9.0</td>\n",
" <td>45.0</td>\n",
" <td>5.0</td>\n",
" <td>10.0</td>\n",
" <td>3.0</td>\n",
" <td>2.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-12-01</th>\n",
" <td>15.0</td>\n",
" <td>23.0</td>\n",
" <td>24.0</td>\n",
" <td>19.0</td>\n",
" <td>7.0</td>\n",
" <td>5.0</td>\n",
" <td>21.0</td>\n",
" <td>11.0</td>\n",
" <td>13.0</td>\n",
" <td>10.0</td>\n",
" <td>12.0</td>\n",
" <td>8.0</td>\n",
" <td>1.0</td>\n",
" <td>16.0</td>\n",
" <td>14.0</td>\n",
" <td>17.0</td>\n",
" <td>3.0</td>\n",
" <td>4.0</td>\n",
" <td>9.0</td>\n",
" <td>2.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-12-01</th>\n",
" <td>21.0</td>\n",
" <td>19.0</td>\n",
" <td>17.0</td>\n",
" <td>9.0</td>\n",
" <td>12.0</td>\n",
" <td>18.0</td>\n",
" <td>16.0</td>\n",
" <td>15.0</td>\n",
" <td>11.0</td>\n",
" <td>13.0</td>\n",
" <td>4.0</td>\n",
" <td>10.0</td>\n",
" <td>1.0</td>\n",
" <td>20.0</td>\n",
" <td>14.0</td>\n",
" <td>7.0</td>\n",
" <td>8.0</td>\n",
" <td>3.0</td>\n",
" <td>6.0</td>\n",
" <td>2.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"corp_name KB금융 KT&G LG생활건강 LG화학 NAVER POSCO SK SK텔레콤 기아차 삼성SDS \\\n",
"year \n",
"1995-12-01 NaN NaN NaN NaN NaN 3.0 NaN 4.0 15.0 NaN \n",
"1996-12-01 NaN NaN NaN NaN NaN 2.0 NaN 4.0 10.0 NaN \n",
"1997-12-01 NaN NaN NaN NaN NaN 2.0 NaN 4.0 26.0 NaN \n",
"1998-12-01 NaN NaN NaN NaN NaN 4.0 NaN 5.0 146.0 NaN \n",
"1999-12-01 NaN 15.0 NaN NaN NaN 7.0 NaN 4.0 22.0 NaN \n",
"2000-12-01 NaN 9.0 NaN NaN NaN 6.0 NaN 3.0 11.0 NaN \n",
"2001-12-01 NaN 11.0 80.0 35.0 NaN 6.0 NaN 2.0 13.0 NaN \n",
"2002-12-01 NaN 18.0 68.0 20.0 112.0 6.0 NaN 2.0 13.0 NaN \n",
"2003-12-01 NaN 19.0 99.0 21.0 58.0 4.0 NaN 2.0 17.0 NaN \n",
"2004-12-01 NaN 18.0 116.0 31.0 57.0 3.0 NaN 4.0 26.0 NaN \n",
"2005-12-01 NaN 20.0 113.0 38.0 33.0 5.0 NaN 9.0 15.0 NaN \n",
"2006-12-01 NaN 19.0 71.0 52.0 31.0 3.0 NaN 6.0 35.0 NaN \n",
"2007-12-01 NaN 20.0 72.0 37.0 22.0 2.0 NaN 7.0 65.0 NaN \n",
"2008-12-01 6.0 8.0 43.0 26.0 20.0 2.0 NaN 4.0 61.0 NaN \n",
"2009-12-01 4.0 24.0 41.0 9.0 23.0 2.0 81.0 11.0 29.0 NaN \n",
"2010-12-01 8.0 33.0 45.0 6.0 24.0 2.0 61.0 18.0 10.0 NaN \n",
"2011-12-01 12.0 17.0 30.0 6.0 21.0 3.0 44.0 15.0 5.0 NaN \n",
"2012-12-01 14.0 20.0 24.0 6.0 22.0 3.0 49.0 15.0 5.0 NaN \n",
"2013-12-01 15.0 23.0 29.0 12.0 6.0 4.0 38.0 14.0 8.0 NaN \n",
"2014-12-01 15.0 23.0 24.0 19.0 7.0 5.0 21.0 11.0 13.0 10.0 \n",
"2015-12-01 21.0 19.0 17.0 9.0 12.0 18.0 16.0 15.0 11.0 13.0 \n",
"\n",
"corp_name 삼성물산 삼성생명 삼성전자 삼성화재 신한지주 아모레 하이닉스 한국전력 현대모비스 현대차 \n",
"year \n",
"1995-12-01 NaN NaN 2.0 29.0 NaN NaN NaN 1.0 57.0 13.0 \n",
"1996-12-01 NaN NaN 3.0 28.0 NaN NaN 16.0 1.0 49.0 24.0 \n",
"1997-12-01 NaN NaN 3.0 29.0 NaN NaN 6.0 1.0 73.0 16.0 \n",
"1998-12-01 NaN NaN 3.0 23.0 NaN NaN 10.0 1.0 92.0 24.0 \n",
"1999-12-01 NaN NaN 2.0 47.0 NaN NaN 8.0 5.0 101.0 18.0 \n",
"2000-12-01 NaN NaN 2.0 28.0 NaN NaN 19.0 5.0 62.0 13.0 \n",
"2001-12-01 NaN NaN 1.0 17.0 9.0 NaN 22.0 5.0 33.0 8.0 \n",
"2002-12-01 NaN NaN 1.0 16.0 10.0 NaN 32.0 5.0 25.0 7.0 \n",
"2003-12-01 NaN NaN 1.0 23.0 11.0 NaN 32.0 5.0 12.0 7.0 \n",
"2004-12-01 NaN NaN 1.0 25.0 13.0 NaN 20.0 2.0 16.0 7.0 \n",
"2005-12-01 NaN NaN 1.0 24.0 10.0 NaN 7.0 3.0 19.0 4.0 \n",
"2006-12-01 NaN NaN 1.0 22.0 5.0 44.0 8.0 2.0 25.0 9.0 \n",
"2007-12-01 NaN NaN 1.0 18.0 6.0 58.0 19.0 4.0 33.0 10.0 \n",
"2008-12-01 NaN NaN 1.0 12.0 7.0 37.0 41.0 3.0 25.0 13.0 \n",
"2009-12-01 NaN NaN 1.0 21.0 6.0 38.0 12.0 5.0 8.0 3.0 \n",
"2010-12-01 NaN 9.0 1.0 25.0 7.0 43.0 17.0 11.0 5.0 3.0 \n",
"2011-12-01 NaN 10.0 1.0 22.0 8.0 41.0 14.0 9.0 4.0 2.0 \n",
"2012-12-01 NaN 9.0 1.0 23.0 10.0 37.0 12.0 7.0 4.0 2.0 \n",
"2013-12-01 NaN 11.0 1.0 19.0 9.0 45.0 5.0 10.0 3.0 2.0 \n",
"2014-12-01 12.0 8.0 1.0 16.0 14.0 17.0 3.0 4.0 9.0 2.0 \n",
"2015-12-01 4.0 10.0 1.0 20.0 14.0 7.0 8.0 3.0 6.0 2.0 "
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pivoted = df_fav.pivot('year', 'corp_name', 'rank')\n",
"pivoted"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "notes"
}
},
"source": [
"1995~2015년까지 관심종목의 시가총액 순위표가 된다. NaN 이 눈에 띄는데 데이터가 없다는 뜻이며, 이는 상장하기 전이라는 의미가 된다. 예를 들어, '아모레' 종목의 경우 2006년에 상장된 종목임을 알 수 있다.\n",
"\n",
"실제, 아모레퍼시픽은 2006년 6월 태평양의 기업 분할로 화장품 사업 부문이 자회사로 설립된 법인이다 (존속법인 태평양은 지주회사가 되었다)\n",
"\n",
"상당히 최근(2014년)에 상장한 두 기업을 보자. 삼성SDS는 2014년에 신규상장되었다.\n",
"삼성물산은 약간 복잡하다. 제일모직이 2014년에 상장했고, 2015년 삼성물산과 제일모직이 통합 상장하면서 회사 이름은 삼성물산이 되었다."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## 순위 변동 시각화\n",
"\n",
"관심종목(fav_stocks)중의 앞쪽 7개 종목의 순위 변동을 살펴보자."
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>code</th>\n",
" <th>corp_name</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>005930</td>\n",
" <td>삼성전자</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>005380</td>\n",
" <td>현대차</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>015760</td>\n",
" <td>한국전력</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>028260</td>\n",
" <td>삼성물산</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>012330</td>\n",
" <td>현대모비스</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>090430</td>\n",
" <td>아모레</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>000660</td>\n",
" <td>하이닉스</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" code corp_name\n",
"0 005930 삼성전자\n",
"1 005380 현대차\n",
"2 015760 한국전력\n",
"3 028260 삼성물산\n",
"4 012330 현대모비스\n",
"5 090430 아모레\n",
"6 000660 하이닉스"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"fav7 = fav_stocks[:7]\n",
"fav7"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "notes"
}
},
"source": [
"pivoted 에서 fav7의 종목명인 fav7['corp_name'] 의 컬럼만 추출한다."
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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" text-align: left;\n",
" }\n",
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" .dataframe tbody tr th {\n",
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>corp_name</th>\n",
" <th>삼성전자</th>\n",
" <th>현대차</th>\n",
" <th>한국전력</th>\n",
" <th>삼성물산</th>\n",
" <th>현대모비스</th>\n",
" <th>아모레</th>\n",
" <th>하이닉스</th>\n",
" </tr>\n",
" <tr>\n",
" <th>year</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>1995-12-01</th>\n",
" <td>2.0</td>\n",
" <td>13.0</td>\n",
" <td>1.0</td>\n",
" <td>NaN</td>\n",
" <td>57.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1996-12-01</th>\n",
" <td>3.0</td>\n",
" <td>24.0</td>\n",
" <td>1.0</td>\n",
" <td>NaN</td>\n",
" <td>49.0</td>\n",
" <td>NaN</td>\n",
" <td>16.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1997-12-01</th>\n",
" <td>3.0</td>\n",
" <td>16.0</td>\n",
" <td>1.0</td>\n",
" <td>NaN</td>\n",
" <td>73.0</td>\n",
" <td>NaN</td>\n",
" <td>6.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1998-12-01</th>\n",
" <td>3.0</td>\n",
" <td>24.0</td>\n",
" <td>1.0</td>\n",
" <td>NaN</td>\n",
" <td>92.0</td>\n",
" <td>NaN</td>\n",
" <td>10.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1999-12-01</th>\n",
" <td>2.0</td>\n",
" <td>18.0</td>\n",
" <td>5.0</td>\n",
" <td>NaN</td>\n",
" <td>101.0</td>\n",
" <td>NaN</td>\n",
" <td>8.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000-12-01</th>\n",
" <td>2.0</td>\n",
" <td>13.0</td>\n",
" <td>5.0</td>\n",
" <td>NaN</td>\n",
" <td>62.0</td>\n",
" <td>NaN</td>\n",
" <td>19.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2001-12-01</th>\n",
" <td>1.0</td>\n",
" <td>8.0</td>\n",
" <td>5.0</td>\n",
" <td>NaN</td>\n",
" <td>33.0</td>\n",
" <td>NaN</td>\n",
" <td>22.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2002-12-01</th>\n",
" <td>1.0</td>\n",
" <td>7.0</td>\n",
" <td>5.0</td>\n",
" <td>NaN</td>\n",
" <td>25.0</td>\n",
" <td>NaN</td>\n",
" <td>32.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2003-12-01</th>\n",
" <td>1.0</td>\n",
" <td>7.0</td>\n",
" <td>5.0</td>\n",
" <td>NaN</td>\n",
" <td>12.0</td>\n",
" <td>NaN</td>\n",
" <td>32.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2004-12-01</th>\n",
" <td>1.0</td>\n",
" <td>7.0</td>\n",
" <td>2.0</td>\n",
" <td>NaN</td>\n",
" <td>16.0</td>\n",
" <td>NaN</td>\n",
" <td>20.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2005-12-01</th>\n",
" <td>1.0</td>\n",
" <td>4.0</td>\n",
" <td>3.0</td>\n",
" <td>NaN</td>\n",
" <td>19.0</td>\n",
" <td>NaN</td>\n",
" <td>7.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2006-12-01</th>\n",
" <td>1.0</td>\n",
" <td>9.0</td>\n",
" <td>2.0</td>\n",
" <td>NaN</td>\n",
" <td>25.0</td>\n",
" <td>44.0</td>\n",
" <td>8.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2007-12-01</th>\n",
" <td>1.0</td>\n",
" <td>10.0</td>\n",
" <td>4.0</td>\n",
" <td>NaN</td>\n",
" <td>33.0</td>\n",
" <td>58.0</td>\n",
" <td>19.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2008-12-01</th>\n",
" <td>1.0</td>\n",
" <td>13.0</td>\n",
" <td>3.0</td>\n",
" <td>NaN</td>\n",
" <td>25.0</td>\n",
" <td>37.0</td>\n",
" <td>41.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2009-12-01</th>\n",
" <td>1.0</td>\n",
" <td>3.0</td>\n",
" <td>5.0</td>\n",
" <td>NaN</td>\n",
" <td>8.0</td>\n",
" <td>38.0</td>\n",
" <td>12.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2010-12-01</th>\n",
" <td>1.0</td>\n",
" <td>3.0</td>\n",
" <td>11.0</td>\n",
" <td>NaN</td>\n",
" <td>5.0</td>\n",
" <td>43.0</td>\n",
" <td>17.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-12-01</th>\n",
" <td>1.0</td>\n",
" <td>2.0</td>\n",
" <td>9.0</td>\n",
" <td>NaN</td>\n",
" <td>4.0</td>\n",
" <td>41.0</td>\n",
" <td>14.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012-12-01</th>\n",
" <td>1.0</td>\n",
" <td>2.0</td>\n",
" <td>7.0</td>\n",
" <td>NaN</td>\n",
" <td>4.0</td>\n",
" <td>37.0</td>\n",
" <td>12.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-12-01</th>\n",
" <td>1.0</td>\n",
" <td>2.0</td>\n",
" <td>10.0</td>\n",
" <td>NaN</td>\n",
" <td>3.0</td>\n",
" <td>45.0</td>\n",
" <td>5.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-12-01</th>\n",
" <td>1.0</td>\n",
" <td>2.0</td>\n",
" <td>4.0</td>\n",
" <td>12.0</td>\n",
" <td>9.0</td>\n",
" <td>17.0</td>\n",
" <td>3.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-12-01</th>\n",
" <td>1.0</td>\n",
" <td>2.0</td>\n",
" <td>3.0</td>\n",
" <td>4.0</td>\n",
" <td>6.0</td>\n",
" <td>7.0</td>\n",
" <td>8.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"corp_name 삼성전자 현대차 한국전력 삼성물산 현대모비스 아모레 하이닉스\n",
"year \n",
"1995-12-01 2.0 13.0 1.0 NaN 57.0 NaN NaN\n",
"1996-12-01 3.0 24.0 1.0 NaN 49.0 NaN 16.0\n",
"1997-12-01 3.0 16.0 1.0 NaN 73.0 NaN 6.0\n",
"1998-12-01 3.0 24.0 1.0 NaN 92.0 NaN 10.0\n",
"1999-12-01 2.0 18.0 5.0 NaN 101.0 NaN 8.0\n",
"2000-12-01 2.0 13.0 5.0 NaN 62.0 NaN 19.0\n",
"2001-12-01 1.0 8.0 5.0 NaN 33.0 NaN 22.0\n",
"2002-12-01 1.0 7.0 5.0 NaN 25.0 NaN 32.0\n",
"2003-12-01 1.0 7.0 5.0 NaN 12.0 NaN 32.0\n",
"2004-12-01 1.0 7.0 2.0 NaN 16.0 NaN 20.0\n",
"2005-12-01 1.0 4.0 3.0 NaN 19.0 NaN 7.0\n",
"2006-12-01 1.0 9.0 2.0 NaN 25.0 44.0 8.0\n",
"2007-12-01 1.0 10.0 4.0 NaN 33.0 58.0 19.0\n",
"2008-12-01 1.0 13.0 3.0 NaN 25.0 37.0 41.0\n",
"2009-12-01 1.0 3.0 5.0 NaN 8.0 38.0 12.0\n",
"2010-12-01 1.0 3.0 11.0 NaN 5.0 43.0 17.0\n",
"2011-12-01 1.0 2.0 9.0 NaN 4.0 41.0 14.0\n",
"2012-12-01 1.0 2.0 7.0 NaN 4.0 37.0 12.0\n",
"2013-12-01 1.0 2.0 10.0 NaN 3.0 45.0 5.0\n",
"2014-12-01 1.0 2.0 4.0 12.0 9.0 17.0 3.0\n",
"2015-12-01 1.0 2.0 3.0 4.0 6.0 7.0 8.0"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cols = fav7['corp_name']\n",
"\n",
"pivoted7 = pivoted[cols]\n",
"pivoted7"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## 연도별 시가총액 순위 차트\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"pivoted 데이터를 matplotlib 차트로 그린다.\n",
"\n",
"matplotlib.font_manager를 사용하여 한글 폰트(나눔고딕)을 지정하였다. 축(ax)의 invert_yaxis() 함수를 사용하여, Y축을 뒤집은 것도 보아두자. 순위 데이터는 작을 수록 높은 순위이기 때문에 Y축을 뒤집어서 표현하였다."
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
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sqhMqfKf6ErS6cjvQ3LlzWbBgAU2bNiU7O7vs5xEREcyaNYuHH37YaHNxKrK2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\nVxqwOtOxxNdNyXisYYnE8rCwEXPrGe4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nnnuYMWMGy5cvZ86cOWzbtg0AIbQkJk4lL+9o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\n9wAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f841c84a860>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"import matplotlib.font_manager as fm\n",
"\n",
"path = '/usr/share/fonts/truetype/nanum/NanumGothic.ttf'\n",
"fontprop = fm.FontProperties(fname=path, size=14)\n",
"\n",
"ax = pivoted7.plot(figsize=(12,6), grid=True)\n",
"ax.invert_yaxis()\n",
"\n",
"legend = ax.legend(loc='lower right')\n",
"for text in legend.texts:\n",
" text.set_font_properties(fontprop)\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "notes"
}
},
"source": [
"삼성전자는 거의 부동의 1위이며, 1999년 100위권 현대모비스의 지속적인 약진이 눈에 띈다. 2006년 상장한 아모레는 2014년 이후 급속하게 성장하여 시가총액 7위의 기업이 되었다."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "notes"
}
},
"source": [
"앞서 관심종목으로 지정한 20개 종목을 모두 그려본다."
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"image/png": 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cJk3S9/rcOcGW2PmXY2Jgxoyu9xAFQlVDE6qqKrEZD1YKoqLEZr15LPg118j8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\nJCIiIiIi4vn/jt9lPj3cfskAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f841cbbf400>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ax = pivoted.plot(figsize=(12,8), grid=True)\n",
"ax.invert_yaxis()\n",
"\n",
"legend = ax.legend(loc='lower right', bbox_to_anchor=(1.2, -0.15))\n",
"for text in legend.texts:\n",
" text.set_font_properties(fontprop)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "notes"
}
},
"source": [
"다소 순위가 많이 들죽 날쭉 했던 종목으로 '기아차'가 있다. '기아차'의 시가총액 순위만 따로 보면,"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"image/png": 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vWLscKBQS86wowu6fGUjMS+uhAqEJPjZpAu2b8jKWjAo+NoGAkJhnRRF2/8xA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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f841c9674a8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ax = pivoted['기아차'].plot(figsize=(12,8), grid=True, color='r')\n",
"ax.invert_yaxis()"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "notes"
}
},
"source": [
"'기아차' 종목만 좀 더 자세히 살펴보자."
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>rank</th>\n",
" <th>code</th>\n",
" <th>corp_name</th>\n",
" <th>marcap</th>\n",
" <th>marcap_pct</th>\n",
" </tr>\n",
" <tr>\n",
" <th>year</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>1995-12-01</th>\n",
" <td>15</td>\n",
" <td>000270</td>\n",
" <td>기아차</td>\n",
" <td>1.378519</td>\n",
" <td>0.009766</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1996-12-01</th>\n",
" <td>10</td>\n",
" <td>000270</td>\n",
" <td>기아차</td>\n",
" <td>1.247868</td>\n",
" <td>0.009985</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1997-12-01</th>\n",
" <td>26</td>\n",
" <td>000270</td>\n",
" <td>기아차</td>\n",
" <td>0.464278</td>\n",
" <td>0.005948</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1998-12-01</th>\n",
" <td>146</td>\n",
" <td>000270</td>\n",
" <td>기아차</td>\n",
" <td>0.124969</td>\n",
" <td>0.000858</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1999-12-01</th>\n",
" <td>22</td>\n",
" <td>000270</td>\n",
" <td>기아차</td>\n",
" <td>3.191127</td>\n",
" <td>0.007001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000-12-01</th>\n",
" <td>11</td>\n",
" <td>000270</td>\n",
" <td>기아차</td>\n",
" <td>3.192142</td>\n",
" <td>0.012213</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2001-12-01</th>\n",
" <td>13</td>\n",
" <td>000270</td>\n",
" <td>기아차</td>\n",
" <td>3.274633</td>\n",
" <td>0.010634</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2002-12-01</th>\n",
" <td>13</td>\n",
" <td>000270</td>\n",
" <td>기아차</td>\n",
" <td>3.253628</td>\n",
" <td>0.010979</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2003-12-01</th>\n",
" <td>17</td>\n",
" <td>000270</td>\n",
" <td>기아차</td>\n",
" <td>3.921062</td>\n",
" <td>0.009977</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2004-12-01</th>\n",
" <td>26</td>\n",
" <td>000270</td>\n",
" <td>기아차</td>\n",
" <td>3.784812</td>\n",
" <td>0.008524</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2005-12-01</th>\n",
" <td>15</td>\n",
" <td>000270</td>\n",
" <td>기아차</td>\n",
" <td>9.218969</td>\n",
" <td>0.012694</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2006-12-01</th>\n",
" <td>35</td>\n",
" <td>000270</td>\n",
" <td>기아차</td>\n",
" <td>4.670250</td>\n",
" <td>0.006013</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2007-12-01</th>\n",
" <td>65</td>\n",
" <td>000270</td>\n",
" <td>기아차</td>\n",
" <td>3.507028</td>\n",
" <td>0.003334</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2008-12-01</th>\n",
" <td>61</td>\n",
" <td>000270</td>\n",
" <td>기아차</td>\n",
" <td>2.274359</td>\n",
" <td>0.003650</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2009-12-01</th>\n",
" <td>29</td>\n",
" <td>000270</td>\n",
" <td>기아차</td>\n",
" <td>7.773414</td>\n",
" <td>0.007981</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2010-12-01</th>\n",
" <td>10</td>\n",
" <td>000270</td>\n",
" <td>기아차</td>\n",
" <td>20.117305</td>\n",
" <td>0.016225</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-12-01</th>\n",
" <td>5</td>\n",
" <td>000270</td>\n",
" <td>기아차</td>\n",
" <td>26.509433</td>\n",
" <td>0.023137</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012-12-01</th>\n",
" <td>5</td>\n",
" <td>000270</td>\n",
" <td>기아차</td>\n",
" <td>22.903029</td>\n",
" <td>0.018128</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-12-01</th>\n",
" <td>8</td>\n",
" <td>000270</td>\n",
" <td>기아차</td>\n",
" <td>22.740884</td>\n",
" <td>0.017410</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-12-01</th>\n",
" <td>13</td>\n",
" <td>000270</td>\n",
" <td>기아차</td>\n",
" <td>21.200503</td>\n",
" <td>0.015860</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-12-01</th>\n",
" <td>11</td>\n",
" <td>000270</td>\n",
" <td>기아차</td>\n",
" <td>21.889621</td>\n",
" <td>0.014967</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" rank code corp_name marcap marcap_pct\n",
"year \n",
"1995-12-01 15 000270 기아차 1.378519 0.009766\n",
"1996-12-01 10 000270 기아차 1.247868 0.009985\n",
"1997-12-01 26 000270 기아차 0.464278 0.005948\n",
"1998-12-01 146 000270 기아차 0.124969 0.000858\n",
"1999-12-01 22 000270 기아차 3.191127 0.007001\n",
"2000-12-01 11 000270 기아차 3.192142 0.012213\n",
"2001-12-01 13 000270 기아차 3.274633 0.010634\n",
"2002-12-01 13 000270 기아차 3.253628 0.010979\n",
"2003-12-01 17 000270 기아차 3.921062 0.009977\n",
"2004-12-01 26 000270 기아차 3.784812 0.008524\n",
"2005-12-01 15 000270 기아차 9.218969 0.012694\n",
"2006-12-01 35 000270 기아차 4.670250 0.006013\n",
"2007-12-01 65 000270 기아차 3.507028 0.003334\n",
"2008-12-01 61 000270 기아차 2.274359 0.003650\n",
"2009-12-01 29 000270 기아차 7.773414 0.007981\n",
"2010-12-01 10 000270 기아차 20.117305 0.016225\n",
"2011-12-01 5 000270 기아차 26.509433 0.023137\n",
"2012-12-01 5 000270 기아차 22.903029 0.018128\n",
"2013-12-01 8 000270 기아차 22.740884 0.017410\n",
"2014-12-01 13 000270 기아차 21.200503 0.015860\n",
"2015-12-01 11 000270 기아차 21.889621 0.014967"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_kia = df_master.ix[df_master['corp_name'] == '기아차']\n",
"df_kia.set_index('year', inplace=True)\n",
"df_kia"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "notes"
}
},
"source": [
"1996년까지 기아차는 매우 건실한 회사였다(전문 경영인 체제와 지분 구조도 매우 모범적이라고 평가되었다). \n",
"시가총액 1996년 12조였다가, 2년 뒤인 1998년 1/10로 줄었다. 1997년 봄부터 어려워져 결국 10월 법정관리에 넘어가고 1998년에 현대자동차에 매각된다. 1998년 146위 까지 떨어졌다."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "notes"
}
},
"source": [
"시가총액의 변화를 살펴보면, 시가총액 순위의 변화와 비슷한 양상을 보인다. 2015년 기준으로 시가총액 20조원이 넘는 건실한 자동차회사가 되었다."
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f841c9bb400>"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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WvDD18cfj/rHHwu9+Bz165BtXpdjKIkmS1IyGNpbDD883jvZk3XXh3/+G88+Hzp3jBaGD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\nYS5JObnxRvjkExgyJLYkpKD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eto/spc9K0hsiYsrjCesf2dNyN/CLiNiUyQdb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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f841cb62ba8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"df_kia['marcap'].plot(figsize=(12,8), grid=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"----\n",
"#### 2017 FinanceData http://fb.com/financedata http://financedata.github.com"
]
}
],
"metadata": {
"celltoolbar": "Slideshow",
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
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