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HyeongWookKim/Ch 1. 서울시 구별 CCTV 현황 분석.ipynb

Created May 27, 2020 13:54
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[Ch 1. 서울시 구별 CCTV 현황 분석] from "파이썬으로 데이터 주무르기(민형기 지음)"
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Ch 1. 서울시 구별 CCTV 현황 분석.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 1장. 서울시 구별 CCTV 현황 분석\n",
"<분석 목적>\n",
"- 서울시 각 구별 CCTV 수를 파악하고, 인구 대비 CCTV 비율을 파악해서 순위를 비교해본다\n",
"- 인구 대비 CCTV 평균치를 확인하고, 그로부터 CCTV가 과하게 부족한 구를 확인해본다\n",
"- 모든 과정을 시각화하여 보기 쉽게 만들 예정이다\n",
"\n",
"<데이터 설명>\n",
"- 1. 서울시 자치구 년도별 CCTV 설치 현황 데이터: \"01. CCTV_in_Seoul.csv\"\n",
" - <변수 설명>\n",
" - 기관명: 강남구, 강동구, 강북구, ...\n",
" - 소계: 설치된 CCTV 개수의 총합\n",
" - 2013년도 이전: 2013년도 이전에 설치된 CCTV 개수\n",
" - 2014년: 2014년에 설치된 CCTV 개수\n",
" - 2015년: 2015년에 설치된 CCTV 개수\n",
" - 2016년: 2016년에 설치된 CCTV 개수\n",
"- 2. 서울시 인구 통계 현황 데이터: \"01. population_in_Seoul.xls\"\n",
" - <변수 설명>\n",
" - 자치구: 강남구, 강동구, 강북구, ...\n",
" - 계: 인구 총 합계\n",
" - 계.1: 한국인 인구 총 합계\n",
" - 계.2: 외국인 인구 총 합계\n",
" - 65세이상고령자: 65세 이상 고령자 인구 수"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-25T06:29:11.976168Z",
"start_time": "2020-05-25T06:29:11.495277Z"
}
},
"outputs": [],
"source": [
"import pandas as pd"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 데이터 불러오기"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 서울시 자치구 년도별 CCTV 설치 현황 데이터 불러오기"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-25T06:29:13.212112Z",
"start_time": "2020-05-25T06:29:13.187148Z"
}
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>기관명</th>\n",
" <th>소계</th>\n",
" <th>2013년도 이전</th>\n",
" <th>2014년</th>\n",
" <th>2015년</th>\n",
" <th>2016년</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>강남구</td>\n",
" <td>2780</td>\n",
" <td>1292</td>\n",
" <td>430</td>\n",
" <td>584</td>\n",
" <td>932</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>강동구</td>\n",
" <td>773</td>\n",
" <td>379</td>\n",
" <td>99</td>\n",
" <td>155</td>\n",
" <td>377</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>강북구</td>\n",
" <td>748</td>\n",
" <td>369</td>\n",
" <td>120</td>\n",
" <td>138</td>\n",
" <td>204</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>강서구</td>\n",
" <td>884</td>\n",
" <td>388</td>\n",
" <td>258</td>\n",
" <td>184</td>\n",
" <td>81</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>관악구</td>\n",
" <td>1496</td>\n",
" <td>846</td>\n",
" <td>260</td>\n",
" <td>390</td>\n",
" <td>613</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 기관명 소계 2013년도 이전 2014년 2015년 2016년\n",
"0 강남구 2780 1292 430 584 932\n",
"1 강동구 773 379 99 155 377\n",
"2 강북구 748 369 120 138 204\n",
"3 강서구 884 388 258 184 81\n",
"4 관악구 1496 846 260 390 613"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"CCTV_Seoul = pd.read_csv('../data/01. CCTV_in_Seoul.csv', encoding = 'utf-8')\n",
"CCTV_Seoul.head()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-25T06:29:13.782518Z",
"start_time": "2020-05-25T06:29:13.776538Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"Index(['기관명', '소계', '2013년도 이전', '2014년', '2015년', '2016년'], dtype='object')"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 불러온 CCTV_Seoul 데이터의 컬럼 명들을 반환\n",
"CCTV_Seoul.columns"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-25T06:29:14.224122Z",
"start_time": "2020-05-25T06:29:14.218138Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"'기관명'"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"CCTV_Seoul.columns[0]"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-25T06:29:14.602699Z",
"start_time": "2020-05-25T06:29:14.587772Z"
}
},
"outputs": [
{
"data": {
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"<div>\n",
"<style scoped>\n",
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" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>구별</th>\n",
" <th>소계</th>\n",
" <th>2013년도 이전</th>\n",
" <th>2014년</th>\n",
" <th>2015년</th>\n",
" <th>2016년</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>강남구</td>\n",
" <td>2780</td>\n",
" <td>1292</td>\n",
" <td>430</td>\n",
" <td>584</td>\n",
" <td>932</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>강동구</td>\n",
" <td>773</td>\n",
" <td>379</td>\n",
" <td>99</td>\n",
" <td>155</td>\n",
" <td>377</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>강북구</td>\n",
" <td>748</td>\n",
" <td>369</td>\n",
" <td>120</td>\n",
" <td>138</td>\n",
" <td>204</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>강서구</td>\n",
" <td>884</td>\n",
" <td>388</td>\n",
" <td>258</td>\n",
" <td>184</td>\n",
" <td>81</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>관악구</td>\n",
" <td>1496</td>\n",
" <td>846</td>\n",
" <td>260</td>\n",
" <td>390</td>\n",
" <td>613</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 구별 소계 2013년도 이전 2014년 2015년 2016년\n",
"0 강남구 2780 1292 430 584 932\n",
"1 강동구 773 379 99 155 377\n",
"2 강북구 748 369 120 138 204\n",
"3 강서구 884 388 258 184 81\n",
"4 관악구 1496 846 260 390 613"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# '기관명'이라는 변수명을 '구별'로 변경\n",
"# <참고> inplace = True 로 설정하면, 원본 데이터에 바뀐 값이 바로 적용된다\n",
"CCTV_Seoul.rename(columns = {CCTV_Seoul.columns[0] : '구별'}, inplace = True)\n",
"CCTV_Seoul.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 서울시 인구 통계 현황 데이터 불러오기"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-25T06:29:16.502562Z",
"start_time": "2020-05-25T06:29:16.440727Z"
}
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>자치구</th>\n",
" <th>계</th>\n",
" <th>계.1</th>\n",
" <th>계.2</th>\n",
" <th>65세이상고령자</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>합계</td>\n",
" <td>10197604.0</td>\n",
" <td>9926968.0</td>\n",
" <td>270636.0</td>\n",
" <td>1321458.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>종로구</td>\n",
" <td>162820.0</td>\n",
" <td>153589.0</td>\n",
" <td>9231.0</td>\n",
" <td>25425.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>중구</td>\n",
" <td>133240.0</td>\n",
" <td>124312.0</td>\n",
" <td>8928.0</td>\n",
" <td>20764.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>용산구</td>\n",
" <td>244203.0</td>\n",
" <td>229456.0</td>\n",
" <td>14747.0</td>\n",
" <td>36231.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>성동구</td>\n",
" <td>311244.0</td>\n",
" <td>303380.0</td>\n",
" <td>7864.0</td>\n",
" <td>39997.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 자치구 계 계.1 계.2 65세이상고령자\n",
"0 합계 10197604.0 9926968.0 270636.0 1321458.0\n",
"1 종로구 162820.0 153589.0 9231.0 25425.0\n",
"2 중구 133240.0 124312.0 8928.0 20764.0\n",
"3 용산구 244203.0 229456.0 14747.0 36231.0\n",
"4 성동구 311244.0 303380.0 7864.0 39997.0"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 서울시 인구 통계 현황 데이터 불러오기\n",
"# 엑셀 파일 내에서 세 번째 줄부터 읽어오기 위해 header 옵션 지정\n",
"# B, D, G, J, N 열만 읽어오도록 usecols 옵션을 지정 (판다스 버전 차이로 인해, 더 이상 parse_cols 옵션은 존재 x)\n",
"pop_Seoul = pd.read_excel('../data/01. population_in_Seoul.xls', header = 2, \n",
" usecols = 'B, D, G, J, N', encoding = 'utf-8')\n",
"pop_Seoul.head()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-25T06:29:16.865561Z",
"start_time": "2020-05-25T06:29:16.845617Z"
}
},
"outputs": [
{
"data": {
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>구별</th>\n",
" <th>인구수</th>\n",
" <th>한국인</th>\n",
" <th>외국인</th>\n",
" <th>고령자</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>합계</td>\n",
" <td>10197604.0</td>\n",
" <td>9926968.0</td>\n",
" <td>270636.0</td>\n",
" <td>1321458.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>종로구</td>\n",
" <td>162820.0</td>\n",
" <td>153589.0</td>\n",
" <td>9231.0</td>\n",
" <td>25425.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>중구</td>\n",
" <td>133240.0</td>\n",
" <td>124312.0</td>\n",
" <td>8928.0</td>\n",
" <td>20764.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>용산구</td>\n",
" <td>244203.0</td>\n",
" <td>229456.0</td>\n",
" <td>14747.0</td>\n",
" <td>36231.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>성동구</td>\n",
" <td>311244.0</td>\n",
" <td>303380.0</td>\n",
" <td>7864.0</td>\n",
" <td>39997.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 구별 인구수 한국인 외국인 고령자\n",
"0 합계 10197604.0 9926968.0 270636.0 1321458.0\n",
"1 종로구 162820.0 153589.0 9231.0 25425.0\n",
"2 중구 133240.0 124312.0 8928.0 20764.0\n",
"3 용산구 244203.0 229456.0 14747.0 36231.0\n",
"4 성동구 311244.0 303380.0 7864.0 39997.0"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# rename을 이용해서 컬럼명 변경\n",
"pop_Seoul.rename(columns = {pop_Seoul.columns[0] : '구별',\n",
" pop_Seoul.columns[1] : '인구수',\n",
" pop_Seoul.columns[2] : '한국인',\n",
" pop_Seoul.columns[3] : '외국인',\n",
" pop_Seoul.columns[4] : '고령자'},\n",
" inplace = True)\n",
"pop_Seoul.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 데이터 파악하기\n",
"\n",
"**서울시 CCTV 설치 현황 데이터 파악**"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-25T06:29:17.669293Z",
"start_time": "2020-05-25T06:29:17.657326Z"
}
},
"outputs": [
{
"data": {
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>구별</th>\n",
" <th>소계</th>\n",
" <th>2013년도 이전</th>\n",
" <th>2014년</th>\n",
" <th>2015년</th>\n",
" <th>2016년</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>강남구</td>\n",
" <td>2780</td>\n",
" <td>1292</td>\n",
" <td>430</td>\n",
" <td>584</td>\n",
" <td>932</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>강동구</td>\n",
" <td>773</td>\n",
" <td>379</td>\n",
" <td>99</td>\n",
" <td>155</td>\n",
" <td>377</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>강북구</td>\n",
" <td>748</td>\n",
" <td>369</td>\n",
" <td>120</td>\n",
" <td>138</td>\n",
" <td>204</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>강서구</td>\n",
" <td>884</td>\n",
" <td>388</td>\n",
" <td>258</td>\n",
" <td>184</td>\n",
" <td>81</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>관악구</td>\n",
" <td>1496</td>\n",
" <td>846</td>\n",
" <td>260</td>\n",
" <td>390</td>\n",
" <td>613</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 구별 소계 2013년도 이전 2014년 2015년 2016년\n",
"0 강남구 2780 1292 430 584 932\n",
"1 강동구 773 379 99 155 377\n",
"2 강북구 748 369 120 138 204\n",
"3 강서구 884 388 258 184 81\n",
"4 관악구 1496 846 260 390 613"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"CCTV_Seoul.head()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-25T06:29:18.008218Z",
"start_time": "2020-05-25T06:29:17.992260Z"
}
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>구별</th>\n",
" <th>소계</th>\n",
" <th>2013년도 이전</th>\n",
" <th>2014년</th>\n",
" <th>2015년</th>\n",
" <th>2016년</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>도봉구</td>\n",
" <td>485</td>\n",
" <td>238</td>\n",
" <td>159</td>\n",
" <td>42</td>\n",
" <td>386</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>마포구</td>\n",
" <td>574</td>\n",
" <td>314</td>\n",
" <td>118</td>\n",
" <td>169</td>\n",
" <td>379</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>송파구</td>\n",
" <td>618</td>\n",
" <td>529</td>\n",
" <td>21</td>\n",
" <td>68</td>\n",
" <td>463</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>중랑구</td>\n",
" <td>660</td>\n",
" <td>509</td>\n",
" <td>121</td>\n",
" <td>177</td>\n",
" <td>109</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>중구</td>\n",
" <td>671</td>\n",
" <td>413</td>\n",
" <td>190</td>\n",
" <td>72</td>\n",
" <td>348</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 구별 소계 2013년도 이전 2014년 2015년 2016년\n",
"9 도봉구 485 238 159 42 386\n",
"12 마포구 574 314 118 169 379\n",
"17 송파구 618 529 21 68 463\n",
"24 중랑구 660 509 121 177 109\n",
"23 중구 671 413 190 72 348"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 구별 CCTV 데이터에서 CCTV 전체 개수인 '소계'로 오름차순 정렬(default)\n",
"CCTV_Seoul.sort_values(by = '소계').head(5)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- CCTV의 전체 개수가 가장 작은 상위 5개의 구는 '도봉구', '마포구', '송파구', '중랑구', '중구'이다\n",
"- 강남 3구 중 하나인 '송파구'가 CCTV가 적다니...실화인가?"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-25T06:29:18.630958Z",
"start_time": "2020-05-25T06:29:18.616999Z"
}
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
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" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>구별</th>\n",
" <th>소계</th>\n",
" <th>2013년도 이전</th>\n",
" <th>2014년</th>\n",
" <th>2015년</th>\n",
" <th>2016년</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>강남구</td>\n",
" <td>2780</td>\n",
" <td>1292</td>\n",
" <td>430</td>\n",
" <td>584</td>\n",
" <td>932</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>양천구</td>\n",
" <td>2034</td>\n",
" <td>1843</td>\n",
" <td>142</td>\n",
" <td>30</td>\n",
" <td>467</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>서초구</td>\n",
" <td>1930</td>\n",
" <td>1406</td>\n",
" <td>157</td>\n",
" <td>336</td>\n",
" <td>398</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>은평구</td>\n",
" <td>1873</td>\n",
" <td>1138</td>\n",
" <td>224</td>\n",
" <td>278</td>\n",
" <td>468</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>용산구</td>\n",
" <td>1624</td>\n",
" <td>1368</td>\n",
" <td>218</td>\n",
" <td>112</td>\n",
" <td>398</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 구별 소계 2013년도 이전 2014년 2015년 2016년\n",
"0 강남구 2780 1292 430 584 932\n",
"18 양천구 2034 1843 142 30 467\n",
"14 서초구 1930 1406 157 336 398\n",
"21 은평구 1873 1138 224 278 468\n",
"20 용산구 1624 1368 218 112 398"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 구별 CCTV 데이터에서 CCTV 전체 개수인 '소계'로 내림차순 정렬\n",
"CCTV_Seoul.sort_values(by = '소계', ascending = False).head(5)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- CCTV의 전체 개수가 가장 많은 상위 5개의 구는 '강남구', '양천구', '서초구', '은평구', '용산구'이다"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-25T06:29:19.018176Z",
"start_time": "2020-05-25T06:29:18.990251Z"
}
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
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" }\n",
"\n",
" .dataframe thead th {\n",
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" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>구별</th>\n",
" <th>소계</th>\n",
" <th>2013년도 이전</th>\n",
" <th>2014년</th>\n",
" <th>2015년</th>\n",
" <th>2016년</th>\n",
" <th>최근증가율</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>종로구</td>\n",
" <td>1002</td>\n",
" <td>464</td>\n",
" <td>314</td>\n",
" <td>211</td>\n",
" <td>630</td>\n",
" <td>248.922414</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>도봉구</td>\n",
" <td>485</td>\n",
" <td>238</td>\n",
" <td>159</td>\n",
" <td>42</td>\n",
" <td>386</td>\n",
" <td>246.638655</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>마포구</td>\n",
" <td>574</td>\n",
" <td>314</td>\n",
" <td>118</td>\n",
" <td>169</td>\n",
" <td>379</td>\n",
" <td>212.101911</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>노원구</td>\n",
" <td>1265</td>\n",
" <td>542</td>\n",
" <td>57</td>\n",
" <td>451</td>\n",
" <td>516</td>\n",
" <td>188.929889</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>강동구</td>\n",
" <td>773</td>\n",
" <td>379</td>\n",
" <td>99</td>\n",
" <td>155</td>\n",
" <td>377</td>\n",
" <td>166.490765</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 구별 소계 2013년도 이전 2014년 2015년 2016년 최근증가율\n",
"22 종로구 1002 464 314 211 630 248.922414\n",
"9 도봉구 485 238 159 42 386 246.638655\n",
"12 마포구 574 314 118 169 379 212.101911\n",
"8 노원구 1265 542 57 451 516 188.929889\n",
"1 강동구 773 379 99 155 377 166.490765"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 2014년부터 2016년까지 최근 3년간 CCTV 수를 더하고\n",
"# 2013년 이전 CCTV 수로 나눠서 최근 3년간 CCTV 증가율을 계산\n",
"CCTV_Seoul['최근증가율'] = (CCTV_Seoul['2016년'] + CCTV_Seoul['2015년'] + \\\n",
" CCTV_Seoul['2014년']) / CCTV_Seoul['2013년도 이전'] * 100\n",
"CCTV_Seoul.sort_values(by ='최근증가율', ascending = False).head(5)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- 최근 3년간 CCTV가 이전에 대비해서 많이 증가한 상위 3개의 구는 '종로구', '도봉구', '마포구'이다"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**서울시 인구 통계 현황 데이터 파악**"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-25T06:29:19.443105Z",
"start_time": "2020-05-25T06:29:19.430140Z"
},
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>구별</th>\n",
" <th>인구수</th>\n",
" <th>한국인</th>\n",
" <th>외국인</th>\n",
" <th>고령자</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>합계</td>\n",
" <td>10197604.0</td>\n",
" <td>9926968.0</td>\n",
" <td>270636.0</td>\n",
" <td>1321458.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>종로구</td>\n",
" <td>162820.0</td>\n",
" <td>153589.0</td>\n",
" <td>9231.0</td>\n",
" <td>25425.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>중구</td>\n",
" <td>133240.0</td>\n",
" <td>124312.0</td>\n",
" <td>8928.0</td>\n",
" <td>20764.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>용산구</td>\n",
" <td>244203.0</td>\n",
" <td>229456.0</td>\n",
" <td>14747.0</td>\n",
" <td>36231.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>성동구</td>\n",
" <td>311244.0</td>\n",
" <td>303380.0</td>\n",
" <td>7864.0</td>\n",
" <td>39997.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 구별 인구수 한국인 외국인 고령자\n",
"0 합계 10197604.0 9926968.0 270636.0 1321458.0\n",
"1 종로구 162820.0 153589.0 9231.0 25425.0\n",
"2 중구 133240.0 124312.0 8928.0 20764.0\n",
"3 용산구 244203.0 229456.0 14747.0 36231.0\n",
"4 성동구 311244.0 303380.0 7864.0 39997.0"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pop_Seoul.head()"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-25T06:29:19.561667Z",
"start_time": "2020-05-25T06:29:19.543751Z"
}
},
"outputs": [
{
"data": {
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" <th>외국인</th>\n",
" <th>고령자</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>종로구</td>\n",
" <td>162820.0</td>\n",
" <td>153589.0</td>\n",
" <td>9231.0</td>\n",
" <td>25425.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>중구</td>\n",
" <td>133240.0</td>\n",
" <td>124312.0</td>\n",
" <td>8928.0</td>\n",
" <td>20764.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>용산구</td>\n",
" <td>244203.0</td>\n",
" <td>229456.0</td>\n",
" <td>14747.0</td>\n",
" <td>36231.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>성동구</td>\n",
" <td>311244.0</td>\n",
" <td>303380.0</td>\n",
" <td>7864.0</td>\n",
" <td>39997.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>광진구</td>\n",
" <td>372164.0</td>\n",
" <td>357211.0</td>\n",
" <td>14953.0</td>\n",
" <td>42214.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 구별 인구수 한국인 외국인 고령자\n",
"1 종로구 162820.0 153589.0 9231.0 25425.0\n",
"2 중구 133240.0 124312.0 8928.0 20764.0\n",
"3 용산구 244203.0 229456.0 14747.0 36231.0\n",
"4 성동구 311244.0 303380.0 7864.0 39997.0\n",
"5 광진구 372164.0 357211.0 14953.0 42214.0"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 0번 행에 '합계'가 있으므로 삭제\n",
"pop_Seoul.drop([0], inplace = True)\n",
"pop_Seoul.head()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-25T06:29:19.695014Z",
"start_time": "2020-05-25T06:29:19.688066Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"array(['종로구', '중구', '용산구', '성동구', '광진구', '동대문구', '중랑구', '성북구', '강북구',\n",
" '도봉구', '노원구', '은평구', '서대문구', '마포구', '양천구', '강서구', '구로구', '금천구',\n",
" '영등포구', '동작구', '관악구', '서초구', '강남구', '송파구', '강동구', nan],\n",
" dtype=object)"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# unique() 함수를 사용하여 반복된 데이터는 하나로 나타내기\n",
"pop_Seoul['구별'].unique()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- unique() 함수를 사용한 결과를 보면, NaN 값이 존재함을 알 수 있다\n",
"- 이 NaN 값이 어디에 있는지 확인해볼 필요가 있다"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-25T06:29:19.993804Z",
"start_time": "2020-05-25T06:29:19.982833Z"
}
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
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" vertical-align: top;\n",
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"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>구별</th>\n",
" <th>인구수</th>\n",
" <th>한국인</th>\n",
" <th>외국인</th>\n",
" <th>고령자</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 구별 인구수 한국인 외국인 고령자\n",
"26 NaN NaN NaN NaN NaN"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pop_Seoul[pop_Seoul['구별'].isnull()]"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-25T06:29:20.185550Z",
"start_time": "2020-05-25T06:29:20.166601Z"
}
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
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" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>구별</th>\n",
" <th>인구수</th>\n",
" <th>한국인</th>\n",
" <th>외국인</th>\n",
" <th>고령자</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>종로구</td>\n",
" <td>162820.0</td>\n",
" <td>153589.0</td>\n",
" <td>9231.0</td>\n",
" <td>25425.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>중구</td>\n",
" <td>133240.0</td>\n",
" <td>124312.0</td>\n",
" <td>8928.0</td>\n",
" <td>20764.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>용산구</td>\n",
" <td>244203.0</td>\n",
" <td>229456.0</td>\n",
" <td>14747.0</td>\n",
" <td>36231.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>성동구</td>\n",
" <td>311244.0</td>\n",
" <td>303380.0</td>\n",
" <td>7864.0</td>\n",
" <td>39997.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>광진구</td>\n",
" <td>372164.0</td>\n",
" <td>357211.0</td>\n",
" <td>14953.0</td>\n",
" <td>42214.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 구별 인구수 한국인 외국인 고령자\n",
"1 종로구 162820.0 153589.0 9231.0 25425.0\n",
"2 중구 133240.0 124312.0 8928.0 20764.0\n",
"3 용산구 244203.0 229456.0 14747.0 36231.0\n",
"4 성동구 311244.0 303380.0 7864.0 39997.0\n",
"5 광진구 372164.0 357211.0 14953.0 42214.0"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# NaN이 있는 행을 삭제\n",
"pop_Seoul.drop([26], inplace = True)\n",
"pop_Seoul.head()"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-25T06:29:20.311213Z",
"start_time": "2020-05-25T06:29:20.285282Z"
}
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>구별</th>\n",
" <th>인구수</th>\n",
" <th>한국인</th>\n",
" <th>외국인</th>\n",
" <th>고령자</th>\n",
" <th>외국인비율</th>\n",
" <th>고령자비율</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>종로구</td>\n",
" <td>162820.0</td>\n",
" <td>153589.0</td>\n",
" <td>9231.0</td>\n",
" <td>25425.0</td>\n",
" <td>5.669451</td>\n",
" <td>15.615404</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>중구</td>\n",
" <td>133240.0</td>\n",
" <td>124312.0</td>\n",
" <td>8928.0</td>\n",
" <td>20764.0</td>\n",
" <td>6.700690</td>\n",
" <td>15.583909</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>용산구</td>\n",
" <td>244203.0</td>\n",
" <td>229456.0</td>\n",
" <td>14747.0</td>\n",
" <td>36231.0</td>\n",
" <td>6.038828</td>\n",
" <td>14.836427</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>성동구</td>\n",
" <td>311244.0</td>\n",
" <td>303380.0</td>\n",
" <td>7864.0</td>\n",
" <td>39997.0</td>\n",
" <td>2.526635</td>\n",
" <td>12.850689</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>광진구</td>\n",
" <td>372164.0</td>\n",
" <td>357211.0</td>\n",
" <td>14953.0</td>\n",
" <td>42214.0</td>\n",
" <td>4.017852</td>\n",
" <td>11.342849</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 구별 인구수 한국인 외국인 고령자 외국인비율 고령자비율\n",
"1 종로구 162820.0 153589.0 9231.0 25425.0 5.669451 15.615404\n",
"2 중구 133240.0 124312.0 8928.0 20764.0 6.700690 15.583909\n",
"3 용산구 244203.0 229456.0 14747.0 36231.0 6.038828 14.836427\n",
"4 성동구 311244.0 303380.0 7864.0 39997.0 2.526635 12.850689\n",
"5 광진구 372164.0 357211.0 14953.0 42214.0 4.017852 11.342849"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 각 구별로 '외국인비율'과 '고령자비율'을 계산\n",
"pop_Seoul['외국인비율'] = pop_Seoul['외국인'] / pop_Seoul['인구수'] * 100\n",
"pop_Seoul['고령자비율'] = pop_Seoul['고령자'] / pop_Seoul['인구수'] * 100\n",
"pop_Seoul.head()"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-25T06:29:20.480668Z",
"start_time": "2020-05-25T06:29:20.464710Z"
}
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
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" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>구별</th>\n",
" <th>인구수</th>\n",
" <th>한국인</th>\n",
" <th>외국인</th>\n",
" <th>고령자</th>\n",
" <th>외국인비율</th>\n",
" <th>고령자비율</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>송파구</td>\n",
" <td>667483.0</td>\n",
" <td>660584.0</td>\n",
" <td>6899.0</td>\n",
" <td>72506.0</td>\n",
" <td>1.033584</td>\n",
" <td>10.862599</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>강서구</td>\n",
" <td>603772.0</td>\n",
" <td>597248.0</td>\n",
" <td>6524.0</td>\n",
" <td>72548.0</td>\n",
" <td>1.080540</td>\n",
" <td>12.015794</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>강남구</td>\n",
" <td>570500.0</td>\n",
" <td>565550.0</td>\n",
" <td>4950.0</td>\n",
" <td>63167.0</td>\n",
" <td>0.867660</td>\n",
" <td>11.072217</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>노원구</td>\n",
" <td>569384.0</td>\n",
" <td>565565.0</td>\n",
" <td>3819.0</td>\n",
" <td>71941.0</td>\n",
" <td>0.670725</td>\n",
" <td>12.634883</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>관악구</td>\n",
" <td>525515.0</td>\n",
" <td>507203.0</td>\n",
" <td>18312.0</td>\n",
" <td>68082.0</td>\n",
" <td>3.484582</td>\n",
" <td>12.955291</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 구별 인구수 한국인 외국인 고령자 외국인비율 고령자비율\n",
"24 송파구 667483.0 660584.0 6899.0 72506.0 1.033584 10.862599\n",
"16 강서구 603772.0 597248.0 6524.0 72548.0 1.080540 12.015794\n",
"23 강남구 570500.0 565550.0 4950.0 63167.0 0.867660 11.072217\n",
"11 노원구 569384.0 565565.0 3819.0 71941.0 0.670725 12.634883\n",
"21 관악구 525515.0 507203.0 18312.0 68082.0 3.484582 12.955291"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 데이터를 '인구수'를 기준으로 내림차순 정렬\n",
"pop_Seoul.sort_values(by = '인구수', ascending = False).head(5)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- '송파구', '강서구', '강남구', '노원구', '관악구' 순으로 인구가 많다는 것을 알 수 있다"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-25T06:29:20.776025Z",
"start_time": "2020-05-25T06:29:20.757078Z"
}
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
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" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>구별</th>\n",
" <th>인구수</th>\n",
" <th>한국인</th>\n",
" <th>외국인</th>\n",
" <th>고령자</th>\n",
" <th>외국인비율</th>\n",
" <th>고령자비율</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>영등포구</td>\n",
" <td>402985.0</td>\n",
" <td>368072.0</td>\n",
" <td>34913.0</td>\n",
" <td>52413.0</td>\n",
" <td>8.663598</td>\n",
" <td>13.006191</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>구로구</td>\n",
" <td>447874.0</td>\n",
" <td>416487.0</td>\n",
" <td>31387.0</td>\n",
" <td>56833.0</td>\n",
" <td>7.007998</td>\n",
" <td>12.689506</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>금천구</td>\n",
" <td>255082.0</td>\n",
" <td>236353.0</td>\n",
" <td>18729.0</td>\n",
" <td>32970.0</td>\n",
" <td>7.342345</td>\n",
" <td>12.925255</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>관악구</td>\n",
" <td>525515.0</td>\n",
" <td>507203.0</td>\n",
" <td>18312.0</td>\n",
" <td>68082.0</td>\n",
" <td>3.484582</td>\n",
" <td>12.955291</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>동대문구</td>\n",
" <td>369496.0</td>\n",
" <td>354079.0</td>\n",
" <td>15417.0</td>\n",
" <td>54173.0</td>\n",
" <td>4.172440</td>\n",
" <td>14.661322</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 구별 인구수 한국인 외국인 고령자 외국인비율 고령자비율\n",
"19 영등포구 402985.0 368072.0 34913.0 52413.0 8.663598 13.006191\n",
"17 구로구 447874.0 416487.0 31387.0 56833.0 7.007998 12.689506\n",
"18 금천구 255082.0 236353.0 18729.0 32970.0 7.342345 12.925255\n",
"21 관악구 525515.0 507203.0 18312.0 68082.0 3.484582 12.955291\n",
"6 동대문구 369496.0 354079.0 15417.0 54173.0 4.172440 14.661322"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 데이터를 '외국인' 수를 기준으로 내림차순 정렬\n",
"pop_Seoul.sort_values(by = '외국인', ascending = False).head(5)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- '영등포구'와 '구로구'에 비교적 외국인 수가 많다는 것을 알 수 있다"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-25T06:29:21.032758Z",
"start_time": "2020-05-25T06:29:21.015803Z"
}
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>구별</th>\n",
" <th>인구수</th>\n",
" <th>한국인</th>\n",
" <th>외국인</th>\n",
" <th>고령자</th>\n",
" <th>외국인비율</th>\n",
" <th>고령자비율</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>영등포구</td>\n",
" <td>402985.0</td>\n",
" <td>368072.0</td>\n",
" <td>34913.0</td>\n",
" <td>52413.0</td>\n",
" <td>8.663598</td>\n",
" <td>13.006191</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>금천구</td>\n",
" <td>255082.0</td>\n",
" <td>236353.0</td>\n",
" <td>18729.0</td>\n",
" <td>32970.0</td>\n",
" <td>7.342345</td>\n",
" <td>12.925255</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>구로구</td>\n",
" <td>447874.0</td>\n",
" <td>416487.0</td>\n",
" <td>31387.0</td>\n",
" <td>56833.0</td>\n",
" <td>7.007998</td>\n",
" <td>12.689506</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>중구</td>\n",
" <td>133240.0</td>\n",
" <td>124312.0</td>\n",
" <td>8928.0</td>\n",
" <td>20764.0</td>\n",
" <td>6.700690</td>\n",
" <td>15.583909</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>용산구</td>\n",
" <td>244203.0</td>\n",
" <td>229456.0</td>\n",
" <td>14747.0</td>\n",
" <td>36231.0</td>\n",
" <td>6.038828</td>\n",
" <td>14.836427</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 구별 인구수 한국인 외국인 고령자 외국인비율 고령자비율\n",
"19 영등포구 402985.0 368072.0 34913.0 52413.0 8.663598 13.006191\n",
"18 금천구 255082.0 236353.0 18729.0 32970.0 7.342345 12.925255\n",
"17 구로구 447874.0 416487.0 31387.0 56833.0 7.007998 12.689506\n",
"2 중구 133240.0 124312.0 8928.0 20764.0 6.700690 15.583909\n",
"3 용산구 244203.0 229456.0 14747.0 36231.0 6.038828 14.836427"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 데이터를 '외국인비율'을 기준으로 내림차순 정렬\n",
"pop_Seoul.sort_values(by = '외국인비율', ascending = False).head(5)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- '영등포구', '금천구', '구로구'의 외국인 비율이 높다는 것을 알 수 있다\n",
"- 위에서 '외국인' 수를 기준으로 내림차순 정렬한 결과와 비교했을 때, 약간 다른 결과가 나왔다"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-25T06:29:21.370807Z",
"start_time": "2020-05-25T06:29:21.350863Z"
}
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>구별</th>\n",
" <th>인구수</th>\n",
" <th>한국인</th>\n",
" <th>외국인</th>\n",
" <th>고령자</th>\n",
" <th>외국인비율</th>\n",
" <th>고령자비율</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>강서구</td>\n",
" <td>603772.0</td>\n",
" <td>597248.0</td>\n",
" <td>6524.0</td>\n",
" <td>72548.0</td>\n",
" <td>1.080540</td>\n",
" <td>12.015794</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>송파구</td>\n",
" <td>667483.0</td>\n",
" <td>660584.0</td>\n",
" <td>6899.0</td>\n",
" <td>72506.0</td>\n",
" <td>1.033584</td>\n",
" <td>10.862599</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>은평구</td>\n",
" <td>494388.0</td>\n",
" <td>489943.0</td>\n",
" <td>4445.0</td>\n",
" <td>72334.0</td>\n",
" <td>0.899091</td>\n",
" <td>14.631019</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>노원구</td>\n",
" <td>569384.0</td>\n",
" <td>565565.0</td>\n",
" <td>3819.0</td>\n",
" <td>71941.0</td>\n",
" <td>0.670725</td>\n",
" <td>12.634883</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>관악구</td>\n",
" <td>525515.0</td>\n",
" <td>507203.0</td>\n",
" <td>18312.0</td>\n",
" <td>68082.0</td>\n",
" <td>3.484582</td>\n",
" <td>12.955291</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 구별 인구수 한국인 외국인 고령자 외국인비율 고령자비율\n",
"16 강서구 603772.0 597248.0 6524.0 72548.0 1.080540 12.015794\n",
"24 송파구 667483.0 660584.0 6899.0 72506.0 1.033584 10.862599\n",
"12 은평구 494388.0 489943.0 4445.0 72334.0 0.899091 14.631019\n",
"11 노원구 569384.0 565565.0 3819.0 71941.0 0.670725 12.634883\n",
"21 관악구 525515.0 507203.0 18312.0 68082.0 3.484582 12.955291"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 데이터를 '고령자' 수를 기준으로 내림차순 정렬\n",
"pop_Seoul.sort_values(by = '고령자', ascending = False).head(5)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- '강서구', '송파구', '은평구', '노원구'에 고령자가 많다는 것을 알 수 있다"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-25T06:29:21.623549Z",
"start_time": "2020-05-25T06:29:21.602606Z"
}
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>구별</th>\n",
" <th>인구수</th>\n",
" <th>한국인</th>\n",
" <th>외국인</th>\n",
" <th>고령자</th>\n",
" <th>외국인비율</th>\n",
" <th>고령자비율</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>강북구</td>\n",
" <td>330192.0</td>\n",
" <td>326686.0</td>\n",
" <td>3506.0</td>\n",
" <td>54813.0</td>\n",
" <td>1.061806</td>\n",
" <td>16.600342</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>종로구</td>\n",
" <td>162820.0</td>\n",
" <td>153589.0</td>\n",
" <td>9231.0</td>\n",
" <td>25425.0</td>\n",
" <td>5.669451</td>\n",
" <td>15.615404</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>중구</td>\n",
" <td>133240.0</td>\n",
" <td>124312.0</td>\n",
" <td>8928.0</td>\n",
" <td>20764.0</td>\n",
" <td>6.700690</td>\n",
" <td>15.583909</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>용산구</td>\n",
" <td>244203.0</td>\n",
" <td>229456.0</td>\n",
" <td>14747.0</td>\n",
" <td>36231.0</td>\n",
" <td>6.038828</td>\n",
" <td>14.836427</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>서대문구</td>\n",
" <td>327163.0</td>\n",
" <td>314982.0</td>\n",
" <td>12181.0</td>\n",
" <td>48161.0</td>\n",
" <td>3.723221</td>\n",
" <td>14.720797</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 구별 인구수 한국인 외국인 고령자 외국인비율 고령자비율\n",
"9 강북구 330192.0 326686.0 3506.0 54813.0 1.061806 16.600342\n",
"1 종로구 162820.0 153589.0 9231.0 25425.0 5.669451 15.615404\n",
"2 중구 133240.0 124312.0 8928.0 20764.0 6.700690 15.583909\n",
"3 용산구 244203.0 229456.0 14747.0 36231.0 6.038828 14.836427\n",
"13 서대문구 327163.0 314982.0 12181.0 48161.0 3.723221 14.720797"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 데이터를 '고령자비율'을 기준으로 내림차순 정렬\n",
"pop_Seoul.sort_values(by = '고령자비율', ascending = False).head(5)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- '강북구', '종로구', '중구'의 고령자 비율이 높다는 것을 알 수 있다\n",
"- 위에서 '고령자' 수를 기준으로 내림차순 정렬한 결과와 비교했을 때, 약간 다른 결과가 나왔다"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## CCTV 현황 데이터와 인구 현황 데이터 병합하기"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-25T06:29:22.774400Z",
"start_time": "2020-05-25T06:29:22.744485Z"
}
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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"\n",
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" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>구별</th>\n",
" <th>소계</th>\n",
" <th>2013년도 이전</th>\n",
" <th>2014년</th>\n",
" <th>2015년</th>\n",
" <th>2016년</th>\n",
" <th>최근증가율</th>\n",
" <th>인구수</th>\n",
" <th>한국인</th>\n",
" <th>외국인</th>\n",
" <th>고령자</th>\n",
" <th>외국인비율</th>\n",
" <th>고령자비율</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>강남구</td>\n",
" <td>2780</td>\n",
" <td>1292</td>\n",
" <td>430</td>\n",
" <td>584</td>\n",
" <td>932</td>\n",
" <td>150.619195</td>\n",
" <td>570500.0</td>\n",
" <td>565550.0</td>\n",
" <td>4950.0</td>\n",
" <td>63167.0</td>\n",
" <td>0.867660</td>\n",
" <td>11.072217</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>강동구</td>\n",
" <td>773</td>\n",
" <td>379</td>\n",
" <td>99</td>\n",
" <td>155</td>\n",
" <td>377</td>\n",
" <td>166.490765</td>\n",
" <td>453233.0</td>\n",
" <td>449019.0</td>\n",
" <td>4214.0</td>\n",
" <td>54622.0</td>\n",
" <td>0.929765</td>\n",
" <td>12.051638</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>강북구</td>\n",
" <td>748</td>\n",
" <td>369</td>\n",
" <td>120</td>\n",
" <td>138</td>\n",
" <td>204</td>\n",
" <td>125.203252</td>\n",
" <td>330192.0</td>\n",
" <td>326686.0</td>\n",
" <td>3506.0</td>\n",
" <td>54813.0</td>\n",
" <td>1.061806</td>\n",
" <td>16.600342</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>강서구</td>\n",
" <td>884</td>\n",
" <td>388</td>\n",
" <td>258</td>\n",
" <td>184</td>\n",
" <td>81</td>\n",
" <td>134.793814</td>\n",
" <td>603772.0</td>\n",
" <td>597248.0</td>\n",
" <td>6524.0</td>\n",
" <td>72548.0</td>\n",
" <td>1.080540</td>\n",
" <td>12.015794</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>관악구</td>\n",
" <td>1496</td>\n",
" <td>846</td>\n",
" <td>260</td>\n",
" <td>390</td>\n",
" <td>613</td>\n",
" <td>149.290780</td>\n",
" <td>525515.0</td>\n",
" <td>507203.0</td>\n",
" <td>18312.0</td>\n",
" <td>68082.0</td>\n",
" <td>3.484582</td>\n",
" <td>12.955291</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 구별 소계 2013년도 이전 2014년 2015년 2016년 최근증가율 인구수 한국인 \\\n",
"0 강남구 2780 1292 430 584 932 150.619195 570500.0 565550.0 \n",
"1 강동구 773 379 99 155 377 166.490765 453233.0 449019.0 \n",
"2 강북구 748 369 120 138 204 125.203252 330192.0 326686.0 \n",
"3 강서구 884 388 258 184 81 134.793814 603772.0 597248.0 \n",
"4 관악구 1496 846 260 390 613 149.290780 525515.0 507203.0 \n",
"\n",
" 외국인 고령자 외국인비율 고령자비율 \n",
"0 4950.0 63167.0 0.867660 11.072217 \n",
"1 4214.0 54622.0 0.929765 12.051638 \n",
"2 3506.0 54813.0 1.061806 16.600342 \n",
"3 6524.0 72548.0 1.080540 12.015794 \n",
"4 18312.0 68082.0 3.484582 12.955291 "
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 두 데이터를 공통 컬럼인 '구별'을 기준으로 병합(merge)\n",
"data_result = pd.merge(CCTV_Seoul, pop_Seoul, on = '구별')\n",
"data_result.head()"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-25T06:29:22.952884Z",
"start_time": "2020-05-25T06:29:22.929932Z"
}
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
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" }\n",
"\n",
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" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>구별</th>\n",
" <th>소계</th>\n",
" <th>최근증가율</th>\n",
" <th>인구수</th>\n",
" <th>한국인</th>\n",
" <th>외국인</th>\n",
" <th>고령자</th>\n",
" <th>외국인비율</th>\n",
" <th>고령자비율</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>강남구</td>\n",
" <td>2780</td>\n",
" <td>150.619195</td>\n",
" <td>570500.0</td>\n",
" <td>565550.0</td>\n",
" <td>4950.0</td>\n",
" <td>63167.0</td>\n",
" <td>0.867660</td>\n",
" <td>11.072217</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>강동구</td>\n",
" <td>773</td>\n",
" <td>166.490765</td>\n",
" <td>453233.0</td>\n",
" <td>449019.0</td>\n",
" <td>4214.0</td>\n",
" <td>54622.0</td>\n",
" <td>0.929765</td>\n",
" <td>12.051638</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>강북구</td>\n",
" <td>748</td>\n",
" <td>125.203252</td>\n",
" <td>330192.0</td>\n",
" <td>326686.0</td>\n",
" <td>3506.0</td>\n",
" <td>54813.0</td>\n",
" <td>1.061806</td>\n",
" <td>16.600342</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>강서구</td>\n",
" <td>884</td>\n",
" <td>134.793814</td>\n",
" <td>603772.0</td>\n",
" <td>597248.0</td>\n",
" <td>6524.0</td>\n",
" <td>72548.0</td>\n",
" <td>1.080540</td>\n",
" <td>12.015794</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>관악구</td>\n",
" <td>1496</td>\n",
" <td>149.290780</td>\n",
" <td>525515.0</td>\n",
" <td>507203.0</td>\n",
" <td>18312.0</td>\n",
" <td>68082.0</td>\n",
" <td>3.484582</td>\n",
" <td>12.955291</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 구별 소계 최근증가율 인구수 한국인 외국인 고령자 외국인비율 \\\n",
"0 강남구 2780 150.619195 570500.0 565550.0 4950.0 63167.0 0.867660 \n",
"1 강동구 773 166.490765 453233.0 449019.0 4214.0 54622.0 0.929765 \n",
"2 강북구 748 125.203252 330192.0 326686.0 3506.0 54813.0 1.061806 \n",
"3 강서구 884 134.793814 603772.0 597248.0 6524.0 72548.0 1.080540 \n",
"4 관악구 1496 149.290780 525515.0 507203.0 18312.0 68082.0 3.484582 \n",
"\n",
" 고령자비율 \n",
"0 11.072217 \n",
"1 12.051638 \n",
"2 16.600342 \n",
"3 12.015794 \n",
"4 12.955291 "
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 의미없는 칼럼 삭제 --> '열 방향'으로 삭제이므로, del을 사용!\n",
"# '행 방향'으로 삭제: drop 사용\n",
"# '열 방향'으로 삭제: del 사용\n",
"del data_result['2013년도 이전']\n",
"del data_result['2014년']\n",
"del data_result['2015년']\n",
"del data_result['2016년']\n",
"data_result.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**데이터 프레임 인덱스 설정 및 제거**\n",
"- set_index: 기존의 행 인덱스를 제거하고, 데이터 열 중 하나를 인덱스로 설정\n",
"- reset_index: 기존의 행 인덱스를 제거하고, 인덱스를 데이터 열로 추가"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-25T06:29:23.350113Z",
"start_time": "2020-05-25T06:29:23.333193Z"
},
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>소계</th>\n",
" <th>최근증가율</th>\n",
" <th>인구수</th>\n",
" <th>한국인</th>\n",
" <th>외국인</th>\n",
" <th>고령자</th>\n",
" <th>외국인비율</th>\n",
" <th>고령자비율</th>\n",
" </tr>\n",
" <tr>\n",
" <th>구별</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>강남구</th>\n",
" <td>2780</td>\n",
" <td>150.619195</td>\n",
" <td>570500.0</td>\n",
" <td>565550.0</td>\n",
" <td>4950.0</td>\n",
" <td>63167.0</td>\n",
" <td>0.867660</td>\n",
" <td>11.072217</td>\n",
" </tr>\n",
" <tr>\n",
" <th>강동구</th>\n",
" <td>773</td>\n",
" <td>166.490765</td>\n",
" <td>453233.0</td>\n",
" <td>449019.0</td>\n",
" <td>4214.0</td>\n",
" <td>54622.0</td>\n",
" <td>0.929765</td>\n",
" <td>12.051638</td>\n",
" </tr>\n",
" <tr>\n",
" <th>강북구</th>\n",
" <td>748</td>\n",
" <td>125.203252</td>\n",
" <td>330192.0</td>\n",
" <td>326686.0</td>\n",
" <td>3506.0</td>\n",
" <td>54813.0</td>\n",
" <td>1.061806</td>\n",
" <td>16.600342</td>\n",
" </tr>\n",
" <tr>\n",
" <th>강서구</th>\n",
" <td>884</td>\n",
" <td>134.793814</td>\n",
" <td>603772.0</td>\n",
" <td>597248.0</td>\n",
" <td>6524.0</td>\n",
" <td>72548.0</td>\n",
" <td>1.080540</td>\n",
" <td>12.015794</td>\n",
" </tr>\n",
" <tr>\n",
" <th>관악구</th>\n",
" <td>1496</td>\n",
" <td>149.290780</td>\n",
" <td>525515.0</td>\n",
" <td>507203.0</td>\n",
" <td>18312.0</td>\n",
" <td>68082.0</td>\n",
" <td>3.484582</td>\n",
" <td>12.955291</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 소계 최근증가율 인구수 한국인 외국인 고령자 외국인비율 \\\n",
"구별 \n",
"강남구 2780 150.619195 570500.0 565550.0 4950.0 63167.0 0.867660 \n",
"강동구 773 166.490765 453233.0 449019.0 4214.0 54622.0 0.929765 \n",
"강북구 748 125.203252 330192.0 326686.0 3506.0 54813.0 1.061806 \n",
"강서구 884 134.793814 603772.0 597248.0 6524.0 72548.0 1.080540 \n",
"관악구 1496 149.290780 525515.0 507203.0 18312.0 68082.0 3.484582 \n",
"\n",
" 고령자비율 \n",
"구별 \n",
"강남구 11.072217 \n",
"강동구 12.051638 \n",
"강북구 16.600342 \n",
"강서구 12.015794 \n",
"관악구 12.955291 "
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 뒷 부분에서 그래프로 시각화할 것을 고려해서, '구 이름'을 index로 설정\n",
"data_result.set_index('구별', inplace = True)\n",
"data_result.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**두 데이터의 상관계수 구하기**\n",
"- numpy의 corrcoef 명령을 사용"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-25T06:29:23.891440Z",
"start_time": "2020-05-25T06:29:23.886452Z"
}
},
"outputs": [],
"source": [
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-25T06:29:24.198884Z",
"start_time": "2020-05-25T06:29:24.185920Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 1. , -0.28078554],\n",
" [-0.28078554, 1. ]])"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# '고령자비율'과 '소계'와의 상관계수: -0.28078554 (약한 음의 상관관계)\n",
"np.corrcoef(data_result['고령자비율'], data_result['소계'])"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-25T06:29:24.375531Z",
"start_time": "2020-05-25T06:29:24.367589Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 1. , -0.13607433],\n",
" [-0.13607433, 1. ]])"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# '외국인비율'과 '소계'와의 상관계수: -0.13607433 (약한 음의 상관관계)\n",
"np.corrcoef(data_result['외국인비율'], data_result['소계'])"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-25T06:29:24.527597Z",
"start_time": "2020-05-25T06:29:24.520608Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"array([[1. , 0.30634228],\n",
" [0.30634228, 1. ]])"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# '인구수'과 '소계'와의 상관계수: 0.30634228 (약한 양의 상관관계)\n",
"np.corrcoef(data_result['인구수'], data_result['소계'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**이제 CCTV가 많이 설치된 구와 인구수가 많은 구를 시각화해서 비교해보자!!**\n",
"- matplotlib 을 사용"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-25T06:29:25.205110Z",
"start_time": "2020-05-25T06:29:25.183171Z"
},
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
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" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>소계</th>\n",
" <th>최근증가율</th>\n",
" <th>인구수</th>\n",
" <th>한국인</th>\n",
" <th>외국인</th>\n",
" <th>고령자</th>\n",
" <th>외국인비율</th>\n",
" <th>고령자비율</th>\n",
" </tr>\n",
" <tr>\n",
" <th>구별</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>강남구</th>\n",
" <td>2780</td>\n",
" <td>150.619195</td>\n",
" <td>570500.0</td>\n",
" <td>565550.0</td>\n",
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" <td>63167.0</td>\n",
" <td>0.867660</td>\n",
" <td>11.072217</td>\n",
" </tr>\n",
" <tr>\n",
" <th>양천구</th>\n",
" <td>2034</td>\n",
" <td>34.671731</td>\n",
" <td>479978.0</td>\n",
" <td>475949.0</td>\n",
" <td>4029.0</td>\n",
" <td>52975.0</td>\n",
" <td>0.839413</td>\n",
" <td>11.036964</td>\n",
" </tr>\n",
" <tr>\n",
" <th>서초구</th>\n",
" <td>1930</td>\n",
" <td>63.371266</td>\n",
" <td>450310.0</td>\n",
" <td>445994.0</td>\n",
" <td>4316.0</td>\n",
" <td>51733.0</td>\n",
" <td>0.958451</td>\n",
" <td>11.488308</td>\n",
" </tr>\n",
" <tr>\n",
" <th>은평구</th>\n",
" <td>1873</td>\n",
" <td>85.237258</td>\n",
" <td>494388.0</td>\n",
" <td>489943.0</td>\n",
" <td>4445.0</td>\n",
" <td>72334.0</td>\n",
" <td>0.899091</td>\n",
" <td>14.631019</td>\n",
" </tr>\n",
" <tr>\n",
" <th>용산구</th>\n",
" <td>1624</td>\n",
" <td>53.216374</td>\n",
" <td>244203.0</td>\n",
" <td>229456.0</td>\n",
" <td>14747.0</td>\n",
" <td>36231.0</td>\n",
" <td>6.038828</td>\n",
" <td>14.836427</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 소계 최근증가율 인구수 한국인 외국인 고령자 외국인비율 \\\n",
"구별 \n",
"강남구 2780 150.619195 570500.0 565550.0 4950.0 63167.0 0.867660 \n",
"양천구 2034 34.671731 479978.0 475949.0 4029.0 52975.0 0.839413 \n",
"서초구 1930 63.371266 450310.0 445994.0 4316.0 51733.0 0.958451 \n",
"은평구 1873 85.237258 494388.0 489943.0 4445.0 72334.0 0.899091 \n",
"용산구 1624 53.216374 244203.0 229456.0 14747.0 36231.0 6.038828 \n",
"\n",
" 고령자비율 \n",
"구별 \n",
"강남구 11.072217 \n",
"양천구 11.036964 \n",
"서초구 11.488308 \n",
"은평구 14.631019 \n",
"용산구 14.836427 "
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# CCTV가 많이 설치된 상위 5개의 구 \n",
"data_result.sort_values(by = '소계', ascending = False).head(5)"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-25T06:29:27.824231Z",
"start_time": "2020-05-25T06:29:27.802288Z"
}
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>소계</th>\n",
" <th>최근증가율</th>\n",
" <th>인구수</th>\n",
" <th>한국인</th>\n",
" <th>외국인</th>\n",
" <th>고령자</th>\n",
" <th>외국인비율</th>\n",
" <th>고령자비율</th>\n",
" </tr>\n",
" <tr>\n",
" <th>구별</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>송파구</th>\n",
" <td>618</td>\n",
" <td>104.347826</td>\n",
" <td>667483.0</td>\n",
" <td>660584.0</td>\n",
" <td>6899.0</td>\n",
" <td>72506.0</td>\n",
" <td>1.033584</td>\n",
" <td>10.862599</td>\n",
" </tr>\n",
" <tr>\n",
" <th>강서구</th>\n",
" <td>884</td>\n",
" <td>134.793814</td>\n",
" <td>603772.0</td>\n",
" <td>597248.0</td>\n",
" <td>6524.0</td>\n",
" <td>72548.0</td>\n",
" <td>1.080540</td>\n",
" <td>12.015794</td>\n",
" </tr>\n",
" <tr>\n",
" <th>강남구</th>\n",
" <td>2780</td>\n",
" <td>150.619195</td>\n",
" <td>570500.0</td>\n",
" <td>565550.0</td>\n",
" <td>4950.0</td>\n",
" <td>63167.0</td>\n",
" <td>0.867660</td>\n",
" <td>11.072217</td>\n",
" </tr>\n",
" <tr>\n",
" <th>노원구</th>\n",
" <td>1265</td>\n",
" <td>188.929889</td>\n",
" <td>569384.0</td>\n",
" <td>565565.0</td>\n",
" <td>3819.0</td>\n",
" <td>71941.0</td>\n",
" <td>0.670725</td>\n",
" <td>12.634883</td>\n",
" </tr>\n",
" <tr>\n",
" <th>관악구</th>\n",
" <td>1496</td>\n",
" <td>149.290780</td>\n",
" <td>525515.0</td>\n",
" <td>507203.0</td>\n",
" <td>18312.0</td>\n",
" <td>68082.0</td>\n",
" <td>3.484582</td>\n",
" <td>12.955291</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 소계 최근증가율 인구수 한국인 외국인 고령자 외국인비율 \\\n",
"구별 \n",
"송파구 618 104.347826 667483.0 660584.0 6899.0 72506.0 1.033584 \n",
"강서구 884 134.793814 603772.0 597248.0 6524.0 72548.0 1.080540 \n",
"강남구 2780 150.619195 570500.0 565550.0 4950.0 63167.0 0.867660 \n",
"노원구 1265 188.929889 569384.0 565565.0 3819.0 71941.0 0.670725 \n",
"관악구 1496 149.290780 525515.0 507203.0 18312.0 68082.0 3.484582 \n",
"\n",
" 고령자비율 \n",
"구별 \n",
"송파구 10.862599 \n",
"강서구 12.015794 \n",
"강남구 11.072217 \n",
"노원구 12.634883 \n",
"관악구 12.955291 "
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 인구 수가 많은 상위 5개의 구\n",
"data_result.sort_values(by = '인구수', ascending = False).head(5)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## [참고] Matplotlib 기초\n",
"- 파이썬의 대표 시각화 도구이니 Matplotlib에 대해 간략하게 학습해보자!\n",
"- 보통 그래프를 그리는 모듈은 ```import matplotlib.pyplot as plt``` 의 형태로 사용\n",
"- ```%matplotlib inline``` 이라는 명령은 그래프의 결과를 출력 세션에 나타나게 하는 설정"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-25T06:29:31.327293Z",
"start_time": "2020-05-25T06:29:31.050166Z"
}
},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**plot 그리기: plot()**"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-25T06:29:33.812147Z",
"start_time": "2020-05-25T06:29:33.642569Z"
}
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.figure\n",
"plt.plot([1, 2, 3, 4, 5, 6, 7, 8, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0])\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-25T06:29:36.681055Z",
"start_time": "2020-05-25T06:29:36.676073Z"
}
},
"outputs": [],
"source": [
"# numpy를 이용해서 sin을 만들고 그려보기\n",
"import numpy as np\n",
"\n",
"# 0부터 12까지 0.01 간격으로 데이터를 만들기\n",
"t = np.arange(0, 12, 0.01)\n",
"\n",
"# np.sin()로 sin 값 계산\n",
"y = np.sin(t)"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-25T06:29:40.285462Z",
"start_time": "2020-05-25T06:29:40.109933Z"
}
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize = (10, 6))\n",
"# x축 : t, y축 : y\n",
"plt.plot(t, y)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-25T06:30:25.841865Z",
"start_time": "2020-05-25T06:30:25.625411Z"
}
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize = (10, 6))\n",
"plt.plot(t, y)\n",
"\n",
"# 그리드(그래프의 뒷 배경에 격자 무늬) 적용\n",
"plt.grid()\n",
"\n",
"# x축과 y축의 label 설정\n",
"plt.xlabel('time')\n",
"plt.ylabel('Amplitude')\n",
"\n",
"# 그래프의 title 설정\n",
"plt.title('Example of sinewave')\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-25T06:30:28.925455Z",
"start_time": "2020-05-25T06:30:28.730013Z"
}
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize = (10, 6))\n",
"\n",
"# plot 명령을 두 개 넣어서, 한 화면에 그래프를 두 개 만들기\n",
"# 1. sin 그래프\n",
"plt.plot(t, np.sin(t), lw = 3, label = 'sin') # lw 옵션으로 선의 굵기를 지정하고, 범례(legend)를 추가해주기 위해 label을 설정\n",
"# 2. cosin 그래프\n",
"plt.plot(t, np.cos(t), color = 'r', label = 'cos') # color 옵션으로 색상을 지정하고, 범례(legend)를 추가해주기 위해 label을 설정\n",
"\n",
"# 그리드(그래프의 뒷 배경에 격자 무늬) 적용\n",
"plt.grid()\n",
"\n",
"# 범례(legend) 추가\n",
"plt.legend()\n",
"\n",
"# x축과 y축의 label 설정\n",
"plt.xlabel('time')\n",
"plt.ylabel('Amplitude')\n",
"\n",
"# 그래프의 title 설정\n",
"plt.title('Example of sinewave')\n",
"\n",
"# x축과 y축의 범위를 설정\n",
"plt.ylim(-1.2, 1.2)\n",
"plt.xlim(0, np.pi)\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-25T06:30:40.373781Z",
"start_time": "2020-05-25T06:30:40.217198Z"
}
},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 720x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"t = np.arange(0, 5, 0.5)\n",
"\n",
"plt.figure(figsize = (10, 6))\n",
"plt.plot(t, t, 'r--')\n",
"plt.plot(t, t ** 2, 'bs')\n",
"plt.plot(t, t ** 3, 'g^')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-25T06:30:42.724846Z",
"start_time": "2020-05-25T06:30:42.313908Z"
}
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"t = np.arange(0, 5, 0.5)\n",
"\n",
"# 그래프 1\n",
"plt.figure(figsize = (10, 6))\n",
"pl1 = plt.plot(t, t ** 2, 'bs')\n",
"\n",
"# 그래프 2\n",
"plt.figure(figsize = (10, 6))\n",
"pl2 = plt.plot(t, t ** 3, 'g^')\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-25T06:31:07.902354Z",
"start_time": "2020-05-25T06:31:07.697901Z"
}
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"t = [0, 1, 2, 3, 4, 5, 6] \n",
"y = [1, 4, 5, 8, 9, 5, 3]\n",
"\n",
"plt.figure(figsize = (10, 6))\n",
"\n",
"# 선 색상을 '초록색'으로 지정 + 선 스타일을 '점선'으로 지정 + marker를 '동그라미'로 지정\n",
"# marker 색상을 '파란색'으로 지정 + marker 사이즈를 '12'로 지정\n",
"plt.plot(t, y, color = 'green', linestyle = 'dashed', marker = 'o',\n",
" markerfacecolor = 'blue', markersize = 12)\n",
"\n",
"# x축과 y축의 범위를 설정\n",
"plt.xlim([-0.5, 6.5])\n",
"plt.ylim([0.5, 9.5])\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**scatter plot 그리기: scatter()**"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-25T06:31:51.320751Z",
"start_time": "2020-05-25T06:31:51.067428Z"
}
},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 720x432 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"t = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])\n",
"y = np.array([9, 8, 7, 9, 8, 3, 2, 4, 3, 4])\n",
"\n",
"# x축 값인 t에 따라 색상을 바꾸는 color map을 지정\n",
"colormap = t\n",
"\n",
"plt.figure(figsize = (10, 6))\n",
"plt.scatter(t, y, s = 50, c = colormap, marker = '>') # s 옵션은 'marker의 사이즈'를 의미\n",
"\n",
"# 그래프 옆에 color bar를 출력해주기 위한 코드\n",
"plt.colorbar()\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**boxplot 그리기: boxplot()**"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-25T06:31:56.591787Z",
"start_time": "2020-05-25T06:31:56.583813Z"
}
},
"outputs": [],
"source": [
"# numpy의 랜덤변수 함수를 이용해서 데이터 3개 만들기\n",
"s1 = np.random.normal(loc = 0, scale = 1, size = 1000)\n",
"s2 = np.random.normal(loc = 5, scale = 0.5, size = 1000)\n",
"s3 = np.random.normal(loc = 10, scale = 2, size = 1000)"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-25T06:31:59.292321Z",
"start_time": "2020-05-25T06:31:59.041992Z"
}
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize = (10, 6))\n",
"\n",
"plt.plot(s1, label = 's1') # 범례(legend)를 그래프에 표시해주기 위해, label 옵션 지정\n",
"plt.plot(s2, label = 's2') # 범례(legend)를 그래프에 표시해주기 위해, label 옵션 지정\n",
"plt.plot(s3, label = 's3') # 범례(legend)를 그래프에 표시해주기 위해, label 옵션 지정\n",
"\n",
"# 범례(legend) 추가\n",
"plt.legend()\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-25T06:32:01.559866Z",
"start_time": "2020-05-25T06:32:01.385576Z"
}
},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 720x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize = (10, 6))\n",
"\n",
"# boxplot 그리기\n",
"plt.boxplot((s1, s2, s3))\n",
"\n",
"# 그리드(그래프 뒷 배경에 격자 무늬) 설정\n",
"plt.grid()\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**화면에 출력할 그래프 개수 설정: subplot()**\n",
"- 일반적으로 일일히 입력해줘야 하는 subplot()보다 subplots()를 더 많이 사용한다\n",
"- subplot()과 subplots() 관련 참고 사이트: https://m.blog.naver.com/PostView.nhn?blogId=heygun&logNo=221520454294&proxyReferer=https:%2F%2Fwww.google.com%2F"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-25T06:32:07.076134Z",
"start_time": "2020-05-25T06:32:06.729030Z"
}
},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 720x432 with 3 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize = (10, 6))\n",
"\n",
"plt.subplot(221) # plt.subplot(2, 2, 1) 과 동일!\n",
"plt.subplot(222) # plt.subplot(2, 2, 2) 과 동일!\n",
"plt.subplot(212) # plt.subplot(2, 1, 2) 과 동일!\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-25T06:32:09.708225Z",
"start_time": "2020-05-25T06:32:09.223523Z"
}
},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 720x432 with 5 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize = (10, 6))\n",
"\n",
"plt.subplot(411) # plt.subplot(4, 1, 1) 과 동일!\n",
"plt.subplot(423)\n",
"plt.subplot(424)\n",
"plt.subplot(413)\n",
"plt.subplot(414)\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-25T06:32:11.849919Z",
"start_time": "2020-05-25T06:32:11.150791Z"
}
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x864 with 5 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"t = np.arange(0, 5, 0.01)\n",
"\n",
"plt.figure(figsize = (10, 12))\n",
"\n",
"plt.subplot(411) # plt.subplot(4, 1, 1) 과 동일!\n",
"plt.plot(t, np.sqrt(t))\n",
"plt.grid()\n",
"\n",
"plt.subplot(423)\n",
"plt.plot(t, t ** 2)\n",
"plt.grid()\n",
"\n",
"plt.subplot(424)\n",
"plt.plot(t, t ** 3)\n",
"plt.grid()\n",
"\n",
"plt.subplot(413)\n",
"plt.plot(t, np.sin(t))\n",
"plt.grid()\n",
"\n",
"plt.subplot(414)\n",
"plt.plot(t, np.cos(t))\n",
"plt.grid()\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**여기까지 해서 matplotlib에 대한 간략한 설명은 마치겠다**"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## CCTV 현황 그래프로 분석하기"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**matplotlib이 기본으로 가진 폰트는 한굴을 지원하지 않음**\n",
"- 따라서 matplotlib의 폰트를 변경해주어야 함"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-25T06:32:15.428525Z",
"start_time": "2020-05-25T06:32:15.420550Z"
}
},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"import platform\n",
"\n",
"from matplotlib import font_manager, rc\n",
"plt.rcParams['axes.unicode_minus'] = False\n",
"\n",
"if platform.system() == 'Darwin':\n",
" rc('font', family = 'AppleGothic')\n",
"elif platform.system() == 'Windows':\n",
" path = \"c:/Windows/Fonts/malgun.ttf\"\n",
" font_name = font_manager.FontProperties(fname = path).get_name()\n",
" rc('font', family = font_name)\n",
"else:\n",
" print('Unknown system... sorry~~~~')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**결과 변수인 data_result 확인**"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-25T06:32:16.539914Z",
"start_time": "2020-05-25T06:32:16.523958Z"
},
"scrolled": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>소계</th>\n",
" <th>최근증가율</th>\n",
" <th>인구수</th>\n",
" <th>한국인</th>\n",
" <th>외국인</th>\n",
" <th>고령자</th>\n",
" <th>외국인비율</th>\n",
" <th>고령자비율</th>\n",
" </tr>\n",
" <tr>\n",
" <th>구별</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>강남구</th>\n",
" <td>2780</td>\n",
" <td>150.619195</td>\n",
" <td>570500.0</td>\n",
" <td>565550.0</td>\n",
" <td>4950.0</td>\n",
" <td>63167.0</td>\n",
" <td>0.867660</td>\n",
" <td>11.072217</td>\n",
" </tr>\n",
" <tr>\n",
" <th>강동구</th>\n",
" <td>773</td>\n",
" <td>166.490765</td>\n",
" <td>453233.0</td>\n",
" <td>449019.0</td>\n",
" <td>4214.0</td>\n",
" <td>54622.0</td>\n",
" <td>0.929765</td>\n",
" <td>12.051638</td>\n",
" </tr>\n",
" <tr>\n",
" <th>강북구</th>\n",
" <td>748</td>\n",
" <td>125.203252</td>\n",
" <td>330192.0</td>\n",
" <td>326686.0</td>\n",
" <td>3506.0</td>\n",
" <td>54813.0</td>\n",
" <td>1.061806</td>\n",
" <td>16.600342</td>\n",
" </tr>\n",
" <tr>\n",
" <th>강서구</th>\n",
" <td>884</td>\n",
" <td>134.793814</td>\n",
" <td>603772.0</td>\n",
" <td>597248.0</td>\n",
" <td>6524.0</td>\n",
" <td>72548.0</td>\n",
" <td>1.080540</td>\n",
" <td>12.015794</td>\n",
" </tr>\n",
" <tr>\n",
" <th>관악구</th>\n",
" <td>1496</td>\n",
" <td>149.290780</td>\n",
" <td>525515.0</td>\n",
" <td>507203.0</td>\n",
" <td>18312.0</td>\n",
" <td>68082.0</td>\n",
" <td>3.484582</td>\n",
" <td>12.955291</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 소계 최근증가율 인구수 한국인 외국인 고령자 외국인비율 \\\n",
"구별 \n",
"강남구 2780 150.619195 570500.0 565550.0 4950.0 63167.0 0.867660 \n",
"강동구 773 166.490765 453233.0 449019.0 4214.0 54622.0 0.929765 \n",
"강북구 748 125.203252 330192.0 326686.0 3506.0 54813.0 1.061806 \n",
"강서구 884 134.793814 603772.0 597248.0 6524.0 72548.0 1.080540 \n",
"관악구 1496 149.290780 525515.0 507203.0 18312.0 68082.0 3.484582 \n",
"\n",
" 고령자비율 \n",
"구별 \n",
"강남구 11.072217 \n",
"강동구 12.051638 \n",
"강북구 16.600342 \n",
"강서구 12.015794 \n",
"관악구 12.955291 "
]
},
"execution_count": 49,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_result.head()"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-25T06:32:17.722352Z",
"start_time": "2020-05-25T06:32:17.412882Z"
},
"scrolled": true
},
"outputs": [
{
"data": {
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jYgyYXtFfI0mS1CO6fbl2K/BHAJn5b8DHaR+01kdEvc3P2pZ564DnNX1+LnDhYovJzFxS9ZIkST2q22fy3gm8ISLeDDwO+D5mL5s+Enh/RFwMPAJ41jz7r4+Ir2bmZOPzfTz4MuxgY2wxZpZavCRJUq+Kbp/ciognA7uBLwM3Z+ZkRHw/8Azgw8CLz7PEzZl5KiKuAX4ceDgQjZ8HgK8Bt2TmWyNiN3Cwad/NwDmg+XLu4cw80VLjfmA/wNDQ+p2Hjt64vD9WK7ZhAO6dPP88fcvwpks7ss7ExASDg623smq12P9i2f/i2PuVGRkZOZWZu9pt63rIA4iIVwHPB5LZS8SfAQ5l5rnG9k3MPlTxXY1d/hL4H5l5zzzrXQ1ckJk3nee41wKnW0PdQrZs255rrrp+sdPVYaPD0xy5y2/2WYpOfYVKvV6nVqt1ZC0tnf0vlv0vjr1fmYiYN+R1/StUIuIqYDvw7Mz8ocYDFp8BfqVp2s3AHcBLgL3ASeCWbtcmSZJUVatxymQA+FpmNj9wcbYxTuPhiguBT87dexcRdwLrImJdZk61uQxLY96+po8PuQwrSZLUr1Yj5L0LOBQRdeB+YC2z34n3cwCZORMRr2X2QYzmM4sH577TLjNPMnt2T5IkSYvQ9ZCXmQ8Av3yeOd0KcceAJX358cC6tZzxNVGFqdfrjO+tFV2GJEk9r9J3uGfmRNE1SJIkFaGQd9dKkiSpuwx5kiRJFWTIkyRJqiBDniRJUgUZ8iRJkirIkCdJklRBhjxJkqQKMuRJkiRVkCFPkiSpgkr7xouI2JmZp9qMvzMzX9r0+UeAizLz5k4cd3Jqhq0HxjqxlJZhdHiaffa/EPa+WPa/WPP1f9zXXKqHFR7yIuIZwH8FpoH7gP2Z+SXgTcCVTfNuBB4JXB4RtzSGrwG+Dbi4Zc3jwGDLoXYAGzNzSe+ylSRJ6kWFhzzg14Afycx/jojvB/4L8AqAiLgd+I3M/ADws8xeXh4D9jX2va/dgpm5p3UsIsaYDZKSJEmVV4Z78u7LzH9u/P4ZYGhuQ2Ze0Qh4ZOY54BvAE4HNwLOAW4EDiz1QZmanipYkSSqzMoS8P4yIN0TEDwO/AbxlbkNE3B4Rz2+aOwq8DTgCjGXmlcDhRR5nplMFS5IklV3hl2sz82hEbAUeC7w6M882Nt0794BFRKwDXg08LjP3R8SzmA2HL2teKyJ2AwebhjYD54Czje114HBmnujeXyRJklS8KMMVzIj4Tmbvzfs2IBo/v5qZtza2XwC8GPgQMJWZ90XEo4GvAFcBF2fmTW3WvRY4fb5QFxH7gf0AQ0Prdx46emOn/jQt0YYBuHey6Cr6k70vlv0v1nz9H9506eoX02cmJiYYHGx9VlKLNTIyciozd7XbVpaQ9xHgFZn5V43Pg8BHgFrjXry5ea8AvpKZt7Rf6SHrLirkNduybXuuuer6JdWvzhkdnubIXYWfYO5L9r5Y9r9Y8/Xfr1Dpvnq9Tq1WK7qMnhUR84a8MtyTB5DAA0UXIUmSVBVl+d/GVwK/HhGXND4H8Prms3hNDjXO6DW7LTPf3NUKJUmSekgpQl5mngGet4h5bwfevoSljwF++bEkSeo7pQh53ZKZE0vdZ2DdWs54D0Zh6vU643trRZfRl+x9sex/sey/qqgs9+RJkiSpgwx5kiRJFWTIkyRJqiBDniRJUgUZ8iRJkirIkCdJklRBhjxJkqQKMuRJkiRVkCFPkiSpggx5kiRJFdTzrzWLiOcDH8zM7MR6k1MzbD0w1omltAyjw9Ps60L/x31VnSSpz5Qu5EXEzwNrMvNw09hjgHe0mT4K/AwwBkw3zT8ODLbM3QFszMypjhctSZJUMqUJeRHxMGA/sB6YiohrgRsz877M/HxE7AF2ZObHI+J7gHsy80sR8ZC1MnNPm/UfFAQlSZKqrBQhLyJeBWwD3s1sEFvb+DkcEfc0zuo9AngZ8HHgBcCHgC8t5TiduqQrSZJUdoWHvIh4JPCxxg/ADzNb1weB327MeRQwBVzYmLMOuL9pmf8ZER/OzN9e4FAznaxbkiSpzAoPecBG4Mqmzw8wG+Cax8aAe/hWyLuQ2dA35yWZORMRu4GDTeObgXPAWYCIqAOHM/NEcwERsZ/ZS8UMDa3n0LBXdYuyYWD24YtOq9frHV+zaiYmJuxTgex/sex/cex99xQe8jLzs8BnI2IT8GrguxqbPgf8euN+vM3AU4ANEfF04DHAcERcNLdMY62TwMm5tRv39Z1uDXVtargBuAFgy7bteeSuwtvSt0aHp+lG/8f31jq+ZtXU63VqtVrRZfQt+18s+18ce989ZUoz7wZ+Hvhk4/PTgN8DLmf2jNyTgQ8ATwf+BHgk33qC1nvtJEmSmpQp5A0An5t7OCIi/hK4CCAzPwF8ot1OEeEDFZIkSS3KFPKuA25u+kqUAH6huHIkSZJ6V2lCXmZ+FPjoMva7YoHNx3jwAxqSJEl9oTQhrxsyc2Kp+wysW8sZX4FVmHq97kMSkiR1wJqiC5AkSVLnGfIkSZIqyJAnSZJUQYY8SZKkCjLkSZIkVZAhT5IkqYIMeZIkSRVkyJMkSaogQ54kSVIFGfIkSZIqqNSvNYuIIeBRmflXq3XMyakZth4YW63DqcXo8DT77H8h7H33jfvKREmrqFRn8iLiQy1D3w1c1bT99jb7nGgzdjwibm/5+XJErOt81ZIkSeVTmjN5EbEW2NUIYi8ErgEeAdzSNG1rm6B3WetambmnzfpjwHTnKpYkSSqv0oQ8ZkPdu4DXZ+YvAu+NiBpwedOcrwLvadlv/2IPkJm50iIlSZJ6QeEhLyLWMBvwNmXmdRHxsoj4beBVbaa/BGi95PrHizzUzArKlCRJ6ilR9MmtiLgYeF5m3tw0tjkzvxARTwO+F/hb4GDTbhuAAL7YNHaY2SDXPG8zcA442zwvMx90H19E7KdxRnBoaP3OQ0dvXPHfpeXZMAD3ThZdRX+y9903vOnSebdNTEwwODi4itWomf0vjr1fmZGRkVOZuavdtsJD3pzGGb3XAT/EbFhbC3waOJCZky1zrwYuyMybzrPmtcDp1lC3kC3btueaq65fYvXqlNHhaY7cVfgJ5r5k77tvoadr6/U6tVpt9YrRg9j/4tj7lYmIeUNemf6N/pPApUAtMx+Ab55hOwS8NiLWAzc37xAR+4DHZ+amVa5VkiSp1MoU8gDOzQW8hq/P/ZKZXwZqrTu0+woVSZKkflemkPdO4FBEfITZrzpZC5wBXlNoVZIkST2oNCGv8fUmb1jGrj+zwLZjwNRSFhtYt5Yzfit9Yer1OuN7a0WX0ZfsvSRVS2lC3nJl5l8vsG1iNWuRJEkqi1K91kySJEmdYciTJEmqIEOeJElSBRnyJEmSKsiQJ0mSVEGGPEmSpAoy5EmSJFWQIU+SJKmCDHmSJEkV1PNvvOi0yakZth4YK7qMvjU6PM0++1+IpfZ+3Nf/SVKplfpMXkQMRcS/K7oOSZKkXlOqM3kR8aHMfG7T0HcDlwNvbGz/O+Dult0mM/PZLescBwZb5u0ANmbmVGerliRJKp/ShLyIWAvsioh1wAuBa4BHALc0Tbs7M2vnWysz97RZfwyY7ky1kiRJ5VaakMdsqHsX8PrM/EXgvRFRY/ZM3pw1EXE7s2fpAvh6Y/z5mXnf+Q6QmdnZkiVJksqp8JAXEWuYDXibMvO6iHhZRPw28KrWuZn5zMY+VwMXZOZNSzjUTCfqlSRJ6gVR9MmtiLgYeF5m3tw0tjkzvxARTwO+F/gM8PKm3dYweyavObj9DnAJcLBpbDNwDjjbNHY4M0+01LAf2A8wNLR+56GjN67479LybBiAeyeLrqI/LbX3w5su7V4xfWhiYoLBwdZbibVa7H9x7P3KjIyMnMrMXe22FR7y5jTO6L0O+CFmw9ta4NPAgcycbMzZxOwZvic1dvss8NbMvGeeNa8FTreGuoVs2bY911x1/bL/Dq3M6PA0R+4q/ARzX1pq7/0Klc6q1+vUarWiy+hb9r849n5lImLekFemr1D5SeBSoJaZI41Ls58FDjXNeR9wAvixxs+twM2tC0mSJPW7MoU8gHOZ+UDT57kHK+aevr0IOJWZ92fm/cCngAsbT+RKkiSpoUzXxd4JHIqIjzD7VSdrgTPAawAycyYiDgIfiIjm/Q763XeSJEkPVpqQ1/h6kzecZ85twG1LWPYYsKQAOLBuLWe816gw9Xqd8b21osvoS/ZekqqlNCGvGzJzougaJEmSilC2e/IkSZLUAYY8SZKkCjLkSZIkVZAhT5IkqYIMeZIkSRVkyJMkSaogQ54kSVIFGfIkSZIqyJAnSZJUQaV+40VEbAK+IzPvXK1jTk7NsPXA2GodTi1Gh6fZZ/8LYe+LZf87Y9zXUkrfVKqQFxEnMvPKpqHHA5cDd7bMuxq4ODOPzbPOcWCwZXgHsDEzl/QuW0mSpF5UqpAHXDjfhoh4NjDa+PgYICPixY3PRzPzQ3NzM3NPm/3HgOkO1ipJklRapQl5ERHAroi4EHgacAWwFfgbgMy8Fbi1Me848ADwwszMxR5jKXMlSZJ6WZkevNgNfAF4AXAauAX4aPOEiNgI3AS8C3gP8LsRsWWR6890rFJJkqSSK8WZvIi4APjPwLOAdwAnMvMvImII2NSY88uN3/9f4Gxj11PAL0XEv2bmdRGxGzjYtPRm4Nzc/IioA4cz80TX/yhJkqQCRdFXMBsB723AWGYej4inAm8Crga+C7g8M9/Yss8+gMy86TxrXwucPl+oi4j9wH6AoaH1Ow8dvXF5f4xWbMMA3DtZdBX9yd4Xy/53xvCmS5e138TEBIODrc/raTXY+5UZGRk5lZm72m0rw5m8jcAdmXkcIDP/NCJeB6xa+szMG4AbALZs255H7ipDW/rT6PA09r8Y9r5Y9r8zxvfWlrVfvV6nVlvevloZe989hf8bJTPvBu5uGbsTYPYZC2hzGXZufF/TRy/DSpIkNRQe8hYjM08CJ4uuQ5IkqVeUOuRlZh2or2CJY4BffixJkvpOqUPeSmXmxFL3GVi3ljO+Fqcw9Xp92ffUaGXsfbHsv6ROK9P35EmSJKlDDHmSJEkVZMiTJEmqIEOeJElSBRnyJEmSKsiQJ0mSVEGGPEmSpAoy5EmSJFWQIU+SJKmCDHmSJEkVVMhrzSJiE/AdmXnnAnMuAZ6emXesXmUwOTXD1gNjq3lINRkdnmaf/e+acV/ZJ0l9Y1VCXkScyMwrm4YeD1wO3BkRJ5rqOJuZVzXGXg78BHBH0zpngH9qWX5zZm5vOd5xYLBl3g5gY2ZOrfgPkiRJKrnVOpN34UIbM/OKRa7z9y1hkUYgbF1vT+tYRIwB04s8jiRJUk/resiLiAB2RcSFwNOAK4CtwN8sY7mtEXF7y9hli905M3MZx5QkSeo5q3EmbzfwBeAFzF56/VfgqcCmBfb5HuAY8MXmwcx8wgrqmFnBvpIkST2lqyEvIi4A/jPwLOAdwInM/IuIGGLhkPcZ4KeANzTW2Q0cbNq+ltkng5vvrzvMbJBrnrcZOAecbaxTBw5n5oMu8UbEfmA/wNDQeg4Ne1W3KBsGZh++UHfU6/V5t01MTCy4Xd1l/4tl/4tj77unayGvEfDeBhzLzM9HxGuBmyPi6jZztzMb3C4B/g5Ims68ZeZJ4GTT/OcCWzPzrW0O3TzvWuB0a6hrlZk3ADcAbNm2PY/cVchDx2I24Nn/7hnfW5t3W71ep1abf7u6y/4Xy/4Xx953Tze/J28jcEdmHgfIzD8FXsdsgGv2fuAa4KXMnvF7+HwLRsT7Gr9OAhOdLliSJKkqunbKJDPvBu5uGbsTYPZZjG+O/Vbrvs3bWzyqsc+qfneeJElSr+m162I72jxdC/CTmfmPq16NJElSSRUS8jKzDtQX2D73XXj7WsbXL/FQx3jwwxmSJEl9odfO5C1JZi75vr2BdWs546ufClOv1xd8OECSJC1ONx+8kCRJUkEMeZIkSRVkyJMkSaogQ54kSVIFGfIkSZIqyJAnSZJUQYY8SZKkCjLkSZIkVdC8X4YcEQeBbBk+AVwJkJn/NSLenJnXdbE+SZIkLcNCZ/I+Bvwj8G3AdwIfB57G7GvCnt+Ys6ur1bZ2JGwAACAASURBVEmSJGlZ5j2Tl5kfjYgvMxvqHmh8fgLwl8BXV6O4iHh+Zn5gNY41Z3Jqhq0HxlbzkGoyOjzNvmX0f9xX0UmS9CDne3ftW4DHAeMR8RbgdDeKiIiPAA80DU1n5pXAzwAfaMx5IfDKeZb4rcy8uWm948Bgy5wdwMbMnOpY4ZIkSSW1YMjLzCvn7ruLiDe3m9KhOh7IzCvmPkTEiTa1/EEjDD4rM9/bmPci4I7MPNsyd0/r/hExBkx3qF5JkqRSW8zTte2CXETE64DLOlzP+awDntf0+bnAhYvdOTM7FUolSZJKbcEzeRHx48B3RsRVjbkfBO4HPg88ktmHMbppTUTcAtyemW8H7uPBl2EHG2OLMdPp4iRJksoqFjq5FREvbfp4JjM/0ZUiIm5vvVzbuFT8zfGIuAb4ceDhQDR+HgC+BtySmW+NiN3AwaalNwPngObLuYcz80GXgyNiP7AfYGho/c5DR2/s+N+oxdkwAPdOLn2/4U2Xdr6YPjMxMcHgYOutrFot9r9Y9r849n5lRkZGTmVm2287WTDkPWRyxA9l5oc7Vtm31v0I8Kmmoe9uDXkt868GLsjMm86z7rXA6dZQt5At27bnmquuX+x0ddjo8DRH7jrf80AP5dO1K1ev16nVakWX0bfsf7Hsf3Hs/cpExLwh73yXa9dmZvNlzgNAx0Me8BLg4qbPyziXI0mSpDnnO2XyjxHxycbvDyw4cwUy84sLbW9zGXZufF/Tx4dchpUkSepX5wt5f56Z33yatXFZddVl5kngZBHHliRJ6kXnC3mtN+z9XxHxQy1jf9StLxhudz/eEh1j9o0dizawbi1nvL+rMPV6nfG9taLLkCSp5y31DvdvA57A7JOtMBsCP8YSg9RqycyJomuQJEkqwlJD3hcy8793pRJJkiR1zPneeNH6RcO+MUKSJKkHLBjyMvMFLUPRdqIkSZJKZTHvrm32pq5UIUmSpI5aUsjLzNu7VYgkSZI6Z6ln8iRJktQDDHmSJEkVZMiTJEmqIEOeJElSBS31y5Arb3Jqhq0Hxoouo2+NDk+zz/4XolO9H/e1gJJUCj1/Ji8inld0DZIkSWVTmjN5EfF3wN0tw5OZ+ezG9npjbBi4C7gnM/cCPw38r5a1jgODLWvtADZmZinfsytJktRJpQl5wN2ZWZtvY2bWImIA+OxC8xpz97SORcQYML3SIiVJknpBmS7XromI2yPiExHxfxq/3x4RlzTNGQWOR8Qrm3eMiHpEtL6C7SEy03fvSpKkvlCaM3mZ+UyAiLgauCAzb5rbFhEXAQeAf87MN0bEqyPiKLOhj/Od2WuY6XjRkiRJJRVFn9yKiKcDL28aWgMEDw5l72D2RNyfNO33HZn5TxHxa5n5msbYbuBg036bgXPA2aaxw5l5oqWG/cB+gKGh9TsPHb1x5X+YlmXDANw7WXQV/alTvR/edOnKF+lDExMTDA623kqs1WL/i2PvV2ZkZORUZu5qt63wkDcnIjYBrwKe1Bj6LPDWzLynaU693b7zncmLiGuB062hbiFbtm3PNVddv9jp6rDR4WmO3FWaE8x9pVO99ytUlqder1Or1Youo2/Z/+LY+5WJiHlDXpn+a/o+4HXA6xufnw7cDHzf3IR2YS4iFh3gJEmS+kUpHryIiLXARcCpzLw/M+8HPgVcGBHriq1OkiSp95TiTF5mzkTEQeADEdG86eAivtfuc92rTJIkqTeVIuQBZOZtwG3L2O/nFth8DFjSlx8PrFvLGe8pKky9Xmd8b63oMvqSvZekailNyOuGzJwougZJkqQilOKePEmSJHWWIU+SJKmCDHmSJEkVZMiTJEmqIEOeJElSBRnyJEmSKsiQJ0mSVEGGPEmSpAoy5EmSJFVQad94ERGXAE/PzDtW87iTUzNsPTC2modUk9HhafbZ/0L0Q+/HfWWhpD7S9ZAXEX8H3N0yPJmZz25sP9FUx9nMvKox9nLgJ4A7mtY6A/xTy1qbM3N7yzGPA4Mt83YAGzNzSe+ylSRJ6kWrcSbv7sysLTQhM69Y5Fp/n5lXNg80AmHrentaxyJiDJhe5HEkSZJ62mqEvDURcTuzZ9YC+Hpj/PmZed8S19raWKvZZYvdOTNziceTJEnqSV0PeZn5TICIuBq4IDNvWsRu3wMcA77YstYTVlDKzAr2lSRJ6inRrZNbEfF0Zu+rm7OG2TN5zWHrd4BDbS7B3gbsB96QmfsiYjdwsGnK2sZ6zffXHW6s3TxvM3AOONs8LzMfdIk3IvY3jsfQ0Pqdh47euNg/Ux22YQDunSy6iv7UD70f3nRp0SXMa2JigsHB1luJtVrsf3Hs/cqMjIycysxd7bZ17UxeZn4C+EREbAJeBTypsemzwFsz8x6AiCAitjMb3C4B/g5ImsJgZp4ETs59jojnAlsz861tDt0871rgdGuoa1PrDcANAFu2bc8jd5X2oePKGx2exv4Xox96P763VnQJ86rX69RqtaLL6Fv2vzj2vntW43vy3gecAH6s8XMrcHPT9vcD1wAvBZ4FPHy+hSLifY1fJ4GJbhQrSZJUBV393/aIWAtcBJzKzPsbY58CLoyIdZk5lZm/1Wa/+ZZ8FMBqf3eeJElSr+lqyMvMmYg4CHygJbgdXOb31e1o83QtwE9m5j8uq0hJkqQKWo2na28DblviPnMPYuxrGV+/xMMf48EPZ0iSJPWFSt9lnZlLvm9vYN1azvjqo8LU6/VS3xxfZfZekqplNR68kCRJ0ioz5EmSJFWQIU+SJKmCDHmSJEkVZMiTJEmqIEOeJElSBRnyJEmSKsiQJ0mSVEGGPEmSpAoy5EmSJFVQpV9rthyTUzNsPTBWdBl9ZdzXyEmS1HGlCXkR8VfAP7YMfzkzX9Q05yTwEy1zfjczr2hZ6zgw2DJvB7AxM6c6VLIkSVJplSbkAXe3hrU2Hgb8WMvYw1snZeae1rGIGAOml1+eJElS7yhTyFuMhwGtAe4hIW8+mZmdLUeSJKmceiLkRcQa4EJgZ2PoPzBb+7sa2y8GpjJzZoFlFtomSZJUKVGWk1sRcTOwHtgABPDFxqZnA8PA/3OeJd7J7H14B5vGNgPngLNNY4cz80TLsfcD+wGGhtbvPHT0xmX+FVqO4U2XfvP3iYkJBgdbb6fUarD3xbL/xbL/xbH3KzMyMnIqM3e121aakDcnIq4GLsjMm9ps2wi8Cngis0HwL4G3ZmbrAxtz868FTreGuoVs2bY911x1/XJK1zI1P11br9ep1WrFFdPH7H2x7H+x7H9x7P3KRMS8Ia/XvifvPcBJ4CXMXrL9MPDeQiuSJEkqocLvyYuI3Tz4Euvc+L6mj3OXWAeAT2bmZGPOJ4GLIyJ8qEKSJOlbCg95mXmS2bNzi/E64P0RMRfoAnidAU+SJOnBCg95S7HEQAhwDPDLjyVJUt/pqZC3VJk5sdR9Btat5Yyv2ZIkST2u1x68kCRJ0iIY8iRJkirIkCdJklRBhjxJkqQKMuRJkiRVkCFPkiSpggx5kiRJFWTIkyRJqiBDniRJUgUZ8iRJkiqo519rFhGbgMsy8086sd7k1AxbD4x1Yiktw+jwNPvsfyGq1PtxX00oSeUJeRFxIjOvPM+c1wP/NzANfBH4T8BjgSuAP2madxwYbNl9B7AxM6c6WbckSVIZlSbknU9EPBF4fGb+QOPzK4G9wOda52bmnjb7jzEbDiVJkiqvl+7J+wfg4RHxnIh4JrNn9P73UhbIzOxKZZIkSSVTpjN5OyOi3mb85Zn5N5l5LiJeBPwgsAl4HTAJPGqR6890pkxJkqTyi7Kd3IqIq4ELMvOmprGLgLczG9QeBXw78EfABPBp4OmZ+csRsRs42LTcZuAccLZp7HBmnmg55n5gP8DQ0Pqdh47e2Ok/S4u0YQDunSy6iv5Upd4Pb7q06BKWbGJigsHB1luJtVrsf3Hs/cqMjIycysxd7baV6UzevDLz34CXAUTE04ErgXcDrwb+PfCZxryTwMm5/SLiWuB0a6hrs/4NwA0AW7ZtzyN39URbKml0eBr7X4wq9X58b63oEpasXq9Tq9WKLqNv2f/i2Pvu6aV78oiItwIPAJ8EvgAcBd5XaFGSJEklVPj/tre5xDo3vq/p49wl1kcAd2fmJxvjpxs/kiRJalJ4yGu9xLoI74uI+1vGPpWZP9/BsiRJknpa4SFvKTLz6iXucgxY0pcfD6xbyxm/Lb8w9Xq9J++nqgJ7L0nV0lMhb6kyc6LoGiRJkorQUw9eSJIkaXEMeZIkSRVkyJMkSaogQ54kSVIFGfIkSZIqyJAnSZJUQYY8SZKkCjLkSZIkVZAhT5IkqYJ67o0XEfHOzHxp0+cfAS7KzJs7sf7k1AxbD4x1Yiktw+jwNPvsfyGaez/uq/0kqeeVJuRFxPuAR7UMPz4zL2tsvxF4JHB5RNzS2H4N8G3AxS1rHQcGW9baAWzMzCW9y1aSJKkXlSbkZeZVrWNNYQ7gZ5m9vDwG7GuM3TfPWnvarDUGTK+4UEmSpB5Q9nvyvllfZp4DvgE8EdgMPAu4FTiw2MUyMztdoCRJUhkVfiYvInYDB+fZ/B0RUQcOZ+YJYBR4G3AE+NHM/P2IeDEtl2vnMdOJeiVJknpB4SEvM08CJxeaExHrIuIXgMdl5v6IeBbwhxHxspZ5rYFxM3AOONvYXudbgVGSJKmyoixXMCPiM8BXWoY3Z+YTIuIC4MXAh4CpzLwvIh7dmH8VcHFm3tRmzWuB0+cLdRGxH9gPMDS0fuehozeu+O/R8mwYgHsni66iPzX3fnjTpcUW04cmJiYYHGx9Xkyrxf4Xx96vzMjIyKnM3NVuW+Fn8pp8JTOvaB6IiBMAmTkN/G5EvILZYHdLZn6pMe09Kz1wZt4A3ACwZdv2PHJXmdrSX0aHp7H/xWju/fjeWrHF9KF6vU6tViu6jL5l/4tj77un7A9eSJIkaRnKdMpkS+OeuWbb2sw71Dij1+y2zHxzd8qSJEnqPaUJeZn57xYx5+3A25ew7DHALz+WJEl9pzQhrxsyc2Kp+wysW8sZX+lUmHq97v1gBbH3klQt3pMnSZJUQYY8SZKkCjLkSZIkVZAhT5IkqYIMeZIkSRVkyJMkSaogQ54kSVIFGfIkSZIqyJAnSZJUQYY8SZKkCqr0a82WY3Jqhq0Hxoouo2+NDk+zz/4Xwt4Xa6n9H/f1i5LOozQhLyKOA4MtwzuAjZk5FRHrgFvb7PoU4Nszc3qxa3WwbEmSpFIqTcjLzD2tYxExBkw3tk8BV7SZc2Kpa0mSJFVdaULefDIzASJiABgDHmiZ8mQgl7KWJElS1ZU95M00/b4WOJeZz+3AWpIkSZUWRZ/ciojdwMGmoc3AOeBs09hh4GPA3wN/1maZA5l552LXyswHXeKNiP3AfoChofU7Dx29cZl/jVZqwwDcO1l0Ff3J3hdrqf0f3nRp94rpQxMTEwwOtt7KrdVg71dmZGTkVGbuaret8JDXKiKuBU63BrGWOVcDF2TmTStdq9WWbdtzzVXXL3a6Omx0eJojd5X9BHM12ftiLbX/Pl3bWfV6nVqtVnQZfcner0xEzBvy/J48SZKkCuqJ/21vcxl2bnxf08eHXIaVJEnqVz0R8jLzJHCy6DokSZJ6RRlD3jGgU19Y3Mm1JEmSekbpQl5mThS51sC6tZzxhubC1Ot1xvfWii6jL9n7Ytl/SZ3mgxeSJEkVZMiTJEmqIEOeJElSBRnyJEmSKsiQJ0mSVEGGPEmSpAoy5EmSJFWQIU+SJKmCDHmSJEkVZMiTJEmqoNK91qxZRPwg8InMvG+BOd8PXJiZd3TimJNTM2w9MNaJpbQMo8PT7Ctp/8d93Z0kqYeUIuRFxKuBH24aenJmPhr4CeAMcF9EHAWeAnwnMA78G/BKYBNwcct6x4HBlsPsADZm5lQ3/gZJkqQyKUXIy8y3RMT1wCWZ+bWIuL3NnGsBIuL3gTdk5p83Pj+5zdw9rWMRMQZMd7x4SZKkEirTPXmPAd7S+L35bNvvRcR/BIiIS4AnAi9azgEyM1dUoSRJUo8oPORFxIaIeCqzl2I3RMQPAP89Ir63MeU/ZOZvN37/FeBngS0RUVvioWY6UrAkSVIPKMPl2g3AU5kNYe8HNgOTtISyiPgfwF9n5smI+P+AGyNismXObuBg09Bm4BxwtrG9DhzOzBMt++0H9gMMDa3n0LBXdYuyYWD24YsyqtfrRZfQVRMTE5X/G8vM/hfL/hfH3ndPlOUKZkQ8Hng1sK0x9A/Au4A/ycyZiHgMs4FtOjO/2rTfi4GLM/OmNmteC5xuDXUL2bJte6656vrl/yFakdHhaY7cVYb/93ioqj9dW6/XqdVqRZfRt+x/sex/cez9ykTEqczc1W5bKf5rGhEXAO8G/mPTAxXfDdwEfB8wk5mfj4hXAF8BbpnbNzPfs/oVS5IklVvh9+Q1XNj45982jf0tsw9gXPjQ6ZIkSVpIKc7kZea5iHg9cDwiHmgMrwHelJkTLdMPNc7oNbstM9/c9UIlSZJ6RClCHkBm/iHwh+eZ83bg7UtY9hgP/joWSZKkvlCakNcNbc4CntfAurWcqfgN9mVWr9cZ31srugxJknpeWe7JkyRJUgcZ8iRJkirIkCdJklRBhjxJkqQKMuRJkiRVkCFPkiSpggx5kiRJFWTIkyRJqiBDniRJUgUZ8iRJkiqo1K81i4hjwBszc3yBOTsz81Snjjk5NcPWA2OdWk5LNDo8zT77Xwh7X5xxX6UoqQtKEfIi4gBwZdPQE4CntMx5BnAYeAD4GvCfMvNLwJta9iUijgODLYfZAWzMzKnOVi9JklQ+pQh5mXmY2QAHQES8D2gNY78GPD8zvxIRPwC8Edg/z3p7WsciYgyY7ljRkiRJJVaae/IiYigifrXx8RJmz9YBPDwiLgbuy8yvNMY+DTyyad96RLzgfMfIzOxkzZIkSWVVijN5DRcAGxu/H8nMqYgAOAS8FzgeEUeAU8BzgblASGbWFrH+TEerlSRJKrEo+uRWRHwfsA94GPB04BPARcBngC00PXgRERuBy4DPZubXGmNvyMzXN37fDRxsWn4zcA442zR2ODNPtNSwn8al36Gh9TsPHb2xs3+kFm3DANw7WXQV/cneF2d406VMTEwwONh6K7FWi/0vjr1fmZGRkVOZuavdtjKEvEuAIWbvl5sC7ge+kZnfaH26NiLqbZa4JDOfOs/a1wKnW0PdQrZs255rrrp+aX+EOmZ0eJojd5XpBHP/sPfFGT/8HOr1OrVarehS+pb9L469X5mImDfkFf5v9My8D7gvIt6ema9o2fwKmi6ztrssGxGLDnCSJEn9ovCQ12R760Bm+jSsJEnSMpQp5K2d53LsQ+6ha/HVLtUjSZLUs0oT8jJzZJn7/fgCm4/x0O/bW9DAurWc8dvnC1Ov1xnfWyu6jL5k7yWpWkoT8rohMyeKrkGSJKkIpfkyZEmSJHWOIU+SJKmCDHmSJEkVZMiTJEmqIEOeJElSBRnyJEmSKsiQJ0mSVEGGPEmSpAoy5EmSJFVQpd94sRyTUzNsPTBWdBl9a3R4mn32f0HjvnZPkrQIpT6TFxE/tYg5myJi12rUI0mS1CtKEfIi4lci4vbGz5mIeHlj0wvazD3RMvR44MqWOceb1pv7+XJErOvSnyBJklQqpbhcm5mvnfs9It4D3LbA9AsXsd6e1rGIGAOml1WgJElSjynFmbw5EfFC4O7M/HzT2Mca40REALsi4sKIuDwifhnYt9j1MzM7XLIkSVIpleJMXkSsAX4WeAxwXfO2zLy86eNu4AvMXsa9A/hX4KnApkUcZqYjxUqSJPWAKPrkVkQ8AvifwDsy85aWbb+ZmT/d+P0C4DhwDfAO4Ecz86sRUQMuz8w3RsRu4GDTEpuBc8DZprHDmfmg+/oiYj+wH2BoaP3OQ0dv7OSfqCXYMAD3ThZdRbkNb7q0K+tOTEwwODjYlbV1fva/WPa/OPZ+ZUZGRk5lZtsHUAs/k5eZ/wo8JyKeEhEf4lv33K0BjsA3A97bgGOZ+fmIeC1wc0Rc3bLWSeDk3OeIuBY43Rrq2tRwA3ADwJZt2/PIXYW3pW+NDk9j/xc2vrfWlXXr9Tq1WnfW1vnZ/2LZ/+LY++4p039N387s2bl7ACJiEPhwRPwfYBC4IzOPA2Tmn0bE6wDvsZMkSWqjTCEveXBom/s9M/Nu4O4HTc68E2D2WQxJkiQ1K1PIuwa4sem77NYCb8rMfymwJkmSpJ5UmpCXmZ8Blvy+psysA/V5Nh8Dppay3sC6tZzxtVGFqdfrXbvnTJKkflKakNcNmTlRdA2SJElFKNWXIUuSJKkzDHmSJEkVZMiTJEmqIEOeJElSBRnyJEmSKsiQJ0mSVEGGPEmSpAoy5EmSJFWQIU+SJKmCKv3Gi+WYnJph64GxosvoW6PD0+zr0f6P+zo8SVKJlPpMXkT8VNE1SJIk9aJShLyI+JWIuL3xcyYiXt7Y9IKWebe32fdEm7HjTevN/Xw5ItZ16U+QJEkqlVJcrs3M1879HhHvAW6bZ+rWNkHvsjbr7Wkdi4gxYHoldUrS/9/evcfIVZZxHP8+W1otbqRoa6MFVMQ0Eask1Gu87CZivMvF+Ie3mKglRA3ESiI2gRggEEmxJJJUSsCYEAngBSPhnoyAf5CIMUGxGy+pRYhNpdWw0AAtj3/sGXsYdpfp7s68Z89+P8lk55zznjPv/OZ0++w75yJJi0UjiryuiDgT2J2Zj9bmPQBcmZk/B/4L3Niz2qZ+t5+ZuSAdlSRJarhoQt0TESPAucDxwPmZeaiaf0dmfqTWbj3Q+5Xrc5k50cdr/CozPzXDsk1UxeLq1WtOvXDbjrm9Ec3b2pWw50DpXszNhnXHlO7CvExOTjI6Olq6G0uW+Zdl/uWY/fyMj48/lJkbp1tWfCQvIlYBNwDXZ+YPehb/rWpzGrClNn8tEMC/quUAlwOHetodBzwN7KvadYDLM/MFx/Fl5jXANQAnnHhSbn24eCxL1uYNB1ms+e/6/FjpLsxLp9NhbGysdDeWLPMvy/zLMfvBKf6/aWb+B/h4RJwSEb8GVlSLRoCtVZu7gbu760TEF4CjMvPH02yy3u48YGdvUSdJktR2xYu8mu3AWZn5GEBEjAJ3RcSDmbkvItYAN9dXiIgvA2/OzHVD760kSVKDNanIy+pRn/7/z8zcC4z1rjTdJVQkSZKWuiYVeecAO2rXslsGXJqZ+wv2SZIkaVFqTJGXmX8A5nJfqG/Osuxa4Lkj2djK5cuY8PZUxXQ6nUV/AoMkSU3QmCJvrjLzL7MsmxxmXyRJkpqiEbc1kyRJ0sKyyJMkSWohizxJkqQWssiTJElqIYs8SZKkFrLIkyRJaiGLPEmSpBayyJMkSWohizxJkqQWavQdLyLi7Mz80TBf88Bzh3jDd24b5kuqZvOGg3y5Rfnv8hZ5kqRCGlHkRcRlwDuqyeOBKzLzWuAM4Ee1dn8HdvesfiAzP9qzvV8Coz3t3g68LjOP6F62kiRJi1EjirzMvKD7PCJuBO6coenuzBzrY3un986LiNuAg3PtoyRJ0mLSiCKvKyLOZKqQe7Q27wHgysz8OTASEfcwNUoXwJNVs09n5lMvtf3MzAF0W5IkqXEaUeRFxAhwLlNf1Z5fX5aZ76s9/0DV/gvAUZn54yN4mUPz76kkSdLiEKUHtyJiFXADcH1m3tKz7OrM/HpEvBv4am3RCFMjefXC7TrgFcCW2rzjgKeBfbV5l2fmHT2vswnYBLB69ZpTL9y2Y35vSnO2diXsOVC6Fwtnw7pjSnehb5OTk4yO9h7KqmEx/7LMvxyzn5/x8fGHMnPjdMuKF3ldEXEKcAmwopo1AmzNzNtrbdYB3wBOrmb9CfhhZj42wzbPA3b2FnWzOeHEk3Lks1fN4R1oIWzecJCtDzdigHlBLKazazudDmNjY6W7sWSZf1nmX47Zz09EzFjkNel/0+3AWd2CLSJGgbsi4sHM7I7E3QR8F7iomn43cDPw3mF3VpIkqcmadDHkrB716f//jIhlwMuAhzLz2cx8Fvg9sCIilg+1p5IkSQ3XpJG8c4AdtYJtGXBpZu4HyMxDEbEFuDUi6utt8dp3kiRJL9SYIi8z/wDMegBTZt7JzNfQm861gAWgJElachpT5A1CZk4e6Torly9jYhEdLN82nU6HXZ8fK90NSZIWvSYdkydJkqQFYpEnSZLUQhZ5kiRJLWSRJ0mS1EIWeZIkSS1kkSdJktRCFnmSJEktZJEnSZLUQhZ5kiRJLRSZWboPjXLCiSflyGevKt2NJWvzhoNsfbjVN2JpLLMvy/zLMv9y2pr9riHdPSsiHsrMjdMta/RIXkSc3Uebjw6jL5IkSYtJI4q8iLgsIu6pHhMR8dVq0Rm1Ntsj4g3TrL55mu39sra97mNvRCwfzDuQJElqlkaMj2bmBd3nEXEjcOeRrB8RG4F/ZObeanunT9PmNuDgPLsqSZK0KDRiJK8rIs4Edmfmo7V5D1Tzp2v/ciCAjcCal9p+egCiJElaIhoxkhcRI8C5wPHA+fVlmfm+qs3bgJ9ExLNAVo+bpprk9j5e5tCCdlqSJKnBip9dGxGrgBuA6zPzlp5lV2fm119i/e9l5kXV89OALbXFxwFPA/tq8y7PzDt6trEJ2ASwevWaUy/ctmOub0fztHYl7DlQuhdLk9mXZf5lmX85bc1+w7pjhvI64+PjM55dW7zI64qIU4BLgBXVrBFga2beXmtzHXBCz6qvzMx3zrDN84CdvUXdbLyESlltPZV+MTD7ssy/LPMvp63ZN+ESKk1KdTtwVmY+BhARo8BdEfFgZnZH4l6XmR+qrxQRfRdwkiRJS0WTTrzoHmdXn6ZnniRJkvrQpJG8c4AdtWvZLQMuzcz9tTbLI+KenvU2DKV3kiRJi0hjjskbhOor3+cy85l+11m/fn1OTEwMsFeaTafTV0a+bgAAA2hJREFUYWxsrHQ3liSzL8v8yzL/csx+fhbLMXkLLjMnS/dBkiSphCYdkydJkqQFYpEnSZLUQhZ5kiRJLWSRJ0mS1EKtPrt2LiLiScDTa8tZDfy7dCeWKLMvy/zLMv9yzH5+Xp+Za6Zb0Oqza+doYqZTkTV4EfE78y/D7Msy/7LMvxyzHxy/rpUkSWohizxJkqQWssh7sWtKd2CJM/9yzL4s8y/L/Msx+wHxxAtJkqQWciRPkiSphSzyaiLi4oj4TUT8NiJOLt2ftoqIhyOiUz0+FxHrI+LeKvcrau38PBZIRKyJiEsj4uJquu/MZ2qr/kyT/Rcj4pFq/7+r1s7sF1hErIqIG6us74uIN7rvD88M+bv/D5GXUKlExPuBtZn5wYh4K3AF8LHC3WqrPZn5oe5ERNwOfCUzd0XEzRHxLmAFfh4LaSvwV+Doanob/Wf+oraZ+WCJN7FI9Wa/CrggM2/tNpjl94/Zz8/RwLcy8/GI+DjwbeBE3PeHZbr8d+L+PzSO5B32YeCnAJn5R+BVZbvTas93n0TEUcDLM3NXNetnwHvw81hQmfkl4D44ssxnaas+1bOvrAL29zQz+wHIzMcz8/Fqcj/wDO77QzNN/k/h/j9UFnmHvQbYW5s+GBHms8Ai4hXAm6qh+5uA1wJP1Jo8ARyLn8cgraHPzIG1M7TV3B0FfD8i7o+ITdU8sx+giFjH1CjSVtz3h66W/zbc/4fKr2sP+y8v3IGez8znZ2qsucnMp4A3AUTEacCVTP1l13UsU//YV+LnMSj/oc/MgX0ztNUcZeZFwEURcTRwa0T8lml+/2D2CyIiPgF8Evga8DTu+0NVzz8znwDc/4fIkZHD7gc+AxARbwH+WbY77RQRy2qTe4EEXlb9pQdwJnAvfh4Dk5kH6DPzWdpqjqqvoQAOAE8y9W/A7AcgIt4GfDIzz87MJ9z3h6s3/2qe+/8QOZJ32G3AxyLifqZ2vLML96etToqI64Bnq8c5wKuBWyLiGeBXmfnniJjAz2OQvkX/mb+obZkut8ZlEfFOpn7//iIzH4mInZj9IHwEeH9EdKrp3bjvD9N0+e9x/x8eL4YsSZLUQn5dK0mS1EIWeZIkSS1kkSdJktRCFnmSJEktZJEnSZLUQhZ5kiRJLWSRJ0mS1EIWeZIkSS30P/th0n/3QVyqAAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 720x720 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.figure()\n",
"# kind = 'barh': 수평바(horizontal bar)로 그리도록 지정\n",
"data_result['소계'].plot(kind = 'barh', grid = True, figsize = (10, 10))\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**위 그래프를 시각적으로 보기 쉽게 데이터를 정렬한 후, 다시 시각화**\n",
"- '강남구'의 CCTV 개수가 다른 곳들에 비해 월등히 많다\n",
"- 그 뒤를 이어서 '양천구', '서초구', '은평구' 순으로 CCTV가 많이 설치되어 있다\n",
"- 내가 예전에 살던 동네인 '강동구'는 CCTV가 적었다. 근데 '송파구'는 왜 이렇게 CCTV 개수가 적을까??"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-25T06:32:19.267909Z",
"start_time": "2020-05-25T06:32:18.994610Z"
}
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x720 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"data_result['소계'].sort_values().plot(kind = 'barh', grid = True, figsize = (10, 10))\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**인구 대비 CCTV 비율을 계산 후, 데이터를 정렬하고 다시 시각화**\n",
"- '용산구'와 '종로구'가 인구 대비 CCTV 비율이 매우 높다\n",
"- 아니나 다를까 '송파구'의 경우, 인구 대비 CCTV 비율이 제일 낮다! (도대체 왜죠?? 설명 좀...)"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-25T06:32:20.376713Z",
"start_time": "2020-05-25T06:32:20.104412Z"
},
"scrolled": true
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x720 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# 인구 대비 CCTV 비율 계산\n",
"data_result['CCTV비율'] = data_result['소계'] / data_result['인구수'] * 100\n",
"\n",
"# 데이터를 정렬하고 시각화\n",
"data_result['CCTV비율'].sort_values().plot(kind = 'barh', grid = True, figsize = (10, 10))\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**scatter() 함수를 사용하여 산점도로 시각화**"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-25T06:32:21.330618Z",
"start_time": "2020-05-25T06:32:21.172044Z"
}
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize = (6, 6))\n",
"plt.scatter(data_result['인구수'], data_result['소계'], s = 50) # s = 50: marker의 크기\n",
"plt.xlabel('인구수')\n",
"plt.ylabel('CCTV')\n",
"plt.grid()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**앞에서 말한대로, CCTV와 인구 수는 양의 상관관계(r = 0.3)가 있는지 시각화로 다시 확인**\n",
"\n",
"<참고> np.polyfit 과 np.poly1d 에 대한 자세한 설명이 있는 사이트: https://pinkwink.kr/1127\n",
"- 먼저 numpy의 polyfit 명령으로 직선 만들기\n",
" - polyfit 에서 세 번째 인자는 찾고자 하는 함수의 차수 --> 여기서는 '1차식'으로 설정"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-25T06:32:22.186153Z",
"start_time": "2020-05-25T06:32:22.166206Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"array([1.30916415e-03, 6.45066497e+02])"
]
},
"execution_count": 54,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 출력 결과: 1차식의 기울기 및 절편 값을 출력. 즉, 계수들을 출력!\n",
"fp1 = np.polyfit(data_result['인구수'], data_result['소계'], 1)\n",
"fp1"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- 위에서 출력한 계수만을 가지고, 예측값을 찾을 수 없다 --> 함수(function)로 만들어 줘서 입력을 주고 결과를 얻을 수 있어야 한다\n",
" - poly1d 함수를 사용해서 polynomial class를 만들어주기\n",
"- 즉, 직선을 산점도 위에 그리기 위해, x축과 y축 데이터가 필요\n",
"- x축 데이터는 numpy의 linspace로 만들고, y축은 poly1d로 만들기\n",
" - linspace 명령은 선형 구간을 지정한 구간의 수만큼 분할"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-25T06:32:23.312239Z",
"start_time": "2020-05-25T06:32:23.307255Z"
}
},
"outputs": [],
"source": [
"f1 = np.poly1d(fp1)\n",
"fx = np.linspace(100000, 700000, 100) # np.linspace(시작, 끝(포함), 갯수)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**결과를 보면, CCTV와 인구 수는 약한 양의 상관관계가 있음을 알 수 있다**"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-25T06:32:24.392575Z",
"start_time": "2020-05-25T06:32:24.192078Z"
},
"scrolled": true
},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 720x720 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize = (10, 10))\n",
"plt.scatter(data_result['인구수'], data_result['소계'], s = 50)\n",
"plt.plot(fx, f1(fx), ls = 'dashed', lw = 3, color = 'g') # 'ls' 옵션으로 선 스타일을 지정하고, 'lw' 옵션으로 선의 굵기를 지정\n",
"plt.xlabel('인구수')\n",
"plt.ylabel('CCTV')\n",
"plt.grid()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 시각화 자료 보충하기 (설득력 Up!)\n",
"**<아래의 사항들을 추가하여, 다시 시각화>**\n",
"- 직선에서 멀리 떨어져 있는 '구'는 이름이 같이 나타내도록 만들기\n",
"- 직선에서 멀어질수록 다른 색을 나타내도록 만들기"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**먼저 '오차'를 계산할 수 있는 코드 생성**\n",
"- '오차'가 큰 순으로 데이터를 정렬해서 다시 저장"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-25T06:32:25.582733Z",
"start_time": "2020-05-25T06:32:25.552814Z"
}
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>소계</th>\n",
" <th>최근증가율</th>\n",
" <th>인구수</th>\n",
" <th>한국인</th>\n",
" <th>외국인</th>\n",
" <th>고령자</th>\n",
" <th>외국인비율</th>\n",
" <th>고령자비율</th>\n",
" <th>CCTV비율</th>\n",
" <th>오차</th>\n",
" </tr>\n",
" <tr>\n",
" <th>구별</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>강남구</th>\n",
" <td>2780</td>\n",
" <td>150.619195</td>\n",
" <td>570500.0</td>\n",
" <td>565550.0</td>\n",
" <td>4950.0</td>\n",
" <td>63167.0</td>\n",
" <td>0.867660</td>\n",
" <td>11.072217</td>\n",
" <td>0.487292</td>\n",
" <td>1388.055355</td>\n",
" </tr>\n",
" <tr>\n",
" <th>송파구</th>\n",
" <td>618</td>\n",
" <td>104.347826</td>\n",
" <td>667483.0</td>\n",
" <td>660584.0</td>\n",
" <td>6899.0</td>\n",
" <td>72506.0</td>\n",
" <td>1.033584</td>\n",
" <td>10.862599</td>\n",
" <td>0.092587</td>\n",
" <td>900.911312</td>\n",
" </tr>\n",
" <tr>\n",
" <th>양천구</th>\n",
" <td>2034</td>\n",
" <td>34.671731</td>\n",
" <td>479978.0</td>\n",
" <td>475949.0</td>\n",
" <td>4029.0</td>\n",
" <td>52975.0</td>\n",
" <td>0.839413</td>\n",
" <td>11.036964</td>\n",
" <td>0.423769</td>\n",
" <td>760.563512</td>\n",
" </tr>\n",
" <tr>\n",
" <th>서초구</th>\n",
" <td>1930</td>\n",
" <td>63.371266</td>\n",
" <td>450310.0</td>\n",
" <td>445994.0</td>\n",
" <td>4316.0</td>\n",
" <td>51733.0</td>\n",
" <td>0.958451</td>\n",
" <td>11.488308</td>\n",
" <td>0.428594</td>\n",
" <td>695.403794</td>\n",
" </tr>\n",
" <tr>\n",
" <th>용산구</th>\n",
" <td>1624</td>\n",
" <td>53.216374</td>\n",
" <td>244203.0</td>\n",
" <td>229456.0</td>\n",
" <td>14747.0</td>\n",
" <td>36231.0</td>\n",
" <td>6.038828</td>\n",
" <td>14.836427</td>\n",
" <td>0.665020</td>\n",
" <td>659.231690</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 소계 최근증가율 인구수 한국인 외국인 고령자 외국인비율 \\\n",
"구별 \n",
"강남구 2780 150.619195 570500.0 565550.0 4950.0 63167.0 0.867660 \n",
"송파구 618 104.347826 667483.0 660584.0 6899.0 72506.0 1.033584 \n",
"양천구 2034 34.671731 479978.0 475949.0 4029.0 52975.0 0.839413 \n",
"서초구 1930 63.371266 450310.0 445994.0 4316.0 51733.0 0.958451 \n",
"용산구 1624 53.216374 244203.0 229456.0 14747.0 36231.0 6.038828 \n",
"\n",
" 고령자비율 CCTV비율 오차 \n",
"구별 \n",
"강남구 11.072217 0.487292 1388.055355 \n",
"송파구 10.862599 0.092587 900.911312 \n",
"양천구 11.036964 0.423769 760.563512 \n",
"서초구 11.488308 0.428594 695.403794 \n",
"용산구 14.836427 0.665020 659.231690 "
]
},
"execution_count": 57,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"fp1 = np.polyfit(data_result['인구수'], data_result['소계'], 1)\n",
"\n",
"f1 = np.poly1d(fp1)\n",
"fx = np.linspace(100000, 700000, 100)\n",
"\n",
"# '오차'를 구하려면, '소계'에서 '인구수' 값을 f1(1차식)에 대입한 값을 빼줘야 함\n",
"data_result['오차'] = np.abs(data_result['소계'] - f1(data_result['인구수']))\n",
"\n",
"df_sort = data_result.sort_values(by = '오차', ascending = False)\n",
"df_sort.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**위 코드에서 f1(data_result['인구수']) 에 대한 추가 설명**"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-25T06:32:26.444340Z",
"start_time": "2020-05-25T06:32:26.436362Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"array([1391.94464487, 1238.42289235, 1077.34202601, 1435.50315451,\n",
" 1333.05189553, 1132.29026377, 1231.40708166, 979.01070661,\n",
" 1390.48361768, 1101.50134126, 1128.79741381, 1185.12289226,\n",
" 1155.18099895, 1073.3765678 , 1234.59620553, 1052.53598367,\n",
" 1248.93155299, 1518.91131175, 1273.43648757, 1172.64001207,\n",
" 964.76830981, 1292.30154299, 858.22460369, 819.4995281 ,\n",
" 1187.71896477])"
]
},
"execution_count": 58,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"f1(data_result['인구수'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**텍스트('구'의 이름)와 color map을 입히기**\n",
"\n",
"<참고> plt.text 에 대한 설명이 있는 사이트: https://m.blog.naver.com/PostView.nhn?blogId=infoefficien&logNo=221057152472&proxyReferer=https:%2F%2Fwww.google.co.kr%2F"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-25T06:32:27.646545Z",
"start_time": "2020-05-25T06:32:27.319419Z"
},
"scrolled": true
},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 1008x720 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize = (14, 10))\n",
"plt.scatter(data_result['인구수'], data_result['소계'], \n",
" c = data_result['오차'], s = 50)\n",
"plt.plot(fx, f1(fx), ls = 'dashed', lw = 3, color = 'g')\n",
"\n",
"for n in range(10):\n",
" # text 함수에서 text(x축 좌표, y축 좌표, 문자열)을 입력하여, 좌표에 comment를 추가\n",
" plt.text(df_sort['인구수'][n] * 1.02, df_sort['소계'][n] * 0.98,\n",
" df_sort.index[n], fontsize = 15)\n",
" \n",
"plt.xlabel('인구수')\n",
"plt.ylabel('인구당비율')\n",
"plt.colorbar()\n",
"plt.grid()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**<최종 시각화 자료 해석>**\n",
"- 그려진 직선을 기준으로 위에 있는 '강남구', '양천구', '서초구', '은평구', '용산구'는 서울시 일대에서 CCTV가 많이 설치된 지역이다\n",
"- 그려진 직선을 기준으로 아래에 있는 '송파구', '강서구', '중랑구', '마포구', '도봉구'는 서울시 일대에서 CCTV가 적게 설치된 지역이다\n",
"- 특히 주의깊게 살펴볼 점은 '강남구'는 다른 구들에 비해 월등히 많은 CCTV가 설치되었지만, '송파구'의 경우에는 많은 인구 수에 비해 CCTV가 굉장히 적게 설치되어 있다\n",
"- '송파구'에 CCTV가 적게 설치되어 있는 특별한 이유라도 있는 것일까? 그것이 알고 싶다..."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.6"
},
"toc": {
"base_numbering": 1,
"nav_menu": {},
"number_sections": true,
"sideBar": true,
"skip_h1_title": false,
"title_cell": "Table of Contents",
"title_sidebar": "Contents",
"toc_cell": false,
"toc_position": {},
"toc_section_display": true,
"toc_window_display": false
}
},
"nbformat": 4,
"nbformat_minor": 4
}
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