서울시 구별 CCTV 현황 분석.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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",
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       "      <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",
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       "    <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",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>강북구</td>\n",
       "      <td>748</td>\n",
       "      <td>369</td>\n",
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       "      <td>138</td>\n",
       "      <td>204</td>\n",
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       "    <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",
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       "    <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",
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      "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": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['기관명', '소계', '2013년도 이전', '2014년', '2015년', '2016년'], dtype='object')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CCTV_Seoul.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "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": {},
   "outputs": [
    {
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       "      <td>932</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>강동구</td>\n",
       "      <td>773</td>\n",
       "      <td>379</td>\n",
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       "      <td>155</td>\n",
       "      <td>377</td>\n",
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       "      <td>138</td>\n",
       "      <td>204</td>\n",
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       "    <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",
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      "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": [
    "CCTV_Seoul.rename(columns={CCTV_Seoul.columns[0] : '구별'}, inplace=True)\n",
    "CCTV_Seoul.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "      <th>기간</th>\n",
       "      <th>자치구</th>\n",
       "      <th>세대</th>\n",
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       "      <th>1</th>\n",
       "      <td>기간</td>\n",
       "      <td>자치구</td>\n",
       "      <td>세대</td>\n",
       "      <td>계</td>\n",
       "      <td>남자</td>\n",
       "      <td>여자</td>\n",
       "      <td>계</td>\n",
       "      <td>남자</td>\n",
       "      <td>여자</td>\n",
       "      <td>계</td>\n",
       "      <td>남자</td>\n",
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       "      <td>65세이상고령자</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017.1/4</td>\n",
       "      <td>합계</td>\n",
       "      <td>4202888</td>\n",
       "      <td>10197604</td>\n",
       "      <td>5000005</td>\n",
       "      <td>5197599</td>\n",
       "      <td>9926968</td>\n",
       "      <td>4871560</td>\n",
       "      <td>5055408</td>\n",
       "      <td>270636</td>\n",
       "      <td>128445</td>\n",
       "      <td>142191</td>\n",
       "      <td>2.36</td>\n",
       "      <td>1321458</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017.1/4</td>\n",
       "      <td>종로구</td>\n",
       "      <td>72654</td>\n",
       "      <td>162820</td>\n",
       "      <td>79675</td>\n",
       "      <td>83145</td>\n",
       "      <td>153589</td>\n",
       "      <td>75611</td>\n",
       "      <td>77978</td>\n",
       "      <td>9231</td>\n",
       "      <td>4064</td>\n",
       "      <td>5167</td>\n",
       "      <td>2.11</td>\n",
       "      <td>25425</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017.1/4</td>\n",
       "      <td>중구</td>\n",
       "      <td>59481</td>\n",
       "      <td>133240</td>\n",
       "      <td>65790</td>\n",
       "      <td>67450</td>\n",
       "      <td>124312</td>\n",
       "      <td>61656</td>\n",
       "      <td>62656</td>\n",
       "      <td>8928</td>\n",
       "      <td>4134</td>\n",
       "      <td>4794</td>\n",
       "      <td>2.09</td>\n",
       "      <td>20764</td>\n",
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      "text/plain": [
       "         기간  자치구       세대        인구     인구.1     인구.2     인구.3     인구.4  \\\n",
       "0        기간  자치구       세대        합계       합계       합계      한국인      한국인   \n",
       "1        기간  자치구       세대         계       남자       여자        계       남자   \n",
       "2  2017.1/4   합계  4202888  10197604  5000005  5197599  9926968  4871560   \n",
       "3  2017.1/4  종로구    72654    162820    79675    83145   153589    75611   \n",
       "4  2017.1/4   중구    59481    133240    65790    67450   124312    61656   \n",
       "\n",
       "      인구.5    인구.6    인구.7    인구.8  세대당인구  65세이상고령자  \n",
       "0      한국인   등록외국인   등록외국인   등록외국인  세대당인구  65세이상고령자  \n",
       "1       여자       계      남자      여자  세대당인구  65세이상고령자  \n",
       "2  5055408  270636  128445  142191   2.36   1321458  \n",
       "3    77978    9231    4064    5167   2.11     25425  \n",
       "4    62656    8928    4134    4794   2.09     20764  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pop_Seoul=pd.read_excel('./data/01. population_in_Seoul.xls', encoding='utf-8')\n",
    "pop_Seoul.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "      <th>0</th>\n",
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       "      <td>10197604.0</td>\n",
       "      <td>9926968.0</td>\n",
       "      <td>270636.0</td>\n",
       "      <td>1321458.0</td>\n",
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       "      <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",
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       "      <th>3</th>\n",
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       "      <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",
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      "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": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pop_Seoul=pd.read_excel('./data/01. population_in_Seoul.xls',\n",
    "                       header=2,\n",
    "                       usecols = 'B,D,G,J,N',\n",
    "                       encoding='utf-8')\n",
    "pop_Seoul.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
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       "      <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": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "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'}, inplace=True)\n",
    "pop_Seoul.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CCTV_Seoul.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "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",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\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>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": [
    "CCTV_Seoul.sort_values(by='소계', ascending=False).head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": [
    "CCTV_Seoul['최근증가율']=(CCTV_Seoul['2016년']+CCTV_Seoul['2015년']+CCTV_Seoul['2014년'])/CCTV_Seoul['2013년도 이전']*100\n",
    "CCTV_Seoul.sort_values(by='최근증가율', ascending=False).head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <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": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "    <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": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pop_Seoul.drop([0], inplace=True)\n",
    "pop_Seoul.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "    <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": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pop_Seoul[pop_Seoul['구별'].isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pop_Seoul.drop([26], inplace=True)\n",
    "pop_Seoul.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
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       "        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": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "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": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\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>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": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pop_Seoul.sort_values(by='인구수', ascending=False).head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pop_Seoul.sort_values(by='외국인', ascending=False).head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe thead th {\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",
       "      <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": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pop_Seoul.sort_values(by='외국인비율', ascending=False).head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "        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": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pop_Seoul.sort_values(by='고령자', ascending=False).head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "    <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": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pop_Seoul.sort_values(by='고령자비율', ascending=False).head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <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": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_result=pd.merge(CCTV_Seoul, pop_Seoul, on='구별')\n",
    "data_result.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>소계</th>\n",
       "      <th>최근증가율</th>\n",
       "      <th>인구수</th>\n",
       "      <th>한국인</th>\n",
       "      <th>외국인</th>\n",
       "      <th>고령자</th>\n",
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       "      <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": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "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": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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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>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": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_result.set_index('구별', inplace=True)\n",
    "data_result.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.        , -0.28078554],\n",
       "       [-0.28078554,  1.        ]])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#무슨 데이터와 CCTV를 비교할지 정하기 위해 상관계수 조사함\n",
    "import numpy as np\n",
    "np.corrcoef(data_result['고령자비율'], data_result['소계'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.        , -0.13607433],\n",
       "       [-0.13607433,  1.        ]])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.corrcoef(data_result['외국인비율'], data_result['소계'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.        , 0.30634228],\n",
       "       [0.30634228, 1.        ]])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.corrcoef(data_result['인구수'], data_result['소계'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <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",
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       "    <tr>\n",
       "      <th>구별</th>\n",
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       "      <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>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": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_result.sort_values(by='소계', ascending=False).head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
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       "  <tbody>\n",
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       "      <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",
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       "    <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",
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       "      <td>12.015794</td>\n",
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       "    <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": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_result.sort_values(by='인구수', ascending=False).head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: matplotlib in c:\\users\\user\\appdata\\local\\programs\\python\\python38-32\\lib\\site-packages (3.2.1)\n",
      "Requirement already satisfied: python-dateutil>=2.1 in c:\\users\\user\\appdata\\local\\programs\\python\\python38-32\\lib\\site-packages (from matplotlib) (2.8.1)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in c:\\users\\user\\appdata\\local\\programs\\python\\python38-32\\lib\\site-packages (from matplotlib) (1.2.0)\n",
      "Requirement already satisfied: numpy>=1.11 in c:\\users\\user\\appdata\\local\\programs\\python\\python38-32\\lib\\site-packages (from matplotlib) (1.18.1)\n",
      "Requirement already satisfied: cycler>=0.10 in c:\\users\\user\\appdata\\local\\programs\\python\\python38-32\\lib\\site-packages (from matplotlib) (0.10.0)\n",
      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in c:\\users\\user\\appdata\\local\\programs\\python\\python38-32\\lib\\site-packages (from matplotlib) (2.4.7)\n",
      "Requirement already satisfied: six>=1.5 in c:\\users\\user\\appdata\\local\\programs\\python\\python38-32\\lib\\site-packages (from python-dateutil>=2.1->matplotlib) (1.14.0)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: You are using pip version 19.2.3, however version 20.0.2 is available.\n",
      "You should consider upgrading via the 'python -m pip install --upgrade pip' command.\n"
     ]
    }
   ],
   "source": [
    "pip install matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import platform\n",
    "from matplotlib import font_manager, rc\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "\n",
    "if platform.system() == 'Darwin':\n",
    "    rc('font', family='AppleGothinc')\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')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_result.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "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'].plot(kind='barh', grid=True, figsize=(10,10))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "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": "code",
   "execution_count": 35,
   "metadata": {},
   "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['CCTV비율']= data_result['소계']/data_result['인구수']*100\n",
    "data_result['CCTV비율'].sort_values().plot(kind='barh', grid=True, figsize=(10,10))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "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)\n",
    "plt.xlabel('인구수')\n",
    "plt.ylabel('CCTV')\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.30916415e-03, 6.45066497e+02])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fp1 = np.polyfit(data_result['인구수'], data_result['소계'], 1)\n",
    "fp1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "f1 = np.poly1d(fp1)\n",
    "fx = np.linspace(10000, 700000, 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "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": [
    "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')\n",
    "plt.xlabel('인구수')\n",
    "plt.ylabel('CCTV')\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fp1 = np.polyfit(data_result['인구수'], data_result['소계'],1)\n",
    "f1 = np.poly1d(fp1)\n",
    "fx = np.linspace(100000, 700000, 100)\n",
    "\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": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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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",
    "    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",
    "\n",
    "plt.colorbar()\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  }
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