hypothesis_test_economic.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "plt.rcParams['font.sans-serif']=['AppleGothic']\n",
    "plt.rcParams['axes.unicode_minus'] = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 병원 - 영업상태명에서 휴업, 폐업인 병원 제거\n",
    "# 좌표 x, y 가져와서 구글맵에 찍어\n",
    "# 의원 - 상세영업상태명에서 영업중만 남기기\n",
    "# 진료과목내용명에서 소아청소년과만 남기기\n",
    "# 의원수 신경 안쓰기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
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       "      <th>행정구역(시군구)별(1)</th>\n",
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       "      <th>0</th>\n",
       "      <td>전국</td>\n",
       "      <td>소계</td>\n",
       "      <td>19959148</td>\n",
       "      <td>807198885</td>\n",
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       "      <td>17857818</td>\n",
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       "      <td>52698621</td>\n",
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       "      <th>1</th>\n",
       "      <td>서울</td>\n",
       "      <td>소계</td>\n",
       "      <td>3952583</td>\n",
       "      <td>184858796</td>\n",
       "      <td>3944244</td>\n",
       "      <td>184066506</td>\n",
       "      <td>3537573</td>\n",
       "      <td>113851288</td>\n",
       "      <td>2565528</td>\n",
       "      <td>17333494</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>서울</td>\n",
       "      <td>강남구</td>\n",
       "      <td>215632</td>\n",
       "      <td>17477665</td>\n",
       "      <td>215441</td>\n",
       "      <td>17417230</td>\n",
       "      <td>197339</td>\n",
       "      <td>12781199</td>\n",
       "      <td>156295</td>\n",
       "      <td>2932330</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>서울</td>\n",
       "      <td>강동구</td>\n",
       "      <td>192080</td>\n",
       "      <td>8488374</td>\n",
       "      <td>191800</td>\n",
       "      <td>8449977</td>\n",
       "      <td>172314</td>\n",
       "      <td>5005361</td>\n",
       "      <td>126135</td>\n",
       "      <td>630369</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>서울</td>\n",
       "      <td>강북구</td>\n",
       "      <td>105489</td>\n",
       "      <td>3274497</td>\n",
       "      <td>105266</td>\n",
       "      <td>3259552</td>\n",
       "      <td>91807</td>\n",
       "      <td>1661978</td>\n",
       "      <td>61474</td>\n",
       "      <td>135266</td>\n",
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       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>242</th>\n",
       "      <td>경남</td>\n",
       "      <td>합천군</td>\n",
       "      <td>9838</td>\n",
       "      <td>317420</td>\n",
       "      <td>9816</td>\n",
       "      <td>316507</td>\n",
       "      <td>8846</td>\n",
       "      <td>162244</td>\n",
       "      <td>6086</td>\n",
       "      <td>11417</td>\n",
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       "    <tr>\n",
       "      <th>243</th>\n",
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       "      <td>소계</td>\n",
       "      <td>232571</td>\n",
       "      <td>7985643</td>\n",
       "      <td>232345</td>\n",
       "      <td>7951570</td>\n",
       "      <td>207655</td>\n",
       "      <td>4141826</td>\n",
       "      <td>142908</td>\n",
       "      <td>375971</td>\n",
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       "    <tr>\n",
       "      <th>244</th>\n",
       "      <td>제주</td>\n",
       "      <td>제주시</td>\n",
       "      <td>175810</td>\n",
       "      <td>6064037</td>\n",
       "      <td>175652</td>\n",
       "      <td>6039377</td>\n",
       "      <td>157106</td>\n",
       "      <td>3132000</td>\n",
       "      <td>107941</td>\n",
       "      <td>277434</td>\n",
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       "    <tr>\n",
       "      <th>245</th>\n",
       "      <td>제주</td>\n",
       "      <td>서귀포시</td>\n",
       "      <td>56761</td>\n",
       "      <td>1921607</td>\n",
       "      <td>56693</td>\n",
       "      <td>1912193</td>\n",
       "      <td>50549</td>\n",
       "      <td>1009826</td>\n",
       "      <td>34967</td>\n",
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       "      <td>4817</td>\n",
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       "      <td>2817</td>\n",
       "      <td>40642</td>\n",
       "      <td>1546</td>\n",
       "      <td>22779</td>\n",
       "      <td>856</td>\n",
       "      <td>2222</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "<p>247 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    행정구역(시군구)별(1) 행정구역(시군구)별(2)    인원 (명)   금액 (백만원)  인원 (명).1  금액 (백만원).1  \\\n",
       "0              전국            소계  19959148  807198885  19907727   803208612   \n",
       "1              서울            소계   3952583  184858796   3944244   184066506   \n",
       "2              서울           강남구    215632   17477665    215441    17417230   \n",
       "3              서울           강동구    192080    8488374    191800     8449977   \n",
       "4              서울           강북구    105489    3274497    105266     3259552   \n",
       "..            ...           ...       ...        ...       ...         ...   \n",
       "242            경남           합천군      9838     317420      9816      316507   \n",
       "243            제주            소계    232571    7985643    232345     7951570   \n",
       "244            제주           제주시    175810    6064037    175652     6039377   \n",
       "245            제주          서귀포시     56761    1921607     56693     1912193   \n",
       "246            기타            소계      4817      73210      2817       40642   \n",
       "\n",
       "     인원 (명).2  금액 (백만원).2  인원 (명).3  금액 (백만원).3  \n",
       "0    17857818   455957519  12919174    52698621  \n",
       "1     3537573   113851288   2565528    17333494  \n",
       "2      197339    12781199    156295     2932330  \n",
       "3      172314     5005361    126135      630369  \n",
       "4       91807     1661978     61474      135266  \n",
       "..        ...         ...       ...         ...  \n",
       "242      8846      162244      6086       11417  \n",
       "243    207655     4141826    142908      375971  \n",
       "244    157106     3132000    107941      277434  \n",
       "245     50549     1009826     34967       98537  \n",
       "246      1546       22779       856        2222  \n",
       "\n",
       "[247 rows x 10 columns]"
      ]
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   ],
   "source": [
    "# 시·군·구별_근로소득_연말정산_신고현황.csv\n",
    "\n",
    "income = pd.read_csv('4.2.15_시·군·구별_근로소득_연말정산_신고현황_주소지_2016_20231123093654.csv', encoding='UTF-8', skiprows=2)\n",
    "income"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>19959148</td>\n",
       "      <td>807198885</td>\n",
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       "      <th>1</th>\n",
       "      <td>서울</td>\n",
       "      <td>소계</td>\n",
       "      <td>3952583</td>\n",
       "      <td>184858796</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>서울</td>\n",
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       "      <td>서울</td>\n",
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       "      <td>8488374</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>서울</td>\n",
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       "      <td>105489</td>\n",
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       "      <th>242</th>\n",
       "      <td>경남</td>\n",
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       "      <td>9838</td>\n",
       "      <td>317420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>243</th>\n",
       "      <td>제주</td>\n",
       "      <td>소계</td>\n",
       "      <td>232571</td>\n",
       "      <td>7985643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>244</th>\n",
       "      <td>제주</td>\n",
       "      <td>제주시</td>\n",
       "      <td>175810</td>\n",
       "      <td>6064037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245</th>\n",
       "      <td>제주</td>\n",
       "      <td>서귀포시</td>\n",
       "      <td>56761</td>\n",
       "      <td>1921607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246</th>\n",
       "      <td>기타</td>\n",
       "      <td>소계</td>\n",
       "      <td>4817</td>\n",
       "      <td>73210</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>247 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    시/도   자치구     인원(명)  총 급여(백만원)\n",
       "0    전국    소계  19959148  807198885\n",
       "1    서울    소계   3952583  184858796\n",
       "2    서울   강남구    215632   17477665\n",
       "3    서울   강동구    192080    8488374\n",
       "4    서울   강북구    105489    3274497\n",
       "..   ..   ...       ...        ...\n",
       "242  경남   합천군      9838     317420\n",
       "243  제주    소계    232571    7985643\n",
       "244  제주   제주시    175810    6064037\n",
       "245  제주  서귀포시     56761    1921607\n",
       "246  기타    소계      4817      73210\n",
       "\n",
       "[247 rows x 4 columns]"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "# 행정구역(시군구)별(1), 행정구역(시군구)별(2), 급여 총계 인원(명), 급여 총계 금액(백만원)만 남기기\n",
    "income:pd.DataFrame = income.iloc[:, :4]\n",
    "income.columns = ['시/도', '자치구', '인원(명)', '총 급여(백만원)']\n",
    "income"
   ]
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  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "25\n"
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       "      <th>0</th>\n",
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       "      <td>17477665</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>서울</td>\n",
       "      <td>강동구</td>\n",
       "      <td>192080</td>\n",
       "      <td>8488374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>서울</td>\n",
       "      <td>강북구</td>\n",
       "      <td>105489</td>\n",
       "      <td>3274497</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>서울</td>\n",
       "      <td>강서구</td>\n",
       "      <td>252396</td>\n",
       "      <td>9964209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>서울</td>\n",
       "      <td>관악구</td>\n",
       "      <td>224352</td>\n",
       "      <td>7550177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>서울</td>\n",
       "      <td>광진구</td>\n",
       "      <td>150149</td>\n",
       "      <td>6121949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>서울</td>\n",
       "      <td>구로구</td>\n",
       "      <td>174004</td>\n",
       "      <td>6504808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>서울</td>\n",
       "      <td>금천구</td>\n",
       "      <td>104225</td>\n",
       "      <td>3388533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>서울</td>\n",
       "      <td>노원구</td>\n",
       "      <td>194250</td>\n",
       "      <td>7784253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>서울</td>\n",
       "      <td>도봉구</td>\n",
       "      <td>118133</td>\n",
       "      <td>4009934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>서울</td>\n",
       "      <td>동대문구</td>\n",
       "      <td>129684</td>\n",
       "      <td>4913041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>서울</td>\n",
       "      <td>동작구</td>\n",
       "      <td>167538</td>\n",
       "      <td>7403320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>서울</td>\n",
       "      <td>마포구</td>\n",
       "      <td>160890</td>\n",
       "      <td>8245601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>서울</td>\n",
       "      <td>서대문구</td>\n",
       "      <td>124643</td>\n",
       "      <td>5565047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>서울</td>\n",
       "      <td>서초구</td>\n",
       "      <td>169405</td>\n",
       "      <td>13638923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>서울</td>\n",
       "      <td>성동구</td>\n",
       "      <td>118248</td>\n",
       "      <td>6207800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>서울</td>\n",
       "      <td>성북구</td>\n",
       "      <td>162378</td>\n",
       "      <td>6852822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>서울</td>\n",
       "      <td>송파구</td>\n",
       "      <td>288952</td>\n",
       "      <td>16095685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>서울</td>\n",
       "      <td>양천구</td>\n",
       "      <td>178304</td>\n",
       "      <td>8806702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>서울</td>\n",
       "      <td>영등포구</td>\n",
       "      <td>180393</td>\n",
       "      <td>8559698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>서울</td>\n",
       "      <td>용산구</td>\n",
       "      <td>96680</td>\n",
       "      <td>6747813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>서울</td>\n",
       "      <td>은평구</td>\n",
       "      <td>190443</td>\n",
       "      <td>7030234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>서울</td>\n",
       "      <td>종로구</td>\n",
       "      <td>54388</td>\n",
       "      <td>2840850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>서울</td>\n",
       "      <td>중구</td>\n",
       "      <td>49447</td>\n",
       "      <td>2447593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>서울</td>\n",
       "      <td>중랑구</td>\n",
       "      <td>150480</td>\n",
       "      <td>4939270</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   시/도   자치구   인원(명)  총 급여(백만원)\n",
       "0   서울   강남구  215632   17477665\n",
       "1   서울   강동구  192080    8488374\n",
       "2   서울   강북구  105489    3274497\n",
       "3   서울   강서구  252396    9964209\n",
       "4   서울   관악구  224352    7550177\n",
       "5   서울   광진구  150149    6121949\n",
       "6   서울   구로구  174004    6504808\n",
       "7   서울   금천구  104225    3388533\n",
       "8   서울   노원구  194250    7784253\n",
       "9   서울   도봉구  118133    4009934\n",
       "10  서울  동대문구  129684    4913041\n",
       "11  서울   동작구  167538    7403320\n",
       "12  서울   마포구  160890    8245601\n",
       "13  서울  서대문구  124643    5565047\n",
       "14  서울   서초구  169405   13638923\n",
       "15  서울   성동구  118248    6207800\n",
       "16  서울   성북구  162378    6852822\n",
       "17  서울   송파구  288952   16095685\n",
       "18  서울   양천구  178304    8806702\n",
       "19  서울  영등포구  180393    8559698\n",
       "20  서울   용산구   96680    6747813\n",
       "21  서울   은평구  190443    7030234\n",
       "22  서울   종로구   54388    2840850\n",
       "23  서울    중구   49447    2447593\n",
       "24  서울   중랑구  150480    4939270"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 시군구1에서 서울만 남기기\n",
    "income = income.loc[(income['시/도'] == '서울') & (income['자치구'] != '소계')].reset_index(drop=True)\n",
    "print(income['자치구'].unique().size)\n",
    "income"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "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>시/도</th>\n",
       "      <th>자치구</th>\n",
       "      <th>인원(명)</th>\n",
       "      <th>총 급여(백만원)</th>\n",
       "      <th>1인당 총 급여</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>서울</td>\n",
       "      <td>강남구</td>\n",
       "      <td>215632</td>\n",
       "      <td>17477665</td>\n",
       "      <td>81053206.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>서울</td>\n",
       "      <td>강동구</td>\n",
       "      <td>192080</td>\n",
       "      <td>8488374</td>\n",
       "      <td>44191868.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>서울</td>\n",
       "      <td>강북구</td>\n",
       "      <td>105489</td>\n",
       "      <td>3274497</td>\n",
       "      <td>31041123.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>서울</td>\n",
       "      <td>강서구</td>\n",
       "      <td>252396</td>\n",
       "      <td>9964209</td>\n",
       "      <td>39478474.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>서울</td>\n",
       "      <td>관악구</td>\n",
       "      <td>224352</td>\n",
       "      <td>7550177</td>\n",
       "      <td>33653264.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>서울</td>\n",
       "      <td>광진구</td>\n",
       "      <td>150149</td>\n",
       "      <td>6121949</td>\n",
       "      <td>40772493.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>서울</td>\n",
       "      <td>구로구</td>\n",
       "      <td>174004</td>\n",
       "      <td>6504808</td>\n",
       "      <td>37383095.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>서울</td>\n",
       "      <td>금천구</td>\n",
       "      <td>104225</td>\n",
       "      <td>3388533</td>\n",
       "      <td>32511710.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>서울</td>\n",
       "      <td>노원구</td>\n",
       "      <td>194250</td>\n",
       "      <td>7784253</td>\n",
       "      <td>40073375.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>서울</td>\n",
       "      <td>도봉구</td>\n",
       "      <td>118133</td>\n",
       "      <td>4009934</td>\n",
       "      <td>33944232.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>서울</td>\n",
       "      <td>동대문구</td>\n",
       "      <td>129684</td>\n",
       "      <td>4913041</td>\n",
       "      <td>37884712.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>서울</td>\n",
       "      <td>동작구</td>\n",
       "      <td>167538</td>\n",
       "      <td>7403320</td>\n",
       "      <td>44188900.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>서울</td>\n",
       "      <td>마포구</td>\n",
       "      <td>160890</td>\n",
       "      <td>8245601</td>\n",
       "      <td>51249929.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>서울</td>\n",
       "      <td>서대문구</td>\n",
       "      <td>124643</td>\n",
       "      <td>5565047</td>\n",
       "      <td>44647890.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>서울</td>\n",
       "      <td>서초구</td>\n",
       "      <td>169405</td>\n",
       "      <td>13638923</td>\n",
       "      <td>80510746.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>서울</td>\n",
       "      <td>성동구</td>\n",
       "      <td>118248</td>\n",
       "      <td>6207800</td>\n",
       "      <td>52498140.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>서울</td>\n",
       "      <td>성북구</td>\n",
       "      <td>162378</td>\n",
       "      <td>6852822</td>\n",
       "      <td>42202897.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>서울</td>\n",
       "      <td>송파구</td>\n",
       "      <td>288952</td>\n",
       "      <td>16095685</td>\n",
       "      <td>55703664.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>서울</td>\n",
       "      <td>양천구</td>\n",
       "      <td>178304</td>\n",
       "      <td>8806702</td>\n",
       "      <td>49391500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>서울</td>\n",
       "      <td>영등포구</td>\n",
       "      <td>180393</td>\n",
       "      <td>8559698</td>\n",
       "      <td>47450278.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>서울</td>\n",
       "      <td>용산구</td>\n",
       "      <td>96680</td>\n",
       "      <td>6747813</td>\n",
       "      <td>69795335.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>서울</td>\n",
       "      <td>은평구</td>\n",
       "      <td>190443</td>\n",
       "      <td>7030234</td>\n",
       "      <td>36915161.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>서울</td>\n",
       "      <td>종로구</td>\n",
       "      <td>54388</td>\n",
       "      <td>2840850</td>\n",
       "      <td>52233029.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>서울</td>\n",
       "      <td>중구</td>\n",
       "      <td>49447</td>\n",
       "      <td>2447593</td>\n",
       "      <td>49499323.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>서울</td>\n",
       "      <td>중랑구</td>\n",
       "      <td>150480</td>\n",
       "      <td>4939270</td>\n",
       "      <td>32823432.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   시/도   자치구   인원(명)  총 급여(백만원)    1인당 총 급여\n",
       "0   서울   강남구  215632   17477665  81053206.0\n",
       "1   서울   강동구  192080    8488374  44191868.0\n",
       "2   서울   강북구  105489    3274497  31041123.0\n",
       "3   서울   강서구  252396    9964209  39478474.0\n",
       "4   서울   관악구  224352    7550177  33653264.0\n",
       "5   서울   광진구  150149    6121949  40772493.0\n",
       "6   서울   구로구  174004    6504808  37383095.0\n",
       "7   서울   금천구  104225    3388533  32511710.0\n",
       "8   서울   노원구  194250    7784253  40073375.0\n",
       "9   서울   도봉구  118133    4009934  33944232.0\n",
       "10  서울  동대문구  129684    4913041  37884712.0\n",
       "11  서울   동작구  167538    7403320  44188900.0\n",
       "12  서울   마포구  160890    8245601  51249929.0\n",
       "13  서울  서대문구  124643    5565047  44647890.0\n",
       "14  서울   서초구  169405   13638923  80510746.0\n",
       "15  서울   성동구  118248    6207800  52498140.0\n",
       "16  서울   성북구  162378    6852822  42202897.0\n",
       "17  서울   송파구  288952   16095685  55703664.0\n",
       "18  서울   양천구  178304    8806702  49391500.0\n",
       "19  서울  영등포구  180393    8559698  47450278.0\n",
       "20  서울   용산구   96680    6747813  69795335.0\n",
       "21  서울   은평구  190443    7030234  36915161.0\n",
       "22  서울   종로구   54388    2840850  52233029.0\n",
       "23  서울    중구   49447    2447593  49499323.0\n",
       "24  서울   중랑구  150480    4939270  32823432.0"
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1인당 총 급여 구하기 (1년 총 급여)\n",
    "income['1인당 총 급여'] = np.round(income['총 급여(백만원)'] * 1000000 / income['인원(명)'])\n",
    "income"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "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>시/도</th>\n",
       "      <th>자치구</th>\n",
       "      <th>인원(명)</th>\n",
       "      <th>총 급여(백만원)</th>\n",
       "      <th>1인당 총 급여</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>서울</td>\n",
       "      <td>강남구</td>\n",
       "      <td>215632</td>\n",
       "      <td>17477665</td>\n",
       "      <td>81053206.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>서울</td>\n",
       "      <td>서초구</td>\n",
       "      <td>169405</td>\n",
       "      <td>13638923</td>\n",
       "      <td>80510746.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>서울</td>\n",
       "      <td>용산구</td>\n",
       "      <td>96680</td>\n",
       "      <td>6747813</td>\n",
       "      <td>69795335.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>서울</td>\n",
       "      <td>송파구</td>\n",
       "      <td>288952</td>\n",
       "      <td>16095685</td>\n",
       "      <td>55703664.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>서울</td>\n",
       "      <td>성동구</td>\n",
       "      <td>118248</td>\n",
       "      <td>6207800</td>\n",
       "      <td>52498140.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>서울</td>\n",
       "      <td>종로구</td>\n",
       "      <td>54388</td>\n",
       "      <td>2840850</td>\n",
       "      <td>52233029.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>서울</td>\n",
       "      <td>마포구</td>\n",
       "      <td>160890</td>\n",
       "      <td>8245601</td>\n",
       "      <td>51249929.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>서울</td>\n",
       "      <td>중구</td>\n",
       "      <td>49447</td>\n",
       "      <td>2447593</td>\n",
       "      <td>49499323.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>서울</td>\n",
       "      <td>양천구</td>\n",
       "      <td>178304</td>\n",
       "      <td>8806702</td>\n",
       "      <td>49391500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>서울</td>\n",
       "      <td>영등포구</td>\n",
       "      <td>180393</td>\n",
       "      <td>8559698</td>\n",
       "      <td>47450278.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>서울</td>\n",
       "      <td>서대문구</td>\n",
       "      <td>124643</td>\n",
       "      <td>5565047</td>\n",
       "      <td>44647890.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>서울</td>\n",
       "      <td>강동구</td>\n",
       "      <td>192080</td>\n",
       "      <td>8488374</td>\n",
       "      <td>44191868.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>서울</td>\n",
       "      <td>동작구</td>\n",
       "      <td>167538</td>\n",
       "      <td>7403320</td>\n",
       "      <td>44188900.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>서울</td>\n",
       "      <td>성북구</td>\n",
       "      <td>162378</td>\n",
       "      <td>6852822</td>\n",
       "      <td>42202897.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>서울</td>\n",
       "      <td>광진구</td>\n",
       "      <td>150149</td>\n",
       "      <td>6121949</td>\n",
       "      <td>40772493.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>서울</td>\n",
       "      <td>노원구</td>\n",
       "      <td>194250</td>\n",
       "      <td>7784253</td>\n",
       "      <td>40073375.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>서울</td>\n",
       "      <td>강서구</td>\n",
       "      <td>252396</td>\n",
       "      <td>9964209</td>\n",
       "      <td>39478474.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>서울</td>\n",
       "      <td>동대문구</td>\n",
       "      <td>129684</td>\n",
       "      <td>4913041</td>\n",
       "      <td>37884712.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>서울</td>\n",
       "      <td>구로구</td>\n",
       "      <td>174004</td>\n",
       "      <td>6504808</td>\n",
       "      <td>37383095.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>서울</td>\n",
       "      <td>은평구</td>\n",
       "      <td>190443</td>\n",
       "      <td>7030234</td>\n",
       "      <td>36915161.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>서울</td>\n",
       "      <td>도봉구</td>\n",
       "      <td>118133</td>\n",
       "      <td>4009934</td>\n",
       "      <td>33944232.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>서울</td>\n",
       "      <td>관악구</td>\n",
       "      <td>224352</td>\n",
       "      <td>7550177</td>\n",
       "      <td>33653264.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>서울</td>\n",
       "      <td>중랑구</td>\n",
       "      <td>150480</td>\n",
       "      <td>4939270</td>\n",
       "      <td>32823432.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>서울</td>\n",
       "      <td>금천구</td>\n",
       "      <td>104225</td>\n",
       "      <td>3388533</td>\n",
       "      <td>32511710.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>서울</td>\n",
       "      <td>강북구</td>\n",
       "      <td>105489</td>\n",
       "      <td>3274497</td>\n",
       "      <td>31041123.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   시/도   자치구   인원(명)  총 급여(백만원)    1인당 총 급여\n",
       "0   서울   강남구  215632   17477665  81053206.0\n",
       "1   서울   서초구  169405   13638923  80510746.0\n",
       "2   서울   용산구   96680    6747813  69795335.0\n",
       "3   서울   송파구  288952   16095685  55703664.0\n",
       "4   서울   성동구  118248    6207800  52498140.0\n",
       "5   서울   종로구   54388    2840850  52233029.0\n",
       "6   서울   마포구  160890    8245601  51249929.0\n",
       "7   서울    중구   49447    2447593  49499323.0\n",
       "8   서울   양천구  178304    8806702  49391500.0\n",
       "9   서울  영등포구  180393    8559698  47450278.0\n",
       "10  서울  서대문구  124643    5565047  44647890.0\n",
       "11  서울   강동구  192080    8488374  44191868.0\n",
       "12  서울   동작구  167538    7403320  44188900.0\n",
       "13  서울   성북구  162378    6852822  42202897.0\n",
       "14  서울   광진구  150149    6121949  40772493.0\n",
       "15  서울   노원구  194250    7784253  40073375.0\n",
       "16  서울   강서구  252396    9964209  39478474.0\n",
       "17  서울  동대문구  129684    4913041  37884712.0\n",
       "18  서울   구로구  174004    6504808  37383095.0\n",
       "19  서울   은평구  190443    7030234  36915161.0\n",
       "20  서울   도봉구  118133    4009934  33944232.0\n",
       "21  서울   관악구  224352    7550177  33653264.0\n",
       "22  서울   중랑구  150480    4939270  32823432.0\n",
       "23  서울   금천구  104225    3388533  32511710.0\n",
       "24  서울   강북구  105489    3274497  31041123.0"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 소계 제외하고 1인당 총 급여 높은 순으로 정렬\n",
    "income = income.sort_values(by='1인당 총 급여', ascending=False).reset_index(drop=True)\n",
    "income"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "의료기관종별명\n",
      "병원                 324\n",
      "요양병원(일반요양병원)       172\n",
      "한방병원               112\n",
      "치과병원               105\n",
      "종합병원                58\n",
      "요양병원(노인병원)           4\n",
      "요양병원(정신병원)           2\n",
      "요양병원(장애인의료재활시설)      1\n",
      "Name: count, dtype: int64 \n",
      "\n",
      "진료과목내용명 null 개수: 0 \n",
      "\n",
      "상세영업상태명\n",
      "영업중     123\n",
      "폐업       29\n",
      "휴업        1\n",
      "직권폐업      1\n",
      "Name: count, dtype: int64 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# 병원 데이터 전처리\n",
    "hospital = pd.read_csv('[필수활용01]서울시_병원정보.csv', encoding='EUC-KR')\n",
    "\n",
    "# 병원 종류 살펴보기\n",
    "print(hospital['의료기관종별명'].value_counts(), '\\n')\n",
    "\n",
    "# 진료과목 null값 확인\n",
    "print(\"진료과목내용명 null 개수:\", hospital['진료과목내용명'].isnull().sum(), '\\n')\n",
    "\n",
    "# 소아청소년과 병원 영업 상태 확인\n",
    "hospital = hospital[(hospital['의료기관종별명'].isin(['병원', '한방병원', '종합병원'])) & (hospital['진료과목내용명'].str.contains('소아청소년과'))]\n",
    "print(hospital['상세영업상태명'].value_counts(), '\\n')\n",
    "\n",
    "# 영업중인 소아청소년과 병원 정보만 남기기\n",
    "hospital = hospital[hospital['상세영업상태명'] == '영업중']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['중랑구' '영등포구' '서초구' '성동구' '광진구' '은평구' '강남구' '강서구' '노원구' '관악구' '종로구' '서대문구'\n",
      " '송파구' '마포구' '강동구' '동대문구' '강북구' '도봉구' '금천구' '양천구' '중구' '동작구' '구로구' '성북구'\n",
      " '용산구']\n",
      "True\n"
     ]
    }
   ],
   "source": [
    "# 병원 데이터의 시군구명 구하기\n",
    "print(hospital['도로명전체주소'].apply(lambda x:x.split(\" \")[1]).unique())\n",
    "print(len(set(hospital['도로명전체주소'].apply(lambda x:x.split(\" \")[1]).unique()) - set(income['자치구'].unique())) == 0)\n",
    "\n",
    "hospital['자치구'] = hospital['도로명전체주소'].apply(lambda x:x.split(\" \")[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "의료기관종별명\n",
       "병원      64\n",
       "종합병원    50\n",
       "한방병원     9\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hospital['의료기관종별명'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "자치구\n",
       "강남구     10\n",
       "노원구      9\n",
       "중랑구      8\n",
       "은평구      8\n",
       "강서구      8\n",
       "동대문구     7\n",
       "구로구      6\n",
       "종로구      6\n",
       "영등포구     6\n",
       "서초구      5\n",
       "광진구      5\n",
       "동작구      5\n",
       "양천구      5\n",
       "도봉구      5\n",
       "강동구      5\n",
       "송파구      4\n",
       "서대문구     4\n",
       "관악구      4\n",
       "중구       3\n",
       "성북구      3\n",
       "성동구      2\n",
       "용산구      2\n",
       "강북구      1\n",
       "마포구      1\n",
       "금천구      1\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 자치구별로 병원 개수 구하기\n",
    "hospital['자치구'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "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>자치구</th>\n",
       "      <th>1인당 총 급여</th>\n",
       "      <th>소아청소년과 병원 수</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>강남구</td>\n",
       "      <td>81053206.0</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>서초구</td>\n",
       "      <td>80510746.0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>용산구</td>\n",
       "      <td>69795335.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>송파구</td>\n",
       "      <td>55703664.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>성동구</td>\n",
       "      <td>52498140.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>종로구</td>\n",
       "      <td>52233029.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>마포구</td>\n",
       "      <td>51249929.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>중구</td>\n",
       "      <td>49499323.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>양천구</td>\n",
       "      <td>49391500.0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>영등포구</td>\n",
       "      <td>47450278.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>서대문구</td>\n",
       "      <td>44647890.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>강동구</td>\n",
       "      <td>44191868.0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>동작구</td>\n",
       "      <td>44188900.0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>성북구</td>\n",
       "      <td>42202897.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>광진구</td>\n",
       "      <td>40772493.0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>노원구</td>\n",
       "      <td>40073375.0</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>강서구</td>\n",
       "      <td>39478474.0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>동대문구</td>\n",
       "      <td>37884712.0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>구로구</td>\n",
       "      <td>37383095.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>은평구</td>\n",
       "      <td>36915161.0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>도봉구</td>\n",
       "      <td>33944232.0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>관악구</td>\n",
       "      <td>33653264.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>중랑구</td>\n",
       "      <td>32823432.0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>금천구</td>\n",
       "      <td>32511710.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>강북구</td>\n",
       "      <td>31041123.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     자치구    1인당 총 급여  소아청소년과 병원 수\n",
       "0    강남구  81053206.0           10\n",
       "1    서초구  80510746.0            5\n",
       "2    용산구  69795335.0            2\n",
       "3    송파구  55703664.0            4\n",
       "4    성동구  52498140.0            2\n",
       "5    종로구  52233029.0            6\n",
       "6    마포구  51249929.0            1\n",
       "7     중구  49499323.0            3\n",
       "8    양천구  49391500.0            5\n",
       "9   영등포구  47450278.0            6\n",
       "10  서대문구  44647890.0            4\n",
       "11   강동구  44191868.0            5\n",
       "12   동작구  44188900.0            5\n",
       "13   성북구  42202897.0            3\n",
       "14   광진구  40772493.0            5\n",
       "15   노원구  40073375.0            9\n",
       "16   강서구  39478474.0            8\n",
       "17  동대문구  37884712.0            7\n",
       "18   구로구  37383095.0            6\n",
       "19   은평구  36915161.0            8\n",
       "20   도봉구  33944232.0            5\n",
       "21   관악구  33653264.0            4\n",
       "22   중랑구  32823432.0            8\n",
       "23   금천구  32511710.0            1\n",
       "24   강북구  31041123.0            1"
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 소득 데이터와 병원 개수 데이터 합치기\n",
    "count_by_area = income[['자치구', '1인당 총 급여']].join(hospital['자치구'].value_counts(), on='자치구', how='left').rename(columns={'count':'소아청소년과 병원 수'})\n",
    "count_by_area"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 의원 데이터 전처리\n",
    "clinic = pd.read_csv('[필수활용02]서울시_의원정보.csv', encoding='cp949')\n",
    "\n",
    "# 영업중인 의원 정보만 남기기\n",
    "clinic = clinic[clinic['상세영업상태명'] == '영업중']\n",
    "\n",
    "# 보건소 보건지소 치과 제거 & 소아청소년과만 남기기\n",
    "clinic = clinic[(clinic['의료기관종별명'].isin(['의원', '한의원'])) & (clinic['진료과목내용명'].str.contains('소아청소년과'))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3047\n",
      "[]\n",
      "['구로구' '중랑구' '중구' '양천구' '용산구' '은평구' '서초구' '송파구' '성동구' '종로구' '영등포구' '강남구'\n",
      " '금천구' '마포구' '광진구' '서대문구' '성북구' '동대문구' '강동구' '강서구' '동작구' '도봉구' '강북구' '관악구'\n",
      " '노원구']\n",
      "True\n",
      "3047\n"
     ]
    }
   ],
   "source": [
    "# 의원 데이터의 시군구명 구하기\n",
    "print(len(clinic))\n",
    "\n",
    "# 주소가 이상하게 표기되어 있는 데이터 제거 \n",
    "print(clinic[~clinic['도로명전체주소'].apply(lambda x:x.split(\" \")[1]).isin(income['자치구'].unique())]['도로명전체주소'].values)\n",
    "clinic = clinic[clinic['도로명전체주소'].apply(lambda x:x.split(\" \")[1]).isin(income['자치구'].unique())]\n",
    "print(clinic['도로명전체주소'].apply(lambda x:x.split(\" \")[1]).unique())\n",
    "print(len(set(clinic['도로명전체주소'].apply(lambda x:x.split(\" \")[1]).unique()) - set(income['자치구'].unique())) == 0)\n",
    "\n",
    "clinic['자치구'] = clinic['도로명전체주소'].apply(lambda x:x.split(\" \")[1])\n",
    "\n",
    "print(len(clinic))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "의료기관종별명\n",
       "의원    3047\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clinic['의료기관종별명'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "자치구\n",
       "송파구     257\n",
       "강남구     201\n",
       "강서구     197\n",
       "은평구     156\n",
       "광진구     141\n",
       "서초구     141\n",
       "영등포구    136\n",
       "구로구     133\n",
       "마포구     131\n",
       "중랑구     124\n",
       "노원구     124\n",
       "동작구     122\n",
       "관악구     122\n",
       "강북구     119\n",
       "동대문구    118\n",
       "강동구     116\n",
       "성동구     114\n",
       "양천구     101\n",
       "성북구      99\n",
       "도봉구      81\n",
       "금천구      73\n",
       "서대문구     67\n",
       "용산구      66\n",
       "중구       59\n",
       "종로구      49\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 자치구별로 의원 개수 구하기\n",
    "clinic['자치구'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "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>자치구</th>\n",
       "      <th>1인당 총 급여</th>\n",
       "      <th>소아청소년과 병원 수</th>\n",
       "      <th>소아청소년과 의원 수</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>강남구</td>\n",
       "      <td>81053206.0</td>\n",
       "      <td>10</td>\n",
       "      <td>201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>서초구</td>\n",
       "      <td>80510746.0</td>\n",
       "      <td>5</td>\n",
       "      <td>141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>용산구</td>\n",
       "      <td>69795335.0</td>\n",
       "      <td>2</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>송파구</td>\n",
       "      <td>55703664.0</td>\n",
       "      <td>4</td>\n",
       "      <td>257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>성동구</td>\n",
       "      <td>52498140.0</td>\n",
       "      <td>2</td>\n",
       "      <td>114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>종로구</td>\n",
       "      <td>52233029.0</td>\n",
       "      <td>6</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>마포구</td>\n",
       "      <td>51249929.0</td>\n",
       "      <td>1</td>\n",
       "      <td>131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>중구</td>\n",
       "      <td>49499323.0</td>\n",
       "      <td>3</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>양천구</td>\n",
       "      <td>49391500.0</td>\n",
       "      <td>5</td>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>영등포구</td>\n",
       "      <td>47450278.0</td>\n",
       "      <td>6</td>\n",
       "      <td>136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>서대문구</td>\n",
       "      <td>44647890.0</td>\n",
       "      <td>4</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>강동구</td>\n",
       "      <td>44191868.0</td>\n",
       "      <td>5</td>\n",
       "      <td>116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>동작구</td>\n",
       "      <td>44188900.0</td>\n",
       "      <td>5</td>\n",
       "      <td>122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>성북구</td>\n",
       "      <td>42202897.0</td>\n",
       "      <td>3</td>\n",
       "      <td>99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>광진구</td>\n",
       "      <td>40772493.0</td>\n",
       "      <td>5</td>\n",
       "      <td>141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>노원구</td>\n",
       "      <td>40073375.0</td>\n",
       "      <td>9</td>\n",
       "      <td>124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>강서구</td>\n",
       "      <td>39478474.0</td>\n",
       "      <td>8</td>\n",
       "      <td>197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>동대문구</td>\n",
       "      <td>37884712.0</td>\n",
       "      <td>7</td>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>구로구</td>\n",
       "      <td>37383095.0</td>\n",
       "      <td>6</td>\n",
       "      <td>133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>은평구</td>\n",
       "      <td>36915161.0</td>\n",
       "      <td>8</td>\n",
       "      <td>156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>도봉구</td>\n",
       "      <td>33944232.0</td>\n",
       "      <td>5</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>관악구</td>\n",
       "      <td>33653264.0</td>\n",
       "      <td>4</td>\n",
       "      <td>122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>중랑구</td>\n",
       "      <td>32823432.0</td>\n",
       "      <td>8</td>\n",
       "      <td>124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>금천구</td>\n",
       "      <td>32511710.0</td>\n",
       "      <td>1</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>강북구</td>\n",
       "      <td>31041123.0</td>\n",
       "      <td>1</td>\n",
       "      <td>119</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     자치구    1인당 총 급여  소아청소년과 병원 수  소아청소년과 의원 수\n",
       "0    강남구  81053206.0           10          201\n",
       "1    서초구  80510746.0            5          141\n",
       "2    용산구  69795335.0            2           66\n",
       "3    송파구  55703664.0            4          257\n",
       "4    성동구  52498140.0            2          114\n",
       "5    종로구  52233029.0            6           49\n",
       "6    마포구  51249929.0            1          131\n",
       "7     중구  49499323.0            3           59\n",
       "8    양천구  49391500.0            5          101\n",
       "9   영등포구  47450278.0            6          136\n",
       "10  서대문구  44647890.0            4           67\n",
       "11   강동구  44191868.0            5          116\n",
       "12   동작구  44188900.0            5          122\n",
       "13   성북구  42202897.0            3           99\n",
       "14   광진구  40772493.0            5          141\n",
       "15   노원구  40073375.0            9          124\n",
       "16   강서구  39478474.0            8          197\n",
       "17  동대문구  37884712.0            7          118\n",
       "18   구로구  37383095.0            6          133\n",
       "19   은평구  36915161.0            8          156\n",
       "20   도봉구  33944232.0            5           81\n",
       "21   관악구  33653264.0            4          122\n",
       "22   중랑구  32823432.0            8          124\n",
       "23   금천구  32511710.0            1           73\n",
       "24   강북구  31041123.0            1          119"
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 소득 데이터와 의원 개수 데이터 합치기\n",
    "count_by_area = count_by_area.join(clinic['자치구'].value_counts(), on='자치구', how='left').rename(columns={'count':'소아청소년과 의원 수'})\n",
    "count_by_area\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "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>1인당 총 급여</th>\n",
       "      <th>소아청소년과 병원 수</th>\n",
       "      <th>소아청소년과 의원 수</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2.500000e+01</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>25.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4.644391e+07</td>\n",
       "      <td>4.920000</td>\n",
       "      <td>121.880000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.359716e+07</td>\n",
       "      <td>2.515287</td>\n",
       "      <td>46.997624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>3.104112e+07</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>49.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.738310e+07</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>99.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.418890e+07</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>122.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5.124993e+07</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>136.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>8.105321e+07</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>257.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           1인당 총 급여  소아청소년과 병원 수  소아청소년과 의원 수\n",
       "count  2.500000e+01    25.000000    25.000000\n",
       "mean   4.644391e+07     4.920000   121.880000\n",
       "std    1.359716e+07     2.515287    46.997624\n",
       "min    3.104112e+07     1.000000    49.000000\n",
       "25%    3.738310e+07     3.000000    99.000000\n",
       "50%    4.418890e+07     5.000000   122.000000\n",
       "75%    5.124993e+07     6.000000   136.000000\n",
       "max    8.105321e+07    10.000000   257.000000"
      ]
     },
     "execution_count": 216,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count_by_area.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 지역별 소득 수준 EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(count_by_area['1인당 총 급여'] / 10000, color='dodgerblue')\n",
    "plt.title('지역별 소득수준 분포')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy.stats import probplot\n",
    "\n",
    "probplot(count_by_area['1인당 총 급여'], plot=plt)\n",
    "plt.title('지역별 소득수준 QQ-Plot')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# EDA\n",
    "# 지역별 소득 수준 시각화\n",
    "plt.figure(figsize=(20, 10))\n",
    "# sns.barplot(data=count_by_area, x='자치구', y='1인당 총 급여')\n",
    "sns.barplot(x=count_by_area['자치구'], y=count_by_area['1인당 총 급여'] / 10000)\n",
    "plt.xticks(rotation=45)\n",
    "plt.xlabel('자치구', fontsize=20)\n",
    "plt.ylabel('1인당 총 급여 (단위:만원)', fontsize=20)\n",
    "plt.title('지역별 소득 수준', fontsize=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 가설 01: 지역의 소득 수준과 소아청소년과 병원의 개수는 관련이 있을 것이다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PearsonRResult(statistic=0.06753461215520816, pvalue=0.7483984365532081)"
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy.stats import pearsonr\n",
    "\n",
    "pearsonr(count_by_area['1인당 총 급여'], count_by_area['소아청소년과 병원 수'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(count_by_area[['1인당 총 급여', '소아청소년과 병원 수']].corr( method='pearson'), annot=True, cmap='Reds')\n",
    "plt.title('소득 수준과 소아청소년과 병원의 개수 상관분석(Pearson)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 5))\n",
    "sns.regplot(data=count_by_area, x=np.round(count_by_area['1인당 총 급여'] / 10000, 0), y='소아청소년과 병원 수', color='blue')\n",
    "plt.xticks(rotation=45)\n",
    "plt.xlabel('1인당 총 급여(만원)')\n",
    "plt.title('경제 수준과 소아청소년과 병원 수')\n",
    "\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 가설 검정 01 결론: 경제 수준과 소아청소년과 병원의 수가 연관이 있다고 보기는 어렵다. <br />\n",
    "pearson 상관 계수 결과 p-value 0.7, statistic 0.06으로 나왔다. <br />\n",
    "또한 시각화를 해봤을 때에도 뚜렷한 상관성을 찾지 못했다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PearsonRResult(statistic=0.1986954841756121, pvalue=0.34101833818291766)"
      ]
     },
     "execution_count": 225,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy.stats import pearsonr\n",
    "\n",
    "pearsonr(count_by_area['1인당 총 급여'], count_by_area['소아청소년과 의원 수'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(count_by_area[['1인당 총 급여', '소아청소년과 의원 수']].corr( method='pearson'), annot=True, cmap='Reds')\n",
    "plt.title('소득 수준과 소아청소년과 의원의 개수 상관분석(Pearson)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 5))\n",
    "sns.regplot(data=count_by_area, x=np.round(count_by_area['1인당 총 급여'] / 10000, 0), y='소아청소년과 의원 수', color='red')\n",
    "plt.xticks(rotation=45)\n",
    "plt.xlabel('1인당 총 급여(만원)')\n",
    "plt.title('지역 경제 수준과 소아청소년과 의원 수')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 가설 검정 02 결론: 경제 수준과 소아청소년과 의원의 수가 연관이 있다고 보기는 어렵다. <br />\n",
    "pearson 상관 계수 결과 p-value 0.19, statistic 0.34로 나왔다. <br />\n",
    "또한 시각화를 해봤을 때에도 뚜렷한 상관성을 찾지 못했다."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "myvenv",
   "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}