Chap_1_서울시CCTV설치운영현황.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "995d87a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "3bbb5d55",
   "metadata": {},
   "outputs": [],
   "source": [
    "a = pd.read_csv('./서울시CCTV설치운영현황(자치구)_년도별_210731기준.csv', header=1, encoding='euc-kr')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "4ed87d6d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>구분</th>\n",
       "      <th>총계</th>\n",
       "      <th>2012년 이전</th>\n",
       "      <th>2012년</th>\n",
       "      <th>2013년</th>\n",
       "      <th>2014년</th>\n",
       "      <th>2015년</th>\n",
       "      <th>2016년</th>\n",
       "      <th>2017년</th>\n",
       "      <th>2018년</th>\n",
       "      <th>2019년</th>\n",
       "      <th>2020년</th>\n",
       "      <th>2021년</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>관악구</td>\n",
       "      <td>4,942</td>\n",
       "      <td>428</td>\n",
       "      <td>205</td>\n",
       "      <td>291</td>\n",
       "      <td>513</td>\n",
       "      <td>529</td>\n",
       "      <td>621</td>\n",
       "      <td>687</td>\n",
       "      <td>663</td>\n",
       "      <td>640</td>\n",
       "      <td>331</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>서초구</td>\n",
       "      <td>3,704</td>\n",
       "      <td>116</td>\n",
       "      <td>75</td>\n",
       "      <td>94</td>\n",
       "      <td>71</td>\n",
       "      <td>563</td>\n",
       "      <td>516</td>\n",
       "      <td>1060</td>\n",
       "      <td>428</td>\n",
       "      <td>358</td>\n",
       "      <td>420</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>강남구</td>\n",
       "      <td>6,502</td>\n",
       "      <td>124</td>\n",
       "      <td>77</td>\n",
       "      <td>75</td>\n",
       "      <td>597</td>\n",
       "      <td>840</td>\n",
       "      <td>1310</td>\n",
       "      <td>999</td>\n",
       "      <td>748</td>\n",
       "      <td>789</td>\n",
       "      <td>942</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>송파구</td>\n",
       "      <td>2,854</td>\n",
       "      <td>72</td>\n",
       "      <td>61</td>\n",
       "      <td>86</td>\n",
       "      <td>85</td>\n",
       "      <td>215</td>\n",
       "      <td>148</td>\n",
       "      <td>241</td>\n",
       "      <td>542</td>\n",
       "      <td>1068</td>\n",
       "      <td>235</td>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>강동구</td>\n",
       "      <td>2,547</td>\n",
       "      <td>332</td>\n",
       "      <td>27</td>\n",
       "      <td>163</td>\n",
       "      <td>49</td>\n",
       "      <td>154</td>\n",
       "      <td>202</td>\n",
       "      <td>273</td>\n",
       "      <td>377</td>\n",
       "      <td>356</td>\n",
       "      <td>614</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     구분     총계 2012년 이전 2012년 2013년 2014년 2015년 2016년 2017년 2018년 2019년 2020년  \\\n",
       "21  관악구  4,942      428   205   291   513   529   621   687   663   640   331   \n",
       "22  서초구  3,704      116    75    94    71   563   516  1060   428   358   420   \n",
       "23  강남구  6,502      124    77    75   597   840  1310   999   748   789   942   \n",
       "24  송파구  2,854       72    61    86    85   215   148   241   542  1068   235   \n",
       "25  강동구  2,547      332    27   163    49   154   202   273   377   356   614   \n",
       "\n",
       "   2021년  \n",
       "21    34  \n",
       "22     3  \n",
       "23     1  \n",
       "24   101  \n",
       "25     0  "
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "ec8ae36c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "1d88aee0",
   "metadata": {},
   "outputs": [],
   "source": [
    "b = pd.read_excel('./Report.xls',usecols = 'B, D, G, J, N', header=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "5981f4a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "b.columns = ['구분', '인구수', '한국인', '외국인', '고령자']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "ef79656a",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>구분</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>21</th>\n",
       "      <td>관악구</td>\n",
       "      <td>499449</td>\n",
       "      <td>485699</td>\n",
       "      <td>13750</td>\n",
       "      <td>79871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>서초구</td>\n",
       "      <td>416167</td>\n",
       "      <td>412279</td>\n",
       "      <td>3888</td>\n",
       "      <td>60678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>강남구</td>\n",
       "      <td>537800</td>\n",
       "      <td>533042</td>\n",
       "      <td>4758</td>\n",
       "      <td>78226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>송파구</td>\n",
       "      <td>663965</td>\n",
       "      <td>658338</td>\n",
       "      <td>5627</td>\n",
       "      <td>97691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>강동구</td>\n",
       "      <td>466472</td>\n",
       "      <td>462664</td>\n",
       "      <td>3808</td>\n",
       "      <td>74070</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "     구분     인구수     한국인    외국인    고령자\n",
       "21  관악구  499449  485699  13750  79871\n",
       "22  서초구  416167  412279   3888  60678\n",
       "23  강남구  537800  533042   4758  78226\n",
       "24  송파구  663965  658338   5627  97691\n",
       "25  강동구  466472  462664   3808  74070"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "566dea06",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2022-01-02</th>\n",
       "      <td>-0.527184</td>\n",
       "      <td>1.147993</td>\n",
       "      <td>-1.111525</td>\n",
       "      <td>0.243748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-03</th>\n",
       "      <td>0.876879</td>\n",
       "      <td>-0.763611</td>\n",
       "      <td>0.224522</td>\n",
       "      <td>0.050912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-04</th>\n",
       "      <td>1.759086</td>\n",
       "      <td>-1.203655</td>\n",
       "      <td>-1.993030</td>\n",
       "      <td>1.168132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-05</th>\n",
       "      <td>-0.605677</td>\n",
       "      <td>-0.163607</td>\n",
       "      <td>1.085209</td>\n",
       "      <td>0.067206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-06</th>\n",
       "      <td>-0.168955</td>\n",
       "      <td>1.562395</td>\n",
       "      <td>-0.705232</td>\n",
       "      <td>0.104026</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "                   A         B         C         D\n",
       "2022-01-02 -0.527184  1.147993 -1.111525  0.243748\n",
       "2022-01-03  0.876879 -0.763611  0.224522  0.050912\n",
       "2022-01-04  1.759086 -1.203655 -1.993030  1.168132\n",
       "2022-01-05 -0.605677 -0.163607  1.085209  0.067206\n",
       "2022-01-06 -0.168955  1.562395 -0.705232  0.104026"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dates = pd.date_range('20220101', periods=6)\n",
    "df = pd.DataFrame(np.random.randn(6, 4), index=dates, columns=['A', 'B', 'C', 'D'])\n",
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "07aca872",
   "metadata": {},
   "outputs": [
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       "      <th>2022-01-06</th>\n",
       "      <td>-0.168955</td>\n",
       "      <td>1.562395</td>\n",
       "      <td>-0.705232</td>\n",
       "      <td>0.104026</td>\n",
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       "    <tr>\n",
       "      <th>2022-01-02</th>\n",
       "      <td>-0.527184</td>\n",
       "      <td>1.147993</td>\n",
       "      <td>-1.111525</td>\n",
       "      <td>0.243748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-01</th>\n",
       "      <td>-0.355780</td>\n",
       "      <td>0.433563</td>\n",
       "      <td>1.125087</td>\n",
       "      <td>0.238850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-05</th>\n",
       "      <td>-0.605677</td>\n",
       "      <td>-0.163607</td>\n",
       "      <td>1.085209</td>\n",
       "      <td>0.067206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-03</th>\n",
       "      <td>0.876879</td>\n",
       "      <td>-0.763611</td>\n",
       "      <td>0.224522</td>\n",
       "      <td>0.050912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-04</th>\n",
       "      <td>1.759086</td>\n",
       "      <td>-1.203655</td>\n",
       "      <td>-1.993030</td>\n",
       "      <td>1.168132</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2022-01-06 -0.168955  1.562395 -0.705232  0.104026\n",
       "2022-01-02 -0.527184  1.147993 -1.111525  0.243748\n",
       "2022-01-01 -0.355780  0.433563  1.125087  0.238850\n",
       "2022-01-05 -0.605677 -0.163607  1.085209  0.067206\n",
       "2022-01-03  0.876879 -0.763611  0.224522  0.050912\n",
       "2022-01-04  1.759086 -1.203655 -1.993030  1.168132"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values(by='B', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "f9c15a1a",
   "metadata": {},
   "outputs": [
    {
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       "      <th>D</th>\n",
       "      <th>E</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2022-01-01</th>\n",
       "      <td>-0.355780</td>\n",
       "      <td>0.433563</td>\n",
       "      <td>1.125087</td>\n",
       "      <td>0.238850</td>\n",
       "      <td>하나</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-02</th>\n",
       "      <td>-0.527184</td>\n",
       "      <td>1.147993</td>\n",
       "      <td>-1.111525</td>\n",
       "      <td>0.243748</td>\n",
       "      <td>하나</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-03</th>\n",
       "      <td>0.876879</td>\n",
       "      <td>-0.763611</td>\n",
       "      <td>0.224522</td>\n",
       "      <td>0.050912</td>\n",
       "      <td>둘</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-04</th>\n",
       "      <td>1.759086</td>\n",
       "      <td>-1.203655</td>\n",
       "      <td>-1.993030</td>\n",
       "      <td>1.168132</td>\n",
       "      <td>셋</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-05</th>\n",
       "      <td>-0.605677</td>\n",
       "      <td>-0.163607</td>\n",
       "      <td>1.085209</td>\n",
       "      <td>0.067206</td>\n",
       "      <td>넷</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-06</th>\n",
       "      <td>-0.168955</td>\n",
       "      <td>1.562395</td>\n",
       "      <td>-0.705232</td>\n",
       "      <td>0.104026</td>\n",
       "      <td>셋</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D   E\n",
       "2022-01-01 -0.355780  0.433563  1.125087  0.238850  하나\n",
       "2022-01-02 -0.527184  1.147993 -1.111525  0.243748  하나\n",
       "2022-01-03  0.876879 -0.763611  0.224522  0.050912   둘\n",
       "2022-01-04  1.759086 -1.203655 -1.993030  1.168132   셋\n",
       "2022-01-05 -0.605677 -0.163607  1.085209  0.067206   넷\n",
       "2022-01-06 -0.168955  1.562395 -0.705232  0.104026   셋"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = df.copy()\n",
    "df2['E'] = ['하나', '하나', '둘', '셋', '넷', '셋']\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "995706e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>E</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2022-01-03</th>\n",
       "      <td>0.876879</td>\n",
       "      <td>-0.763611</td>\n",
       "      <td>0.224522</td>\n",
       "      <td>0.050912</td>\n",
       "      <td>둘</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-05</th>\n",
       "      <td>-0.605677</td>\n",
       "      <td>-0.163607</td>\n",
       "      <td>1.085209</td>\n",
       "      <td>0.067206</td>\n",
       "      <td>넷</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D  E\n",
       "2022-01-03  0.876879 -0.763611  0.224522  0.050912  둘\n",
       "2022-01-05 -0.605677 -0.163607  1.085209  0.067206  넷"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2[df2['E'].isin(['둘', '넷'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "ced07abe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>E</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2022-01-01</th>\n",
       "      <td>-0.355780</td>\n",
       "      <td>0.433563</td>\n",
       "      <td>1.125087</td>\n",
       "      <td>0.238850</td>\n",
       "      <td>하나</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-02</th>\n",
       "      <td>-0.882964</td>\n",
       "      <td>1.581556</td>\n",
       "      <td>0.013562</td>\n",
       "      <td>0.482598</td>\n",
       "      <td>하나하나</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-03</th>\n",
       "      <td>-0.006085</td>\n",
       "      <td>0.817945</td>\n",
       "      <td>0.238084</td>\n",
       "      <td>0.533510</td>\n",
       "      <td>하나하나둘</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-04</th>\n",
       "      <td>1.753002</td>\n",
       "      <td>-0.385711</td>\n",
       "      <td>-1.754946</td>\n",
       "      <td>1.701642</td>\n",
       "      <td>하나하나둘셋</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-05</th>\n",
       "      <td>1.147324</td>\n",
       "      <td>-0.549317</td>\n",
       "      <td>-0.669737</td>\n",
       "      <td>1.768848</td>\n",
       "      <td>하나하나둘셋넷</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-06</th>\n",
       "      <td>0.978370</td>\n",
       "      <td>1.013078</td>\n",
       "      <td>-1.374969</td>\n",
       "      <td>1.872873</td>\n",
       "      <td>하나하나둘셋넷셋</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D         E\n",
       "2022-01-01 -0.355780  0.433563  1.125087  0.238850        하나\n",
       "2022-01-02 -0.882964  1.581556  0.013562  0.482598      하나하나\n",
       "2022-01-03 -0.006085  0.817945  0.238084  0.533510     하나하나둘\n",
       "2022-01-04  1.753002 -0.385711 -1.754946  1.701642    하나하나둘셋\n",
       "2022-01-05  1.147324 -0.549317 -0.669737  1.768848   하나하나둘셋넷\n",
       "2022-01-06  0.978370  1.013078 -1.374969  1.872873  하나하나둘셋넷셋"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.apply(np.cumsum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "9b6ad38c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>구분</th>\n",
       "      <th>총계</th>\n",
       "      <th>2012년 이전</th>\n",
       "      <th>2012년</th>\n",
       "      <th>2013년</th>\n",
       "      <th>2014년</th>\n",
       "      <th>2015년</th>\n",
       "      <th>2016년</th>\n",
       "      <th>2017년</th>\n",
       "      <th>2018년</th>\n",
       "      <th>2019년</th>\n",
       "      <th>2020년</th>\n",
       "      <th>2021년</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>관악구</td>\n",
       "      <td>4,942</td>\n",
       "      <td>428</td>\n",
       "      <td>205</td>\n",
       "      <td>291</td>\n",
       "      <td>513</td>\n",
       "      <td>529</td>\n",
       "      <td>621</td>\n",
       "      <td>687</td>\n",
       "      <td>663</td>\n",
       "      <td>640</td>\n",
       "      <td>331</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>서초구</td>\n",
       "      <td>3,704</td>\n",
       "      <td>116</td>\n",
       "      <td>75</td>\n",
       "      <td>94</td>\n",
       "      <td>71</td>\n",
       "      <td>563</td>\n",
       "      <td>516</td>\n",
       "      <td>1060</td>\n",
       "      <td>428</td>\n",
       "      <td>358</td>\n",
       "      <td>420</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>강남구</td>\n",
       "      <td>6,502</td>\n",
       "      <td>124</td>\n",
       "      <td>77</td>\n",
       "      <td>75</td>\n",
       "      <td>597</td>\n",
       "      <td>840</td>\n",
       "      <td>1310</td>\n",
       "      <td>999</td>\n",
       "      <td>748</td>\n",
       "      <td>789</td>\n",
       "      <td>942</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>송파구</td>\n",
       "      <td>2,854</td>\n",
       "      <td>72</td>\n",
       "      <td>61</td>\n",
       "      <td>86</td>\n",
       "      <td>85</td>\n",
       "      <td>215</td>\n",
       "      <td>148</td>\n",
       "      <td>241</td>\n",
       "      <td>542</td>\n",
       "      <td>1068</td>\n",
       "      <td>235</td>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>강동구</td>\n",
       "      <td>2,547</td>\n",
       "      <td>332</td>\n",
       "      <td>27</td>\n",
       "      <td>163</td>\n",
       "      <td>49</td>\n",
       "      <td>154</td>\n",
       "      <td>202</td>\n",
       "      <td>273</td>\n",
       "      <td>377</td>\n",
       "      <td>356</td>\n",
       "      <td>614</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     구분     총계 2012년 이전 2012년 2013년 2014년 2015년 2016년 2017년 2018년 2019년 2020년  \\\n",
       "21  관악구  4,942      428   205   291   513   529   621   687   663   640   331   \n",
       "22  서초구  3,704      116    75    94    71   563   516  1060   428   358   420   \n",
       "23  강남구  6,502      124    77    75   597   840  1310   999   748   789   942   \n",
       "24  송파구  2,854       72    61    86    85   215   148   241   542  1068   235   \n",
       "25  강동구  2,547      332    27   163    49   154   202   273   377   356   614   \n",
       "\n",
       "   2021년  \n",
       "21    34  \n",
       "22     3  \n",
       "23     1  \n",
       "24   101  \n",
       "25     0  "
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "56fc17d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "a.drop(['2012년 이전', '2012년', '2013년', '2014년', '2015년', '2016년', '2017년', '2018년'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "d5f2e647",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>구분</th>\n",
       "      <th>총계</th>\n",
       "      <th>2019년</th>\n",
       "      <th>2020년</th>\n",
       "      <th>2021년</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>관악구</td>\n",
       "      <td>4,942</td>\n",
       "      <td>640</td>\n",
       "      <td>331</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>서초구</td>\n",
       "      <td>3,704</td>\n",
       "      <td>358</td>\n",
       "      <td>420</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>강남구</td>\n",
       "      <td>6,502</td>\n",
       "      <td>789</td>\n",
       "      <td>942</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>송파구</td>\n",
       "      <td>2,854</td>\n",
       "      <td>1068</td>\n",
       "      <td>235</td>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>강동구</td>\n",
       "      <td>2,547</td>\n",
       "      <td>356</td>\n",
       "      <td>614</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     구분     총계 2019년 2020년 2021년\n",
       "21  관악구  4,942   640   331    34\n",
       "22  서초구  3,704   358   420     3\n",
       "23  강남구  6,502   789   942     1\n",
       "24  송파구  2,854  1068   235   101\n",
       "25  강동구  2,547   356   614     0"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "4141a69f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 26 entries, 0 to 25\n",
      "Data columns (total 5 columns):\n",
      " #   Column  Non-Null Count  Dtype \n",
      "---  ------  --------------  ----- \n",
      " 0   구분      26 non-null     object\n",
      " 1   총계      26 non-null     int32 \n",
      " 2   2019년   26 non-null     int32 \n",
      " 3   2020년   26 non-null     int32 \n",
      " 4   2021년   26 non-null     int32 \n",
      "dtypes: int32(4), object(1)\n",
      "memory usage: 752.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "a['총계'] = a['총계'].apply(lambda x: x.replace(',', ''))\n",
    "a['2019년'] = a['2019년'].apply(lambda x: x.replace(',', ''))\n",
    "a['2020년'] = a['2020년'].apply(lambda x: x.replace(',', ''))\n",
    "a['2021년'] = a['2021년'].apply(lambda x: x.replace(',', ''))\n",
    "a = a.astype({'총계': 'int', '2019년': 'int', '2020년': 'int',\n",
    "             '2021년': 'int'})\n",
    "a.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "187a6570",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>구분</th>\n",
       "      <th>총계</th>\n",
       "      <th>2019년</th>\n",
       "      <th>2020년</th>\n",
       "      <th>2021년</th>\n",
       "      <th>증가율</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>계</td>\n",
       "      <td>77032</td>\n",
       "      <td>11961</td>\n",
       "      <td>11132</td>\n",
       "      <td>1608</td>\n",
       "      <td>23095.087444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>중랑구</td>\n",
       "      <td>3296</td>\n",
       "      <td>1049</td>\n",
       "      <td>939</td>\n",
       "      <td>4</td>\n",
       "      <td>1988.121359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>강남구</td>\n",
       "      <td>6502</td>\n",
       "      <td>789</td>\n",
       "      <td>942</td>\n",
       "      <td>1</td>\n",
       "      <td>1731.015380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>강북구</td>\n",
       "      <td>2462</td>\n",
       "      <td>1000</td>\n",
       "      <td>588</td>\n",
       "      <td>39</td>\n",
       "      <td>1589.584078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>은평구</td>\n",
       "      <td>3791</td>\n",
       "      <td>1057</td>\n",
       "      <td>288</td>\n",
       "      <td>131</td>\n",
       "      <td>1348.455553</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     구분     총계  2019년  2020년  2021년           증가율\n",
       "0     계  77032  11961  11132   1608  23095.087444\n",
       "7   중랑구   3296   1049    939      4   1988.121359\n",
       "23  강남구   6502    789    942      1   1731.015380\n",
       "9   강북구   2462   1000    588     39   1589.584078\n",
       "12  은평구   3791   1057    288    131   1348.455553"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a['증가율'] = (a['2019년'] + a['2020년'] + a['2021년'] / a['총계'] * 100)\n",
    "a.sort_values(by='증가율', ascending=False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "579abaee",
   "metadata": {},
   "outputs": [],
   "source": [
    "a.drop(0, axis=0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "4434a23a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>구분</th>\n",
       "      <th>총계</th>\n",
       "      <th>2019년</th>\n",
       "      <th>2020년</th>\n",
       "      <th>2021년</th>\n",
       "      <th>증가율</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>종로구</td>\n",
       "      <td>1772</td>\n",
       "      <td>9</td>\n",
       "      <td>200</td>\n",
       "      <td>43</td>\n",
       "      <td>211.426637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>중 구</td>\n",
       "      <td>2333</td>\n",
       "      <td>155</td>\n",
       "      <td>361</td>\n",
       "      <td>289</td>\n",
       "      <td>528.387484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>용산구</td>\n",
       "      <td>2383</td>\n",
       "      <td>307</td>\n",
       "      <td>617</td>\n",
       "      <td>0</td>\n",
       "      <td>924.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>성동구</td>\n",
       "      <td>3602</td>\n",
       "      <td>262</td>\n",
       "      <td>461</td>\n",
       "      <td>76</td>\n",
       "      <td>725.109939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>광진구</td>\n",
       "      <td>2588</td>\n",
       "      <td>712</td>\n",
       "      <td>175</td>\n",
       "      <td>32</td>\n",
       "      <td>888.236476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>동대문구</td>\n",
       "      <td>2497</td>\n",
       "      <td>209</td>\n",
       "      <td>223</td>\n",
       "      <td>63</td>\n",
       "      <td>434.523028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>중랑구</td>\n",
       "      <td>3296</td>\n",
       "      <td>1049</td>\n",
       "      <td>939</td>\n",
       "      <td>4</td>\n",
       "      <td>1988.121359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>성북구</td>\n",
       "      <td>3958</td>\n",
       "      <td>714</td>\n",
       "      <td>251</td>\n",
       "      <td>188</td>\n",
       "      <td>969.749874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>강북구</td>\n",
       "      <td>2462</td>\n",
       "      <td>1000</td>\n",
       "      <td>588</td>\n",
       "      <td>39</td>\n",
       "      <td>1589.584078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>도봉구</td>\n",
       "      <td>1629</td>\n",
       "      <td>198</td>\n",
       "      <td>168</td>\n",
       "      <td>331</td>\n",
       "      <td>386.319214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>노원구</td>\n",
       "      <td>2415</td>\n",
       "      <td>320</td>\n",
       "      <td>386</td>\n",
       "      <td>104</td>\n",
       "      <td>710.306418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>은평구</td>\n",
       "      <td>3791</td>\n",
       "      <td>1057</td>\n",
       "      <td>288</td>\n",
       "      <td>131</td>\n",
       "      <td>1348.455553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>서대문구</td>\n",
       "      <td>2940</td>\n",
       "      <td>343</td>\n",
       "      <td>397</td>\n",
       "      <td>1</td>\n",
       "      <td>740.034014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>마포구</td>\n",
       "      <td>2421</td>\n",
       "      <td>494</td>\n",
       "      <td>300</td>\n",
       "      <td>20</td>\n",
       "      <td>794.826105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>양천구</td>\n",
       "      <td>3312</td>\n",
       "      <td>274</td>\n",
       "      <td>325</td>\n",
       "      <td>0</td>\n",
       "      <td>599.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>강서구</td>\n",
       "      <td>2560</td>\n",
       "      <td>457</td>\n",
       "      <td>356</td>\n",
       "      <td>123</td>\n",
       "      <td>817.804688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>구로구</td>\n",
       "      <td>4075</td>\n",
       "      <td>434</td>\n",
       "      <td>415</td>\n",
       "      <td>0</td>\n",
       "      <td>849.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>금천구</td>\n",
       "      <td>2374</td>\n",
       "      <td>369</td>\n",
       "      <td>508</td>\n",
       "      <td>9</td>\n",
       "      <td>877.379107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>영등포구</td>\n",
       "      <td>3778</td>\n",
       "      <td>65</td>\n",
       "      <td>1213</td>\n",
       "      <td>5</td>\n",
       "      <td>1278.132345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>동작구</td>\n",
       "      <td>2297</td>\n",
       "      <td>322</td>\n",
       "      <td>419</td>\n",
       "      <td>11</td>\n",
       "      <td>741.478886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>관악구</td>\n",
       "      <td>4942</td>\n",
       "      <td>640</td>\n",
       "      <td>331</td>\n",
       "      <td>34</td>\n",
       "      <td>971.687981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>서초구</td>\n",
       "      <td>3704</td>\n",
       "      <td>358</td>\n",
       "      <td>420</td>\n",
       "      <td>3</td>\n",
       "      <td>778.080994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>강남구</td>\n",
       "      <td>6502</td>\n",
       "      <td>789</td>\n",
       "      <td>942</td>\n",
       "      <td>1</td>\n",
       "      <td>1731.015380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>송파구</td>\n",
       "      <td>2854</td>\n",
       "      <td>1068</td>\n",
       "      <td>235</td>\n",
       "      <td>101</td>\n",
       "      <td>1306.538893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>강동구</td>\n",
       "      <td>2547</td>\n",
       "      <td>356</td>\n",
       "      <td>614</td>\n",
       "      <td>0</td>\n",
       "      <td>970.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      구분    총계  2019년  2020년  2021년          증가율\n",
       "1    종로구  1772      9    200     43   211.426637\n",
       "2    중 구  2333    155    361    289   528.387484\n",
       "3    용산구  2383    307    617      0   924.000000\n",
       "4    성동구  3602    262    461     76   725.109939\n",
       "5    광진구  2588    712    175     32   888.236476\n",
       "6   동대문구  2497    209    223     63   434.523028\n",
       "7    중랑구  3296   1049    939      4  1988.121359\n",
       "8    성북구  3958    714    251    188   969.749874\n",
       "9    강북구  2462   1000    588     39  1589.584078\n",
       "10   도봉구  1629    198    168    331   386.319214\n",
       "11   노원구  2415    320    386    104   710.306418\n",
       "12   은평구  3791   1057    288    131  1348.455553\n",
       "13  서대문구  2940    343    397      1   740.034014\n",
       "14   마포구  2421    494    300     20   794.826105\n",
       "15   양천구  3312    274    325      0   599.000000\n",
       "16   강서구  2560    457    356    123   817.804688\n",
       "17   구로구  4075    434    415      0   849.000000\n",
       "18   금천구  2374    369    508      9   877.379107\n",
       "19  영등포구  3778     65   1213      5  1278.132345\n",
       "20   동작구  2297    322    419     11   741.478886\n",
       "21   관악구  4942    640    331     34   971.687981\n",
       "22   서초구  3704    358    420      3   778.080994\n",
       "23   강남구  6502    789    942      1  1731.015380\n",
       "24   송파구  2854   1068    235    101  1306.538893\n",
       "25   강동구  2547    356    614      0   970.000000"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "c9996b7b",
   "metadata": {},
   "outputs": [],
   "source": [
    "b.drop(0, axis=0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "93388345",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['종로구', '중구', '용산구', '성동구', '광진구', '동대문구', '중랑구', '성북구', '강북구',\n",
       "       '도봉구', '노원구', '은평구', '서대문구', '마포구', '양천구', '강서구', '구로구', '금천구',\n",
       "       '영등포구', '동작구', '관악구', '서초구', '강남구', '송파구', '강동구'], dtype=object)"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b['구분'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "d61b763e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "구분     0\n",
       "인구수    0\n",
       "한국인    0\n",
       "외국인    0\n",
       "고령자    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "36c69733",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>구분</th>\n",
       "      <th>인구수</th>\n",
       "      <th>한국인</th>\n",
       "      <th>외국인</th>\n",
       "      <th>고령자</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>종로구</td>\n",
       "      <td>153789</td>\n",
       "      <td>144683</td>\n",
       "      <td>9106</td>\n",
       "      <td>27818</td>\n",
       "      <td>5.921100</td>\n",
       "      <td>18.088420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>중구</td>\n",
       "      <td>131787</td>\n",
       "      <td>122499</td>\n",
       "      <td>9288</td>\n",
       "      <td>24392</td>\n",
       "      <td>7.047736</td>\n",
       "      <td>18.508654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>용산구</td>\n",
       "      <td>237285</td>\n",
       "      <td>222953</td>\n",
       "      <td>14332</td>\n",
       "      <td>39070</td>\n",
       "      <td>6.039994</td>\n",
       "      <td>16.465432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>성동구</td>\n",
       "      <td>292672</td>\n",
       "      <td>285990</td>\n",
       "      <td>6682</td>\n",
       "      <td>46380</td>\n",
       "      <td>2.283102</td>\n",
       "      <td>15.847092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>광진구</td>\n",
       "      <td>352627</td>\n",
       "      <td>339996</td>\n",
       "      <td>12631</td>\n",
       "      <td>51723</td>\n",
       "      <td>3.581972</td>\n",
       "      <td>14.667907</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    구분     인구수     한국인    외국인    고령자     외국인비율      고령자비율\n",
       "1  종로구  153789  144683   9106  27818  5.921100  18.088420\n",
       "2   중구  131787  122499   9288  24392  7.047736  18.508654\n",
       "3  용산구  237285  222953  14332  39070  6.039994  16.465432\n",
       "4  성동구  292672  285990   6682  46380  2.283102  15.847092\n",
       "5  광진구  352627  339996  12631  51723  3.581972  14.667907"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b['외국인비율'] = b['외국인'] / b['인구수'] * 100\n",
    "b['고령자비율'] = b['고령자'] / b['인구수'] * 100\n",
    "b.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "6861d783",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>총계</th>\n",
       "      <th>2019년</th>\n",
       "      <th>2020년</th>\n",
       "      <th>2021년</th>\n",
       "      <th>증가율</th>\n",
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       "      <th>구분</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>종로구</th>\n",
       "      <td>1772</td>\n",
       "      <td>9</td>\n",
       "      <td>200</td>\n",
       "      <td>43</td>\n",
       "      <td>211.426637</td>\n",
       "      <td>153789</td>\n",
       "      <td>144683</td>\n",
       "      <td>9106</td>\n",
       "      <td>27818</td>\n",
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       "    <tr>\n",
       "      <th>용산구</th>\n",
       "      <td>2383</td>\n",
       "      <td>307</td>\n",
       "      <td>617</td>\n",
       "      <td>0</td>\n",
       "      <td>924.000000</td>\n",
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       "      <td>222953</td>\n",
       "      <td>14332</td>\n",
       "      <td>39070</td>\n",
       "      <td>6.039994</td>\n",
       "      <td>16.465432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>성동구</th>\n",
       "      <td>3602</td>\n",
       "      <td>262</td>\n",
       "      <td>461</td>\n",
       "      <td>76</td>\n",
       "      <td>725.109939</td>\n",
       "      <td>292672</td>\n",
       "      <td>285990</td>\n",
       "      <td>6682</td>\n",
       "      <td>46380</td>\n",
       "      <td>2.283102</td>\n",
       "      <td>15.847092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>광진구</th>\n",
       "      <td>2588</td>\n",
       "      <td>712</td>\n",
       "      <td>175</td>\n",
       "      <td>32</td>\n",
       "      <td>888.236476</td>\n",
       "      <td>352627</td>\n",
       "      <td>339996</td>\n",
       "      <td>12631</td>\n",
       "      <td>51723</td>\n",
       "      <td>3.581972</td>\n",
       "      <td>14.667907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>동대문구</th>\n",
       "      <td>2497</td>\n",
       "      <td>209</td>\n",
       "      <td>223</td>\n",
       "      <td>63</td>\n",
       "      <td>434.523028</td>\n",
       "      <td>352006</td>\n",
       "      <td>337400</td>\n",
       "      <td>14606</td>\n",
       "      <td>62211</td>\n",
       "      <td>4.149361</td>\n",
       "      <td>17.673278</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        총계  2019년  2020년  2021년         증가율     인구수     한국인    외국인    고령자  \\\n",
       "구분                                                                          \n",
       "종로구   1772      9    200     43  211.426637  153789  144683   9106  27818   \n",
       "용산구   2383    307    617      0  924.000000  237285  222953  14332  39070   \n",
       "성동구   3602    262    461     76  725.109939  292672  285990   6682  46380   \n",
       "광진구   2588    712    175     32  888.236476  352627  339996  12631  51723   \n",
       "동대문구  2497    209    223     63  434.523028  352006  337400  14606  62211   \n",
       "\n",
       "         외국인비율      고령자비율  \n",
       "구분                         \n",
       "종로구   5.921100  18.088420  \n",
       "용산구   6.039994  16.465432  \n",
       "성동구   2.283102  15.847092  \n",
       "광진구   3.581972  14.667907  \n",
       "동대문구  4.149361  17.673278  "
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_result = pd.merge(a, b, on='구분')\n",
    "data_result.set_index('구분', inplace=True)\n",
    "data_result.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "4b0e8739",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>총계</th>\n",
       "      <th>2019년</th>\n",
       "      <th>2020년</th>\n",
       "      <th>2021년</th>\n",
       "      <th>증가율</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>총계</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.339716</td>\n",
       "      <td>0.411937</td>\n",
       "      <td>-0.241999</td>\n",
       "      <td>0.511168</td>\n",
       "      <td>0.442951</td>\n",
       "      <td>0.430239</td>\n",
       "      <td>0.127602</td>\n",
       "      <td>0.350971</td>\n",
       "      <td>-0.133597</td>\n",
       "      <td>-0.373766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019년</th>\n",
       "      <td>0.339716</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.047552</td>\n",
       "      <td>0.047917</td>\n",
       "      <td>0.781229</td>\n",
       "      <td>0.462976</td>\n",
       "      <td>0.473572</td>\n",
       "      <td>-0.322157</td>\n",
       "      <td>0.529508</td>\n",
       "      <td>-0.469803</td>\n",
       "      <td>0.087976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020년</th>\n",
       "      <td>0.411937</td>\n",
       "      <td>0.047552</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.460350</td>\n",
       "      <td>0.660620</td>\n",
       "      <td>0.028191</td>\n",
       "      <td>0.016454</td>\n",
       "      <td>0.216537</td>\n",
       "      <td>0.002525</td>\n",
       "      <td>0.128509</td>\n",
       "      <td>-0.082105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021년</th>\n",
       "      <td>-0.241999</td>\n",
       "      <td>0.047917</td>\n",
       "      <td>-0.460350</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.244029</td>\n",
       "      <td>0.123783</td>\n",
       "      <td>0.140452</td>\n",
       "      <td>-0.350028</td>\n",
       "      <td>0.283237</td>\n",
       "      <td>-0.362357</td>\n",
       "      <td>0.410588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>증가율</th>\n",
       "      <td>0.511168</td>\n",
       "      <td>0.781229</td>\n",
       "      <td>0.660620</td>\n",
       "      <td>-0.244029</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.365995</td>\n",
       "      <td>0.366764</td>\n",
       "      <td>-0.109566</td>\n",
       "      <td>0.401447</td>\n",
       "      <td>-0.275710</td>\n",
       "      <td>0.019089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>인구수</th>\n",
       "      <td>0.442951</td>\n",
       "      <td>0.462976</td>\n",
       "      <td>0.028191</td>\n",
       "      <td>0.123783</td>\n",
       "      <td>0.365995</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.998700</td>\n",
       "      <td>-0.234521</td>\n",
       "      <td>0.938881</td>\n",
       "      <td>-0.603026</td>\n",
       "      <td>-0.406828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>한국인</th>\n",
       "      <td>0.430239</td>\n",
       "      <td>0.473572</td>\n",
       "      <td>0.016454</td>\n",
       "      <td>0.140452</td>\n",
       "      <td>0.366764</td>\n",
       "      <td>0.998700</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.283763</td>\n",
       "      <td>0.941389</td>\n",
       "      <td>-0.640050</td>\n",
       "      <td>-0.393231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>외국인</th>\n",
       "      <td>0.127602</td>\n",
       "      <td>-0.322157</td>\n",
       "      <td>0.216537</td>\n",
       "      <td>-0.350028</td>\n",
       "      <td>-0.109566</td>\n",
       "      <td>-0.234521</td>\n",
       "      <td>-0.283763</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.291301</td>\n",
       "      <td>0.862531</td>\n",
       "      <td>-0.153830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>고령자</th>\n",
       "      <td>0.350971</td>\n",
       "      <td>0.529508</td>\n",
       "      <td>0.002525</td>\n",
       "      <td>0.283237</td>\n",
       "      <td>0.401447</td>\n",
       "      <td>0.938881</td>\n",
       "      <td>0.941389</td>\n",
       "      <td>-0.291301</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.657263</td>\n",
       "      <td>-0.078938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>외국인비율</th>\n",
       "      <td>-0.133597</td>\n",
       "      <td>-0.469803</td>\n",
       "      <td>0.128509</td>\n",
       "      <td>-0.362357</td>\n",
       "      <td>-0.275710</td>\n",
       "      <td>-0.603026</td>\n",
       "      <td>-0.640050</td>\n",
       "      <td>0.862531</td>\n",
       "      <td>-0.657263</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.016869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>고령자비율</th>\n",
       "      <td>-0.373766</td>\n",
       "      <td>0.087976</td>\n",
       "      <td>-0.082105</td>\n",
       "      <td>0.410588</td>\n",
       "      <td>0.019089</td>\n",
       "      <td>-0.406828</td>\n",
       "      <td>-0.393231</td>\n",
       "      <td>-0.153830</td>\n",
       "      <td>-0.078938</td>\n",
       "      <td>-0.016869</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             총계     2019년     2020년     2021년       증가율       인구수       한국인  \\\n",
       "총계     1.000000  0.339716  0.411937 -0.241999  0.511168  0.442951  0.430239   \n",
       "2019년  0.339716  1.000000  0.047552  0.047917  0.781229  0.462976  0.473572   \n",
       "2020년  0.411937  0.047552  1.000000 -0.460350  0.660620  0.028191  0.016454   \n",
       "2021년 -0.241999  0.047917 -0.460350  1.000000 -0.244029  0.123783  0.140452   \n",
       "증가율    0.511168  0.781229  0.660620 -0.244029  1.000000  0.365995  0.366764   \n",
       "인구수    0.442951  0.462976  0.028191  0.123783  0.365995  1.000000  0.998700   \n",
       "한국인    0.430239  0.473572  0.016454  0.140452  0.366764  0.998700  1.000000   \n",
       "외국인    0.127602 -0.322157  0.216537 -0.350028 -0.109566 -0.234521 -0.283763   \n",
       "고령자    0.350971  0.529508  0.002525  0.283237  0.401447  0.938881  0.941389   \n",
       "외국인비율 -0.133597 -0.469803  0.128509 -0.362357 -0.275710 -0.603026 -0.640050   \n",
       "고령자비율 -0.373766  0.087976 -0.082105  0.410588  0.019089 -0.406828 -0.393231   \n",
       "\n",
       "            외국인       고령자     외국인비율     고령자비율  \n",
       "총계     0.127602  0.350971 -0.133597 -0.373766  \n",
       "2019년 -0.322157  0.529508 -0.469803  0.087976  \n",
       "2020년  0.216537  0.002525  0.128509 -0.082105  \n",
       "2021년 -0.350028  0.283237 -0.362357  0.410588  \n",
       "증가율   -0.109566  0.401447 -0.275710  0.019089  \n",
       "인구수   -0.234521  0.938881 -0.603026 -0.406828  \n",
       "한국인   -0.283763  0.941389 -0.640050 -0.393231  \n",
       "외국인    1.000000 -0.291301  0.862531 -0.153830  \n",
       "고령자   -0.291301  1.000000 -0.657263 -0.078938  \n",
       "외국인비율  0.862531 -0.657263  1.000000 -0.016869  \n",
       "고령자비율 -0.153830 -0.078938 -0.016869  1.000000  "
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_result.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "6598a8e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "63f557ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rcParams['font.family'] = 'Malgun Gothic'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "6d9f8dfb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure\n",
    "x = [0,1,2,3,4]\n",
    "y = [10,20,30,40,50]\n",
    "plt.plot(x, y)\n",
    "plt.xlabel('안녕')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "bfd367dd",
   "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['총계'].sort_values().plot(kind='barh', grid=True, figsize=(10, 10))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "1089e0a5",
   "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['총계'] / 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": 134,
   "id": "85d55cb5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.scatter(data_result['인구수'], data_result['총계'], s=50)\n",
    "plt.xlabel('인구수')\n",
    "plt.ylabel('cctv')\n",
    "plt.title('산점도 찍어볼게')\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "928bd470",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "0862e090",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot(data_result['인구수'], data_result['총계'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "b32bdfe4",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x221fde490d0>"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.jointplot(data_result['인구수'], data_result['총계'], kind='hex')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d509bc61",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}