3. 주민등록인구 EDA.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "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 os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# matplotlib 한글 출력..\n",
    "\n",
    "import matplotlib as mpl\n",
    "import matplotlib.font_manager as fm\n",
    "mpl.rcParams['axes.unicode_minus'] = False\n",
    "path = 'C:/Windows/Fonts/malgun.ttf'\n",
    "font_name = fm.FontProperties(fname=path, size=50).get_name()\n",
    "plt.rc('font', family=font_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 경고창 삭제\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>행정구역</th>\n",
       "      <th>2010년01월_계_연령구간인구수</th>\n",
       "      <th>2010년02월_계_연령구간인구수</th>\n",
       "      <th>2010년03월_계_연령구간인구수</th>\n",
       "      <th>2010년04월_계_연령구간인구수</th>\n",
       "      <th>2010년05월_계_연령구간인구수</th>\n",
       "      <th>2010년06월_계_연령구간인구수</th>\n",
       "      <th>2010년07월_계_연령구간인구수</th>\n",
       "      <th>2010년08월_계_연령구간인구수</th>\n",
       "      <th>2010년09월_계_연령구간인구수</th>\n",
       "      <th>...</th>\n",
       "      <th>2019년03월_계_연령구간인구수</th>\n",
       "      <th>2019년04월_계_연령구간인구수</th>\n",
       "      <th>2019년05월_계_연령구간인구수</th>\n",
       "      <th>2019년06월_계_연령구간인구수</th>\n",
       "      <th>2019년07월_계_연령구간인구수</th>\n",
       "      <th>2019년08월_계_연령구간인구수</th>\n",
       "      <th>2019년09월_계_연령구간인구수</th>\n",
       "      <th>2019년10월_계_연령구간인구수</th>\n",
       "      <th>2019년11월_계_연령구간인구수</th>\n",
       "      <th>2019년12월_계_연령구간인구수</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>서울특별시  (1100000000)</td>\n",
       "      <td>509764.0</td>\n",
       "      <td>508535.0</td>\n",
       "      <td>506755.0</td>\n",
       "      <td>505627.0</td>\n",
       "      <td>504480.0</td>\n",
       "      <td>503991.0</td>\n",
       "      <td>503200.0</td>\n",
       "      <td>502690.0</td>\n",
       "      <td>502054.0</td>\n",
       "      <td>...</td>\n",
       "      <td>386601.0</td>\n",
       "      <td>384909.0</td>\n",
       "      <td>383271.0</td>\n",
       "      <td>381399.0</td>\n",
       "      <td>379415.0</td>\n",
       "      <td>376635.0</td>\n",
       "      <td>374490.0</td>\n",
       "      <td>372892.0</td>\n",
       "      <td>371110.0</td>\n",
       "      <td>369938.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>서울특별시 종로구 (1111000000)</td>\n",
       "      <td>6420.0</td>\n",
       "      <td>6416.0</td>\n",
       "      <td>6372.0</td>\n",
       "      <td>6388.0</td>\n",
       "      <td>6348.0</td>\n",
       "      <td>6354.0</td>\n",
       "      <td>6311.0</td>\n",
       "      <td>6285.0</td>\n",
       "      <td>6259.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4635.0</td>\n",
       "      <td>4618.0</td>\n",
       "      <td>4589.0</td>\n",
       "      <td>4544.0</td>\n",
       "      <td>4534.0</td>\n",
       "      <td>4524.0</td>\n",
       "      <td>4502.0</td>\n",
       "      <td>4469.0</td>\n",
       "      <td>4442.0</td>\n",
       "      <td>4417.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>서울특별시 종로구 청운효자동(1111051500)</td>\n",
       "      <td>710.0</td>\n",
       "      <td>709.0</td>\n",
       "      <td>699.0</td>\n",
       "      <td>691.0</td>\n",
       "      <td>684.0</td>\n",
       "      <td>680.0</td>\n",
       "      <td>678.0</td>\n",
       "      <td>667.0</td>\n",
       "      <td>656.0</td>\n",
       "      <td>...</td>\n",
       "      <td>499.0</td>\n",
       "      <td>497.0</td>\n",
       "      <td>492.0</td>\n",
       "      <td>483.0</td>\n",
       "      <td>493.0</td>\n",
       "      <td>493.0</td>\n",
       "      <td>491.0</td>\n",
       "      <td>486.0</td>\n",
       "      <td>487.0</td>\n",
       "      <td>481.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>서울특별시 종로구 사직동(1111053000)</td>\n",
       "      <td>446.0</td>\n",
       "      <td>436.0</td>\n",
       "      <td>437.0</td>\n",
       "      <td>442.0</td>\n",
       "      <td>443.0</td>\n",
       "      <td>455.0</td>\n",
       "      <td>452.0</td>\n",
       "      <td>453.0</td>\n",
       "      <td>452.0</td>\n",
       "      <td>...</td>\n",
       "      <td>354.0</td>\n",
       "      <td>349.0</td>\n",
       "      <td>346.0</td>\n",
       "      <td>340.0</td>\n",
       "      <td>336.0</td>\n",
       "      <td>338.0</td>\n",
       "      <td>337.0</td>\n",
       "      <td>336.0</td>\n",
       "      <td>337.0</td>\n",
       "      <td>343.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>서울특별시 종로구 삼청동(1111054000)</td>\n",
       "      <td>132.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>127.0</td>\n",
       "      <td>127.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>127.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>123.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>...</td>\n",
       "      <td>70.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>57.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 121 columns</p>\n",
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      "text/plain": [
       "                          행정구역  2010년01월_계_연령구간인구수  2010년02월_계_연령구간인구수  \\\n",
       "0          서울특별시  (1100000000)            509764.0            508535.0   \n",
       "1       서울특별시 종로구 (1111000000)              6420.0              6416.0   \n",
       "2  서울특별시 종로구 청운효자동(1111051500)               710.0               709.0   \n",
       "3    서울특별시 종로구 사직동(1111053000)               446.0               436.0   \n",
       "4    서울특별시 종로구 삼청동(1111054000)               132.0               128.0   \n",
       "\n",
       "   2010년03월_계_연령구간인구수  2010년04월_계_연령구간인구수  2010년05월_계_연령구간인구수  \\\n",
       "0            506755.0            505627.0            504480.0   \n",
       "1              6372.0              6388.0              6348.0   \n",
       "2               699.0               691.0               684.0   \n",
       "3               437.0               442.0               443.0   \n",
       "4               127.0               127.0               125.0   \n",
       "\n",
       "   2010년06월_계_연령구간인구수  2010년07월_계_연령구간인구수  2010년08월_계_연령구간인구수  \\\n",
       "0            503991.0            503200.0            502690.0   \n",
       "1              6354.0              6311.0              6285.0   \n",
       "2               680.0               678.0               667.0   \n",
       "3               455.0               452.0               453.0   \n",
       "4               127.0               125.0               123.0   \n",
       "\n",
       "   2010년09월_계_연령구간인구수  ...  2019년03월_계_연령구간인구수  2019년04월_계_연령구간인구수  \\\n",
       "0            502054.0  ...            386601.0            384909.0   \n",
       "1              6259.0  ...              4635.0              4618.0   \n",
       "2               656.0  ...               499.0               497.0   \n",
       "3               452.0  ...               354.0               349.0   \n",
       "4               120.0  ...                70.0                69.0   \n",
       "\n",
       "   2019년05월_계_연령구간인구수  2019년06월_계_연령구간인구수  2019년07월_계_연령구간인구수  \\\n",
       "0            383271.0            381399.0            379415.0   \n",
       "1              4589.0              4544.0              4534.0   \n",
       "2               492.0               483.0               493.0   \n",
       "3               346.0               340.0               336.0   \n",
       "4                67.0                67.0                63.0   \n",
       "\n",
       "   2019년08월_계_연령구간인구수  2019년09월_계_연령구간인구수  2019년10월_계_연령구간인구수  \\\n",
       "0            376635.0            374490.0            372892.0   \n",
       "1              4524.0              4502.0              4469.0   \n",
       "2               493.0               491.0               486.0   \n",
       "3               338.0               337.0               336.0   \n",
       "4                61.0                62.0                61.0   \n",
       "\n",
       "   2019년11월_계_연령구간인구수  2019년12월_계_연령구간인구수  \n",
       "0            371110.0            369938.0  \n",
       "1              4442.0              4417.0  \n",
       "2               487.0               481.0  \n",
       "3               337.0               343.0  \n",
       "4                57.0                57.0  \n",
       "\n",
       "[5 rows x 121 columns]"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 머지완료한 파일 불러오기\n",
    "pop_merge = pd.read_csv('./data/pop_merge.csv', thousands = ',')\n",
    "pop_merge.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>행정구역</th>\n",
       "      <th>2010년01월_계_연령구간인구수</th>\n",
       "      <th>2010년02월_계_연령구간인구수</th>\n",
       "      <th>2010년03월_계_연령구간인구수</th>\n",
       "      <th>2010년04월_계_연령구간인구수</th>\n",
       "      <th>2010년05월_계_연령구간인구수</th>\n",
       "      <th>2010년06월_계_연령구간인구수</th>\n",
       "      <th>2010년07월_계_연령구간인구수</th>\n",
       "      <th>2010년08월_계_연령구간인구수</th>\n",
       "      <th>2010년09월_계_연령구간인구수</th>\n",
       "      <th>...</th>\n",
       "      <th>2019년03월_계_연령구간인구수</th>\n",
       "      <th>2019년04월_계_연령구간인구수</th>\n",
       "      <th>2019년05월_계_연령구간인구수</th>\n",
       "      <th>2019년06월_계_연령구간인구수</th>\n",
       "      <th>2019년07월_계_연령구간인구수</th>\n",
       "      <th>2019년08월_계_연령구간인구수</th>\n",
       "      <th>2019년09월_계_연령구간인구수</th>\n",
       "      <th>2019년10월_계_연령구간인구수</th>\n",
       "      <th>2019년11월_계_연령구간인구수</th>\n",
       "      <th>2019년12월_계_연령구간인구수</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>서울특별시  (1100000000)</td>\n",
       "      <td>509764.0</td>\n",
       "      <td>508535.0</td>\n",
       "      <td>506755.0</td>\n",
       "      <td>505627.0</td>\n",
       "      <td>504480.0</td>\n",
       "      <td>503991.0</td>\n",
       "      <td>503200.0</td>\n",
       "      <td>502690.0</td>\n",
       "      <td>502054.0</td>\n",
       "      <td>...</td>\n",
       "      <td>386601.0</td>\n",
       "      <td>384909.0</td>\n",
       "      <td>383271.0</td>\n",
       "      <td>381399.0</td>\n",
       "      <td>379415.0</td>\n",
       "      <td>376635.0</td>\n",
       "      <td>374490.0</td>\n",
       "      <td>372892.0</td>\n",
       "      <td>371110.0</td>\n",
       "      <td>369938.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>서울특별시 종로구 (1111000000)</td>\n",
       "      <td>6420.0</td>\n",
       "      <td>6416.0</td>\n",
       "      <td>6372.0</td>\n",
       "      <td>6388.0</td>\n",
       "      <td>6348.0</td>\n",
       "      <td>6354.0</td>\n",
       "      <td>6311.0</td>\n",
       "      <td>6285.0</td>\n",
       "      <td>6259.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4635.0</td>\n",
       "      <td>4618.0</td>\n",
       "      <td>4589.0</td>\n",
       "      <td>4544.0</td>\n",
       "      <td>4534.0</td>\n",
       "      <td>4524.0</td>\n",
       "      <td>4502.0</td>\n",
       "      <td>4469.0</td>\n",
       "      <td>4442.0</td>\n",
       "      <td>4417.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>서울특별시 종로구 청운효자동(1111051500)</td>\n",
       "      <td>710.0</td>\n",
       "      <td>709.0</td>\n",
       "      <td>699.0</td>\n",
       "      <td>691.0</td>\n",
       "      <td>684.0</td>\n",
       "      <td>680.0</td>\n",
       "      <td>678.0</td>\n",
       "      <td>667.0</td>\n",
       "      <td>656.0</td>\n",
       "      <td>...</td>\n",
       "      <td>499.0</td>\n",
       "      <td>497.0</td>\n",
       "      <td>492.0</td>\n",
       "      <td>483.0</td>\n",
       "      <td>493.0</td>\n",
       "      <td>493.0</td>\n",
       "      <td>491.0</td>\n",
       "      <td>486.0</td>\n",
       "      <td>487.0</td>\n",
       "      <td>481.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>서울특별시 종로구 사직동(1111053000)</td>\n",
       "      <td>446.0</td>\n",
       "      <td>436.0</td>\n",
       "      <td>437.0</td>\n",
       "      <td>442.0</td>\n",
       "      <td>443.0</td>\n",
       "      <td>455.0</td>\n",
       "      <td>452.0</td>\n",
       "      <td>453.0</td>\n",
       "      <td>452.0</td>\n",
       "      <td>...</td>\n",
       "      <td>354.0</td>\n",
       "      <td>349.0</td>\n",
       "      <td>346.0</td>\n",
       "      <td>340.0</td>\n",
       "      <td>336.0</td>\n",
       "      <td>338.0</td>\n",
       "      <td>337.0</td>\n",
       "      <td>336.0</td>\n",
       "      <td>337.0</td>\n",
       "      <td>343.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>서울특별시 종로구 삼청동(1111054000)</td>\n",
       "      <td>132.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>127.0</td>\n",
       "      <td>127.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>127.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>123.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>...</td>\n",
       "      <td>70.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>57.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 121 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                          행정구역  2010년01월_계_연령구간인구수  2010년02월_계_연령구간인구수  \\\n",
       "0          서울특별시  (1100000000)            509764.0            508535.0   \n",
       "1       서울특별시 종로구 (1111000000)              6420.0              6416.0   \n",
       "2  서울특별시 종로구 청운효자동(1111051500)               710.0               709.0   \n",
       "3    서울특별시 종로구 사직동(1111053000)               446.0               436.0   \n",
       "4    서울특별시 종로구 삼청동(1111054000)               132.0               128.0   \n",
       "\n",
       "   2010년03월_계_연령구간인구수  2010년04월_계_연령구간인구수  2010년05월_계_연령구간인구수  \\\n",
       "0            506755.0            505627.0            504480.0   \n",
       "1              6372.0              6388.0              6348.0   \n",
       "2               699.0               691.0               684.0   \n",
       "3               437.0               442.0               443.0   \n",
       "4               127.0               127.0               125.0   \n",
       "\n",
       "   2010년06월_계_연령구간인구수  2010년07월_계_연령구간인구수  2010년08월_계_연령구간인구수  \\\n",
       "0            503991.0            503200.0            502690.0   \n",
       "1              6354.0              6311.0              6285.0   \n",
       "2               680.0               678.0               667.0   \n",
       "3               455.0               452.0               453.0   \n",
       "4               127.0               125.0               123.0   \n",
       "\n",
       "   2010년09월_계_연령구간인구수  ...  2019년03월_계_연령구간인구수  2019년04월_계_연령구간인구수  \\\n",
       "0            502054.0  ...            386601.0            384909.0   \n",
       "1              6259.0  ...              4635.0              4618.0   \n",
       "2               656.0  ...               499.0               497.0   \n",
       "3               452.0  ...               354.0               349.0   \n",
       "4               120.0  ...                70.0                69.0   \n",
       "\n",
       "   2019년05월_계_연령구간인구수  2019년06월_계_연령구간인구수  2019년07월_계_연령구간인구수  \\\n",
       "0            383271.0            381399.0            379415.0   \n",
       "1              4589.0              4544.0              4534.0   \n",
       "2               492.0               483.0               493.0   \n",
       "3               346.0               340.0               336.0   \n",
       "4                67.0                67.0                63.0   \n",
       "\n",
       "   2019년08월_계_연령구간인구수  2019년09월_계_연령구간인구수  2019년10월_계_연령구간인구수  \\\n",
       "0            376635.0            374490.0            372892.0   \n",
       "1              4524.0              4502.0              4469.0   \n",
       "2               493.0               491.0               486.0   \n",
       "3               338.0               337.0               336.0   \n",
       "4                61.0                62.0                61.0   \n",
       "\n",
       "   2019년11월_계_연령구간인구수  2019년12월_계_연령구간인구수  \n",
       "0            371110.0            369938.0  \n",
       "1              4442.0              4417.0  \n",
       "2               487.0               481.0  \n",
       "3               337.0               343.0  \n",
       "4                57.0                57.0  \n",
       "\n",
       "[5 rows x 121 columns]"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# nan값 확인 후 nan값을 0으로 대체\n",
    "# nan값이 2010년에는 있고, 2019년에는 없음 => 아마 새로생긴 행정구역이 아닐까?\n",
    "pop_merge.isnull()\n",
    "pop_merge.fillna(0, inplace=True)\n",
    "pop_merge.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['2010-01',\n",
       " '2010-02',\n",
       " '2010-03',\n",
       " '2010-04',\n",
       " '2010-05',\n",
       " '2010-06',\n",
       " '2010-07',\n",
       " '2010-08',\n",
       " '2010-09',\n",
       " '2010-10',\n",
       " '2010-11',\n",
       " '2010-12',\n",
       " '2011-01',\n",
       " '2011-02',\n",
       " '2011-03',\n",
       " '2011-04',\n",
       " '2011-05',\n",
       " '2011-06',\n",
       " '2011-07',\n",
       " '2011-08',\n",
       " '2011-09',\n",
       " '2011-10',\n",
       " '2011-11',\n",
       " '2011-12',\n",
       " '2012-01',\n",
       " '2012-02',\n",
       " '2012-03',\n",
       " '2012-04',\n",
       " '2012-05',\n",
       " '2012-06',\n",
       " '2012-07',\n",
       " '2012-08',\n",
       " '2012-09',\n",
       " '2012-10',\n",
       " '2012-11',\n",
       " '2012-12',\n",
       " '2013-01',\n",
       " '2013-02',\n",
       " '2013-03',\n",
       " '2013-04',\n",
       " '2013-05',\n",
       " '2013-06',\n",
       " '2013-07',\n",
       " '2013-08',\n",
       " '2013-09',\n",
       " '2013-10',\n",
       " '2013-11',\n",
       " '2013-12',\n",
       " '2014-01',\n",
       " '2014-02',\n",
       " '2014-03',\n",
       " '2014-04',\n",
       " '2014-05',\n",
       " '2014-06',\n",
       " '2014-07',\n",
       " '2014-08',\n",
       " '2014-09',\n",
       " '2014-10',\n",
       " '2014-11',\n",
       " '2014-12',\n",
       " '2015-01',\n",
       " '2015-02',\n",
       " '2015-03',\n",
       " '2015-04',\n",
       " '2015-05',\n",
       " '2015-06',\n",
       " '2015-07',\n",
       " '2015-08',\n",
       " '2015-09',\n",
       " '2015-10',\n",
       " '2015-11',\n",
       " '2015-12',\n",
       " '2016-01',\n",
       " '2016-02',\n",
       " '2016-03',\n",
       " '2016-04',\n",
       " '2016-05',\n",
       " '2016-06',\n",
       " '2016-07',\n",
       " '2016-08',\n",
       " '2016-09',\n",
       " '2016-10',\n",
       " '2016-11',\n",
       " '2016-12',\n",
       " '2017-01',\n",
       " '2017-02',\n",
       " '2017-03',\n",
       " '2017-04',\n",
       " '2017-05',\n",
       " '2017-06',\n",
       " '2017-07',\n",
       " '2017-08',\n",
       " '2017-09',\n",
       " '2017-10',\n",
       " '2017-11',\n",
       " '2017-12',\n",
       " '2018-01',\n",
       " '2018-02',\n",
       " '2018-03',\n",
       " '2018-04',\n",
       " '2018-05',\n",
       " '2018-06',\n",
       " '2018-07',\n",
       " '2018-08',\n",
       " '2018-09',\n",
       " '2018-10',\n",
       " '2018-11',\n",
       " '2018-12',\n",
       " '2019-01',\n",
       " '2019-02',\n",
       " '2019-03',\n",
       " '2019-04',\n",
       " '2019-05',\n",
       " '2019-06',\n",
       " '2019-07',\n",
       " '2019-08',\n",
       " '2019-09',\n",
       " '2019-10',\n",
       " '2019-11',\n",
       " '2019-12']"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 바꿔줄 칼럼명 생성\n",
    "cols = []\n",
    "for y in range(2010,2020):\n",
    "    for m in range(1,13):\n",
    "        if m >= 10:\n",
    "            cols.append(f'{y}-{m}')\n",
    "        else:\n",
    "            cols.append(f'{y}-0{m}')\n",
    "\n",
    "cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "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>행정구역</th>\n",
       "      <th>2010-01</th>\n",
       "      <th>2010-02</th>\n",
       "      <th>2010-03</th>\n",
       "      <th>2010-04</th>\n",
       "      <th>2010-05</th>\n",
       "      <th>2010-06</th>\n",
       "      <th>2010-07</th>\n",
       "      <th>2010-08</th>\n",
       "      <th>2010-09</th>\n",
       "      <th>...</th>\n",
       "      <th>2019-03</th>\n",
       "      <th>2019-04</th>\n",
       "      <th>2019-05</th>\n",
       "      <th>2019-06</th>\n",
       "      <th>2019-07</th>\n",
       "      <th>2019-08</th>\n",
       "      <th>2019-09</th>\n",
       "      <th>2019-10</th>\n",
       "      <th>2019-11</th>\n",
       "      <th>2019-12</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>서울특별시  (1100000000)</td>\n",
       "      <td>509764.0</td>\n",
       "      <td>508535.0</td>\n",
       "      <td>506755.0</td>\n",
       "      <td>505627.0</td>\n",
       "      <td>504480.0</td>\n",
       "      <td>503991.0</td>\n",
       "      <td>503200.0</td>\n",
       "      <td>502690.0</td>\n",
       "      <td>502054.0</td>\n",
       "      <td>...</td>\n",
       "      <td>386601.0</td>\n",
       "      <td>384909.0</td>\n",
       "      <td>383271.0</td>\n",
       "      <td>381399.0</td>\n",
       "      <td>379415.0</td>\n",
       "      <td>376635.0</td>\n",
       "      <td>374490.0</td>\n",
       "      <td>372892.0</td>\n",
       "      <td>371110.0</td>\n",
       "      <td>369938.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>서울특별시 종로구 (1111000000)</td>\n",
       "      <td>6420.0</td>\n",
       "      <td>6416.0</td>\n",
       "      <td>6372.0</td>\n",
       "      <td>6388.0</td>\n",
       "      <td>6348.0</td>\n",
       "      <td>6354.0</td>\n",
       "      <td>6311.0</td>\n",
       "      <td>6285.0</td>\n",
       "      <td>6259.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4635.0</td>\n",
       "      <td>4618.0</td>\n",
       "      <td>4589.0</td>\n",
       "      <td>4544.0</td>\n",
       "      <td>4534.0</td>\n",
       "      <td>4524.0</td>\n",
       "      <td>4502.0</td>\n",
       "      <td>4469.0</td>\n",
       "      <td>4442.0</td>\n",
       "      <td>4417.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 121 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     행정구역   2010-01   2010-02   2010-03   2010-04   2010-05  \\\n",
       "0     서울특별시  (1100000000)  509764.0  508535.0  506755.0  505627.0  504480.0   \n",
       "1  서울특별시 종로구 (1111000000)    6420.0    6416.0    6372.0    6388.0    6348.0   \n",
       "\n",
       "    2010-06   2010-07   2010-08   2010-09  ...   2019-03   2019-04   2019-05  \\\n",
       "0  503991.0  503200.0  502690.0  502054.0  ...  386601.0  384909.0  383271.0   \n",
       "1    6354.0    6311.0    6285.0    6259.0  ...    4635.0    4618.0    4589.0   \n",
       "\n",
       "    2019-06   2019-07   2019-08   2019-09   2019-10   2019-11   2019-12  \n",
       "0  381399.0  379415.0  376635.0  374490.0  372892.0  371110.0  369938.0  \n",
       "1    4544.0    4534.0    4524.0    4502.0    4469.0    4442.0    4417.0  \n",
       "\n",
       "[2 rows x 121 columns]"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 수정안할 칼럼과 병합\n",
    "fcol = pop_merge.columns[:1].values.tolist()\n",
    "fcol.extend(cols)\n",
    "# 전체칼럼 수정\n",
    "pop_merge.columns = fcol\n",
    "pop_merge.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "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>행정구역</th>\n",
       "      <th>2010-01</th>\n",
       "      <th>2010-02</th>\n",
       "      <th>2010-03</th>\n",
       "      <th>2010-04</th>\n",
       "      <th>2010-05</th>\n",
       "      <th>2010-06</th>\n",
       "      <th>2010-07</th>\n",
       "      <th>2010-08</th>\n",
       "      <th>2010-09</th>\n",
       "      <th>...</th>\n",
       "      <th>2019-03</th>\n",
       "      <th>2019-04</th>\n",
       "      <th>2019-05</th>\n",
       "      <th>2019-06</th>\n",
       "      <th>2019-07</th>\n",
       "      <th>2019-08</th>\n",
       "      <th>2019-09</th>\n",
       "      <th>2019-10</th>\n",
       "      <th>2019-11</th>\n",
       "      <th>2019-12</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>서울특별시 종로구 (1111000000)</td>\n",
       "      <td>6420.0</td>\n",
       "      <td>6416.0</td>\n",
       "      <td>6372.0</td>\n",
       "      <td>6388.0</td>\n",
       "      <td>6348.0</td>\n",
       "      <td>6354.0</td>\n",
       "      <td>6311.0</td>\n",
       "      <td>6285.0</td>\n",
       "      <td>6259.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4635.0</td>\n",
       "      <td>4618.0</td>\n",
       "      <td>4589.0</td>\n",
       "      <td>4544.0</td>\n",
       "      <td>4534.0</td>\n",
       "      <td>4524.0</td>\n",
       "      <td>4502.0</td>\n",
       "      <td>4469.0</td>\n",
       "      <td>4442.0</td>\n",
       "      <td>4417.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>서울특별시 종로구 청운효자동(1111051500)</td>\n",
       "      <td>710.0</td>\n",
       "      <td>709.0</td>\n",
       "      <td>699.0</td>\n",
       "      <td>691.0</td>\n",
       "      <td>684.0</td>\n",
       "      <td>680.0</td>\n",
       "      <td>678.0</td>\n",
       "      <td>667.0</td>\n",
       "      <td>656.0</td>\n",
       "      <td>...</td>\n",
       "      <td>499.0</td>\n",
       "      <td>497.0</td>\n",
       "      <td>492.0</td>\n",
       "      <td>483.0</td>\n",
       "      <td>493.0</td>\n",
       "      <td>493.0</td>\n",
       "      <td>491.0</td>\n",
       "      <td>486.0</td>\n",
       "      <td>487.0</td>\n",
       "      <td>481.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 121 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                          행정구역  2010-01  2010-02  2010-03  2010-04  2010-05  \\\n",
       "1       서울특별시 종로구 (1111000000)   6420.0   6416.0   6372.0   6388.0   6348.0   \n",
       "2  서울특별시 종로구 청운효자동(1111051500)    710.0    709.0    699.0    691.0    684.0   \n",
       "\n",
       "   2010-06  2010-07  2010-08  2010-09  ...  2019-03  2019-04  2019-05  \\\n",
       "1   6354.0   6311.0   6285.0   6259.0  ...   4635.0   4618.0   4589.0   \n",
       "2    680.0    678.0    667.0    656.0  ...    499.0    497.0    492.0   \n",
       "\n",
       "   2019-06  2019-07  2019-08  2019-09  2019-10  2019-11  2019-12  \n",
       "1   4544.0   4534.0   4524.0   4502.0   4469.0   4442.0   4417.0  \n",
       "2    483.0    493.0    493.0    491.0    486.0    487.0    481.0  \n",
       "\n",
       "[2 rows x 121 columns]"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 서울특별시 로우삭제\n",
    "pop_merge = pop_merge.drop([pop_merge.index[0]])\n",
    "pop_merge.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>행정구역</th>\n",
       "      <th>서울특별시 종로구 (1111000000)</th>\n",
       "      <th>서울특별시 종로구 청운효자동(1111051500)</th>\n",
       "      <th>서울특별시 종로구 사직동(1111053000)</th>\n",
       "      <th>서울특별시 종로구 삼청동(1111054000)</th>\n",
       "      <th>서울특별시 종로구 부암동(1111055000)</th>\n",
       "      <th>서울특별시 종로구 평창동(1111056000)</th>\n",
       "      <th>서울특별시 종로구 무악동(1111057000)</th>\n",
       "      <th>서울특별시 종로구 교남동(1111058000)</th>\n",
       "      <th>서울특별시 종로구 가회동(1111060000)</th>\n",
       "      <th>서울특별시 종로구 종로1.2.3.4가동(1111061500)</th>\n",
       "      <th>...</th>\n",
       "      <th>경기도 부천시 대산동(4119074600)</th>\n",
       "      <th>경기도 부천시 범안동(4119079500)</th>\n",
       "      <th>경기도 화성시 동탄7동(4159061000)</th>\n",
       "      <th>충청북도 진천군 덕산읍(4375025300)</th>\n",
       "      <th>경기도 평택시 용이동(4122063500)</th>\n",
       "      <th>경기도 김포시 마산동(4157057500)</th>\n",
       "      <th>경기도 남양주시 퇴계원읍(4136026500)</th>\n",
       "      <th>경기도 화성시 동탄8동(4159062000)</th>\n",
       "      <th>전라북도 전주시 덕진구 여의동(4511366500)</th>\n",
       "      <th>인천광역시 서구 마전동(2826073000)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-01</th>\n",
       "      <td>6420.0</td>\n",
       "      <td>710.0</td>\n",
       "      <td>446.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>334.0</td>\n",
       "      <td>724.0</td>\n",
       "      <td>442.0</td>\n",
       "      <td>371.0</td>\n",
       "      <td>202.0</td>\n",
       "      <td>187.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-02</th>\n",
       "      <td>6416.0</td>\n",
       "      <td>709.0</td>\n",
       "      <td>436.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>331.0</td>\n",
       "      <td>734.0</td>\n",
       "      <td>450.0</td>\n",
       "      <td>375.0</td>\n",
       "      <td>202.0</td>\n",
       "      <td>181.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-03</th>\n",
       "      <td>6372.0</td>\n",
       "      <td>699.0</td>\n",
       "      <td>437.0</td>\n",
       "      <td>127.0</td>\n",
       "      <td>333.0</td>\n",
       "      <td>722.0</td>\n",
       "      <td>449.0</td>\n",
       "      <td>379.0</td>\n",
       "      <td>197.0</td>\n",
       "      <td>171.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-04</th>\n",
       "      <td>6388.0</td>\n",
       "      <td>691.0</td>\n",
       "      <td>442.0</td>\n",
       "      <td>127.0</td>\n",
       "      <td>340.0</td>\n",
       "      <td>722.0</td>\n",
       "      <td>444.0</td>\n",
       "      <td>384.0</td>\n",
       "      <td>198.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-05</th>\n",
       "      <td>6348.0</td>\n",
       "      <td>684.0</td>\n",
       "      <td>443.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>340.0</td>\n",
       "      <td>727.0</td>\n",
       "      <td>447.0</td>\n",
       "      <td>378.0</td>\n",
       "      <td>194.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 4191 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "행정구역     서울특별시 종로구 (1111000000)  서울특별시 종로구 청운효자동(1111051500)  \\\n",
       "2010-01                  6420.0                        710.0   \n",
       "2010-02                  6416.0                        709.0   \n",
       "2010-03                  6372.0                        699.0   \n",
       "2010-04                  6388.0                        691.0   \n",
       "2010-05                  6348.0                        684.0   \n",
       "\n",
       "행정구역     서울특별시 종로구 사직동(1111053000)  서울특별시 종로구 삼청동(1111054000)  \\\n",
       "2010-01                      446.0                      132.0   \n",
       "2010-02                      436.0                      128.0   \n",
       "2010-03                      437.0                      127.0   \n",
       "2010-04                      442.0                      127.0   \n",
       "2010-05                      443.0                      125.0   \n",
       "\n",
       "행정구역     서울특별시 종로구 부암동(1111055000)  서울특별시 종로구 평창동(1111056000)  \\\n",
       "2010-01                      334.0                      724.0   \n",
       "2010-02                      331.0                      734.0   \n",
       "2010-03                      333.0                      722.0   \n",
       "2010-04                      340.0                      722.0   \n",
       "2010-05                      340.0                      727.0   \n",
       "\n",
       "행정구역     서울특별시 종로구 무악동(1111057000)  서울특별시 종로구 교남동(1111058000)  \\\n",
       "2010-01                      442.0                      371.0   \n",
       "2010-02                      450.0                      375.0   \n",
       "2010-03                      449.0                      379.0   \n",
       "2010-04                      444.0                      384.0   \n",
       "2010-05                      447.0                      378.0   \n",
       "\n",
       "행정구역     서울특별시 종로구 가회동(1111060000)  서울특별시 종로구 종로1.2.3.4가동(1111061500)  ...  \\\n",
       "2010-01                      202.0                              187.0  ...   \n",
       "2010-02                      202.0                              181.0  ...   \n",
       "2010-03                      197.0                              171.0  ...   \n",
       "2010-04                      198.0                              172.0  ...   \n",
       "2010-05                      194.0                              170.0  ...   \n",
       "\n",
       "행정구역     경기도 부천시 대산동(4119074600)  경기도 부천시 범안동(4119079500)  \\\n",
       "2010-01                      0.0                      0.0   \n",
       "2010-02                      0.0                      0.0   \n",
       "2010-03                      0.0                      0.0   \n",
       "2010-04                      0.0                      0.0   \n",
       "2010-05                      0.0                      0.0   \n",
       "\n",
       "행정구역     경기도 화성시 동탄7동(4159061000)  충청북도 진천군 덕산읍(4375025300)  \\\n",
       "2010-01                       0.0                       0.0   \n",
       "2010-02                       0.0                       0.0   \n",
       "2010-03                       0.0                       0.0   \n",
       "2010-04                       0.0                       0.0   \n",
       "2010-05                       0.0                       0.0   \n",
       "\n",
       "행정구역     경기도 평택시 용이동(4122063500)  경기도 김포시 마산동(4157057500)  \\\n",
       "2010-01                      0.0                      0.0   \n",
       "2010-02                      0.0                      0.0   \n",
       "2010-03                      0.0                      0.0   \n",
       "2010-04                      0.0                      0.0   \n",
       "2010-05                      0.0                      0.0   \n",
       "\n",
       "행정구역     경기도 남양주시 퇴계원읍(4136026500)  경기도 화성시 동탄8동(4159062000)  \\\n",
       "2010-01                        0.0                       0.0   \n",
       "2010-02                        0.0                       0.0   \n",
       "2010-03                        0.0                       0.0   \n",
       "2010-04                        0.0                       0.0   \n",
       "2010-05                        0.0                       0.0   \n",
       "\n",
       "행정구역     전라북도 전주시 덕진구 여의동(4511366500)  인천광역시 서구 마전동(2826073000)  \n",
       "2010-01                           0.0                       0.0  \n",
       "2010-02                           0.0                       0.0  \n",
       "2010-03                           0.0                       0.0  \n",
       "2010-04                           0.0                       0.0  \n",
       "2010-05                           0.0                       0.0  \n",
       "\n",
       "[5 rows x 4191 columns]"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#인덱스설정 및 전치행렬\n",
    "pop_merge = pop_merge.set_index('행정구역')\n",
    "pop_merge = pop_merge.T\n",
    "pop_merge.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 날짜 추가\n",
    "# pop_merge['date'] = pop_merge.index\n",
    "# pop_merge['date'] = pop_merge['date'].astype(date)\n",
    "# pop_merge.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>행정구역</th>\n",
       "      <th>서울특별시 종로구 (1111000000)</th>\n",
       "      <th>서울특별시 종로구 청운효자동(1111051500)</th>\n",
       "      <th>서울특별시 종로구 사직동(1111053000)</th>\n",
       "      <th>서울특별시 종로구 삼청동(1111054000)</th>\n",
       "      <th>서울특별시 종로구 부암동(1111055000)</th>\n",
       "      <th>서울특별시 종로구 평창동(1111056000)</th>\n",
       "      <th>서울특별시 종로구 무악동(1111057000)</th>\n",
       "      <th>서울특별시 종로구 교남동(1111058000)</th>\n",
       "      <th>서울특별시 종로구 가회동(1111060000)</th>\n",
       "      <th>서울특별시 종로구 종로1.2.3.4가동(1111061500)</th>\n",
       "      <th>...</th>\n",
       "      <th>서울특별시 중구 약수동(1114063500)</th>\n",
       "      <th>서울특별시 중구 청구동(1114064500)</th>\n",
       "      <th>서울특별시 중구 동화동(1114066500)</th>\n",
       "      <th>서울특별시 송파구 위례동(1171064700)</th>\n",
       "      <th>서울특별시 강북구 번1동(1130559500)</th>\n",
       "      <th>서울특별시 강북구 번2동(1130560300)</th>\n",
       "      <th>서울특별시 강북구 번3동(1130560800)</th>\n",
       "      <th>서울특별시 강북구 수유1동(1130561500)</th>\n",
       "      <th>서울특별시 강북구 수유2동(1130562500)</th>\n",
       "      <th>서울특별시 강북구 수유3동(1130563500)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-01</th>\n",
       "      <td>6420.0</td>\n",
       "      <td>710.0</td>\n",
       "      <td>446.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>334.0</td>\n",
       "      <td>724.0</td>\n",
       "      <td>442.0</td>\n",
       "      <td>371.0</td>\n",
       "      <td>202.0</td>\n",
       "      <td>187.0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2010-02</th>\n",
       "      <td>6416.0</td>\n",
       "      <td>709.0</td>\n",
       "      <td>436.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>331.0</td>\n",
       "      <td>734.0</td>\n",
       "      <td>450.0</td>\n",
       "      <td>375.0</td>\n",
       "      <td>202.0</td>\n",
       "      <td>181.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-03</th>\n",
       "      <td>6372.0</td>\n",
       "      <td>699.0</td>\n",
       "      <td>437.0</td>\n",
       "      <td>127.0</td>\n",
       "      <td>333.0</td>\n",
       "      <td>722.0</td>\n",
       "      <td>449.0</td>\n",
       "      <td>379.0</td>\n",
       "      <td>197.0</td>\n",
       "      <td>171.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-04</th>\n",
       "      <td>6388.0</td>\n",
       "      <td>691.0</td>\n",
       "      <td>442.0</td>\n",
       "      <td>127.0</td>\n",
       "      <td>340.0</td>\n",
       "      <td>722.0</td>\n",
       "      <td>444.0</td>\n",
       "      <td>384.0</td>\n",
       "      <td>198.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-05</th>\n",
       "      <td>6348.0</td>\n",
       "      <td>684.0</td>\n",
       "      <td>443.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>340.0</td>\n",
       "      <td>727.0</td>\n",
       "      <td>447.0</td>\n",
       "      <td>378.0</td>\n",
       "      <td>194.0</td>\n",
       "      <td>170.0</td>\n",
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       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 462 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "행정구역     서울특별시 종로구 (1111000000)  서울특별시 종로구 청운효자동(1111051500)  \\\n",
       "2010-01                  6420.0                        710.0   \n",
       "2010-02                  6416.0                        709.0   \n",
       "2010-03                  6372.0                        699.0   \n",
       "2010-04                  6388.0                        691.0   \n",
       "2010-05                  6348.0                        684.0   \n",
       "\n",
       "행정구역     서울특별시 종로구 사직동(1111053000)  서울특별시 종로구 삼청동(1111054000)  \\\n",
       "2010-01                      446.0                      132.0   \n",
       "2010-02                      436.0                      128.0   \n",
       "2010-03                      437.0                      127.0   \n",
       "2010-04                      442.0                      127.0   \n",
       "2010-05                      443.0                      125.0   \n",
       "\n",
       "행정구역     서울특별시 종로구 부암동(1111055000)  서울특별시 종로구 평창동(1111056000)  \\\n",
       "2010-01                      334.0                      724.0   \n",
       "2010-02                      331.0                      734.0   \n",
       "2010-03                      333.0                      722.0   \n",
       "2010-04                      340.0                      722.0   \n",
       "2010-05                      340.0                      727.0   \n",
       "\n",
       "행정구역     서울특별시 종로구 무악동(1111057000)  서울특별시 종로구 교남동(1111058000)  \\\n",
       "2010-01                      442.0                      371.0   \n",
       "2010-02                      450.0                      375.0   \n",
       "2010-03                      449.0                      379.0   \n",
       "2010-04                      444.0                      384.0   \n",
       "2010-05                      447.0                      378.0   \n",
       "\n",
       "행정구역     서울특별시 종로구 가회동(1111060000)  서울특별시 종로구 종로1.2.3.4가동(1111061500)  ...  \\\n",
       "2010-01                      202.0                              187.0  ...   \n",
       "2010-02                      202.0                              181.0  ...   \n",
       "2010-03                      197.0                              171.0  ...   \n",
       "2010-04                      198.0                              172.0  ...   \n",
       "2010-05                      194.0                              170.0  ...   \n",
       "\n",
       "행정구역     서울특별시 중구 약수동(1114063500)  서울특별시 중구 청구동(1114064500)  \\\n",
       "2010-01                       0.0                       0.0   \n",
       "2010-02                       0.0                       0.0   \n",
       "2010-03                       0.0                       0.0   \n",
       "2010-04                       0.0                       0.0   \n",
       "2010-05                       0.0                       0.0   \n",
       "\n",
       "행정구역     서울특별시 중구 동화동(1114066500)  서울특별시 송파구 위례동(1171064700)  \\\n",
       "2010-01                       0.0                        0.0   \n",
       "2010-02                       0.0                        0.0   \n",
       "2010-03                       0.0                        0.0   \n",
       "2010-04                       0.0                        0.0   \n",
       "2010-05                       0.0                        0.0   \n",
       "\n",
       "행정구역     서울특별시 강북구 번1동(1130559500)  서울특별시 강북구 번2동(1130560300)  \\\n",
       "2010-01                        0.0                        0.0   \n",
       "2010-02                        0.0                        0.0   \n",
       "2010-03                        0.0                        0.0   \n",
       "2010-04                        0.0                        0.0   \n",
       "2010-05                        0.0                        0.0   \n",
       "\n",
       "행정구역     서울특별시 강북구 번3동(1130560800)  서울특별시 강북구 수유1동(1130561500)  \\\n",
       "2010-01                        0.0                         0.0   \n",
       "2010-02                        0.0                         0.0   \n",
       "2010-03                        0.0                         0.0   \n",
       "2010-04                        0.0                         0.0   \n",
       "2010-05                        0.0                         0.0   \n",
       "\n",
       "행정구역     서울특별시 강북구 수유2동(1130562500)  서울특별시 강북구 수유3동(1130563500)  \n",
       "2010-01                         0.0                         0.0  \n",
       "2010-02                         0.0                         0.0  \n",
       "2010-03                         0.0                         0.0  \n",
       "2010-04                         0.0                         0.0  \n",
       "2010-05                         0.0                         0.0  \n",
       "\n",
       "[5 rows x 462 columns]"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 서울지역, 자치구만 남기고 drop\n",
    "\n",
    "for new in pop_merge.columns:\n",
    "    if new.split(' ')[0] != '서울특별시':\n",
    "        pop_merge.drop(f'{new}',1, inplace=True)\n",
    "pop_merge.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>행정구역</th>\n",
       "      <th>서울특별시 종로구 (1111000000)</th>\n",
       "      <th>서울특별시 중구 (1114000000)</th>\n",
       "      <th>서울특별시 용산구 (1117000000)</th>\n",
       "      <th>서울특별시 성동구 (1120000000)</th>\n",
       "      <th>서울특별시 광진구 (1121500000)</th>\n",
       "      <th>서울특별시 동대문구 (1123000000)</th>\n",
       "      <th>서울특별시 중랑구 (1126000000)</th>\n",
       "      <th>서울특별시 성북구 (1129000000)</th>\n",
       "      <th>서울특별시 강북구 (1130500000)</th>\n",
       "      <th>서울특별시 도봉구 (1132000000)</th>\n",
       "      <th>...</th>\n",
       "      <th>서울특별시 강서구 (1150000000)</th>\n",
       "      <th>서울특별시 구로구 (1153000000)</th>\n",
       "      <th>서울특별시 금천구 (1154500000)</th>\n",
       "      <th>서울특별시 영등포구 (1156000000)</th>\n",
       "      <th>서울특별시 동작구 (1159000000)</th>\n",
       "      <th>서울특별시 관악구 (1162000000)</th>\n",
       "      <th>서울특별시 서초구 (1165000000)</th>\n",
       "      <th>서울특별시 강남구 (1168000000)</th>\n",
       "      <th>서울특별시 송파구 (1171000000)</th>\n",
       "      <th>서울특별시 강동구 (1174000000)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-01</th>\n",
       "      <td>6420.0</td>\n",
       "      <td>5725.0</td>\n",
       "      <td>11797.0</td>\n",
       "      <td>16416.0</td>\n",
       "      <td>17564.0</td>\n",
       "      <td>16889.0</td>\n",
       "      <td>19965.0</td>\n",
       "      <td>25735.0</td>\n",
       "      <td>15480.0</td>\n",
       "      <td>17426.0</td>\n",
       "      <td>...</td>\n",
       "      <td>32152.0</td>\n",
       "      <td>24889.0</td>\n",
       "      <td>11604.0</td>\n",
       "      <td>22597.0</td>\n",
       "      <td>20383.0</td>\n",
       "      <td>25387.0</td>\n",
       "      <td>21112.0</td>\n",
       "      <td>23460.0</td>\n",
       "      <td>35186.0</td>\n",
       "      <td>24642.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-02</th>\n",
       "      <td>6416.0</td>\n",
       "      <td>5721.0</td>\n",
       "      <td>11732.0</td>\n",
       "      <td>16328.0</td>\n",
       "      <td>17518.0</td>\n",
       "      <td>16796.0</td>\n",
       "      <td>20036.0</td>\n",
       "      <td>25625.0</td>\n",
       "      <td>15407.0</td>\n",
       "      <td>17338.0</td>\n",
       "      <td>...</td>\n",
       "      <td>31992.0</td>\n",
       "      <td>24800.0</td>\n",
       "      <td>11529.0</td>\n",
       "      <td>22534.0</td>\n",
       "      <td>20352.0</td>\n",
       "      <td>25233.0</td>\n",
       "      <td>21125.0</td>\n",
       "      <td>23531.0</td>\n",
       "      <td>35195.0</td>\n",
       "      <td>24714.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-03</th>\n",
       "      <td>6372.0</td>\n",
       "      <td>5702.0</td>\n",
       "      <td>11677.0</td>\n",
       "      <td>16267.0</td>\n",
       "      <td>17436.0</td>\n",
       "      <td>16819.0</td>\n",
       "      <td>20038.0</td>\n",
       "      <td>25565.0</td>\n",
       "      <td>15320.0</td>\n",
       "      <td>17248.0</td>\n",
       "      <td>...</td>\n",
       "      <td>31816.0</td>\n",
       "      <td>24687.0</td>\n",
       "      <td>11441.0</td>\n",
       "      <td>22351.0</td>\n",
       "      <td>20205.0</td>\n",
       "      <td>25137.0</td>\n",
       "      <td>21135.0</td>\n",
       "      <td>23511.0</td>\n",
       "      <td>35136.0</td>\n",
       "      <td>24698.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-04</th>\n",
       "      <td>6388.0</td>\n",
       "      <td>5680.0</td>\n",
       "      <td>11584.0</td>\n",
       "      <td>16264.0</td>\n",
       "      <td>17334.0</td>\n",
       "      <td>16865.0</td>\n",
       "      <td>19916.0</td>\n",
       "      <td>25423.0</td>\n",
       "      <td>15273.0</td>\n",
       "      <td>17164.0</td>\n",
       "      <td>...</td>\n",
       "      <td>31711.0</td>\n",
       "      <td>24625.0</td>\n",
       "      <td>11379.0</td>\n",
       "      <td>22281.0</td>\n",
       "      <td>20173.0</td>\n",
       "      <td>25035.0</td>\n",
       "      <td>21224.0</td>\n",
       "      <td>23593.0</td>\n",
       "      <td>35146.0</td>\n",
       "      <td>24750.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-05</th>\n",
       "      <td>6348.0</td>\n",
       "      <td>5683.0</td>\n",
       "      <td>11532.0</td>\n",
       "      <td>16182.0</td>\n",
       "      <td>17312.0</td>\n",
       "      <td>16820.0</td>\n",
       "      <td>19779.0</td>\n",
       "      <td>25425.0</td>\n",
       "      <td>15158.0</td>\n",
       "      <td>17085.0</td>\n",
       "      <td>...</td>\n",
       "      <td>31573.0</td>\n",
       "      <td>24624.0</td>\n",
       "      <td>11341.0</td>\n",
       "      <td>22219.0</td>\n",
       "      <td>20140.0</td>\n",
       "      <td>24945.0</td>\n",
       "      <td>21306.0</td>\n",
       "      <td>23557.0</td>\n",
       "      <td>35147.0</td>\n",
       "      <td>24762.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "행정구역     서울특별시 종로구 (1111000000)  서울특별시 중구 (1114000000)  \\\n",
       "2010-01                  6420.0                 5725.0   \n",
       "2010-02                  6416.0                 5721.0   \n",
       "2010-03                  6372.0                 5702.0   \n",
       "2010-04                  6388.0                 5680.0   \n",
       "2010-05                  6348.0                 5683.0   \n",
       "\n",
       "행정구역     서울특별시 용산구 (1117000000)  서울특별시 성동구 (1120000000)  \\\n",
       "2010-01                 11797.0                 16416.0   \n",
       "2010-02                 11732.0                 16328.0   \n",
       "2010-03                 11677.0                 16267.0   \n",
       "2010-04                 11584.0                 16264.0   \n",
       "2010-05                 11532.0                 16182.0   \n",
       "\n",
       "행정구역     서울특별시 광진구 (1121500000)  서울특별시 동대문구 (1123000000)  \\\n",
       "2010-01                 17564.0                  16889.0   \n",
       "2010-02                 17518.0                  16796.0   \n",
       "2010-03                 17436.0                  16819.0   \n",
       "2010-04                 17334.0                  16865.0   \n",
       "2010-05                 17312.0                  16820.0   \n",
       "\n",
       "행정구역     서울특별시 중랑구 (1126000000)  서울특별시 성북구 (1129000000)  \\\n",
       "2010-01                 19965.0                 25735.0   \n",
       "2010-02                 20036.0                 25625.0   \n",
       "2010-03                 20038.0                 25565.0   \n",
       "2010-04                 19916.0                 25423.0   \n",
       "2010-05                 19779.0                 25425.0   \n",
       "\n",
       "행정구역     서울특별시 강북구 (1130500000)  서울특별시 도봉구 (1132000000)  ...  \\\n",
       "2010-01                 15480.0                 17426.0  ...   \n",
       "2010-02                 15407.0                 17338.0  ...   \n",
       "2010-03                 15320.0                 17248.0  ...   \n",
       "2010-04                 15273.0                 17164.0  ...   \n",
       "2010-05                 15158.0                 17085.0  ...   \n",
       "\n",
       "행정구역     서울특별시 강서구 (1150000000)  서울특별시 구로구 (1153000000)  \\\n",
       "2010-01                 32152.0                 24889.0   \n",
       "2010-02                 31992.0                 24800.0   \n",
       "2010-03                 31816.0                 24687.0   \n",
       "2010-04                 31711.0                 24625.0   \n",
       "2010-05                 31573.0                 24624.0   \n",
       "\n",
       "행정구역     서울특별시 금천구 (1154500000)  서울특별시 영등포구 (1156000000)  \\\n",
       "2010-01                 11604.0                  22597.0   \n",
       "2010-02                 11529.0                  22534.0   \n",
       "2010-03                 11441.0                  22351.0   \n",
       "2010-04                 11379.0                  22281.0   \n",
       "2010-05                 11341.0                  22219.0   \n",
       "\n",
       "행정구역     서울특별시 동작구 (1159000000)  서울특별시 관악구 (1162000000)  \\\n",
       "2010-01                 20383.0                 25387.0   \n",
       "2010-02                 20352.0                 25233.0   \n",
       "2010-03                 20205.0                 25137.0   \n",
       "2010-04                 20173.0                 25035.0   \n",
       "2010-05                 20140.0                 24945.0   \n",
       "\n",
       "행정구역     서울특별시 서초구 (1165000000)  서울특별시 강남구 (1168000000)  \\\n",
       "2010-01                 21112.0                 23460.0   \n",
       "2010-02                 21125.0                 23531.0   \n",
       "2010-03                 21135.0                 23511.0   \n",
       "2010-04                 21224.0                 23593.0   \n",
       "2010-05                 21306.0                 23557.0   \n",
       "\n",
       "행정구역     서울특별시 송파구 (1171000000)  서울특별시 강동구 (1174000000)  \n",
       "2010-01                 35186.0                 24642.0  \n",
       "2010-02                 35195.0                 24714.0  \n",
       "2010-03                 35136.0                 24698.0  \n",
       "2010-04                 35146.0                 24750.0  \n",
       "2010-05                 35147.0                 24762.0  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for new in pop_merge.columns:\n",
    "    if len(new.split(' ')[2]) != 12:\n",
    "        pop_merge.drop(f'{new}',1, inplace=True)\n",
    "pop_merge.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>2010-01</th>\n",
       "      <th>2010-02</th>\n",
       "      <th>2010-03</th>\n",
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       "      <th>2019-09</th>\n",
       "      <th>2019-10</th>\n",
       "      <th>2019-11</th>\n",
       "      <th>2019-12</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>행정구역</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>서울특별시 종로구 (1111000000)</th>\n",
       "      <td>6420.0</td>\n",
       "      <td>6416.0</td>\n",
       "      <td>6372.0</td>\n",
       "      <td>6388.0</td>\n",
       "      <td>6348.0</td>\n",
       "      <td>6354.0</td>\n",
       "      <td>6311.0</td>\n",
       "      <td>6285.0</td>\n",
       "      <td>6259.0</td>\n",
       "      <td>6236.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4635.0</td>\n",
       "      <td>4618.0</td>\n",
       "      <td>4589.0</td>\n",
       "      <td>4544.0</td>\n",
       "      <td>4534.0</td>\n",
       "      <td>4524.0</td>\n",
       "      <td>4502.0</td>\n",
       "      <td>4469.0</td>\n",
       "      <td>4442.0</td>\n",
       "      <td>4417.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>서울특별시 중구 (1114000000)</th>\n",
       "      <td>5725.0</td>\n",
       "      <td>5721.0</td>\n",
       "      <td>5702.0</td>\n",
       "      <td>5680.0</td>\n",
       "      <td>5683.0</td>\n",
       "      <td>5644.0</td>\n",
       "      <td>5628.0</td>\n",
       "      <td>5628.0</td>\n",
       "      <td>5636.0</td>\n",
       "      <td>5661.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4637.0</td>\n",
       "      <td>4610.0</td>\n",
       "      <td>4597.0</td>\n",
       "      <td>4587.0</td>\n",
       "      <td>4587.0</td>\n",
       "      <td>4559.0</td>\n",
       "      <td>4539.0</td>\n",
       "      <td>4504.0</td>\n",
       "      <td>4501.0</td>\n",
       "      <td>4459.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>서울특별시 용산구 (1117000000)</th>\n",
       "      <td>11797.0</td>\n",
       "      <td>11732.0</td>\n",
       "      <td>11677.0</td>\n",
       "      <td>11584.0</td>\n",
       "      <td>11532.0</td>\n",
       "      <td>11555.0</td>\n",
       "      <td>11555.0</td>\n",
       "      <td>11617.0</td>\n",
       "      <td>11630.0</td>\n",
       "      <td>11643.0</td>\n",
       "      <td>...</td>\n",
       "      <td>8496.0</td>\n",
       "      <td>8438.0</td>\n",
       "      <td>8436.0</td>\n",
       "      <td>8424.0</td>\n",
       "      <td>8404.0</td>\n",
       "      <td>8369.0</td>\n",
       "      <td>8300.0</td>\n",
       "      <td>8278.0</td>\n",
       "      <td>8251.0</td>\n",
       "      <td>8256.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>서울특별시 성동구 (1120000000)</th>\n",
       "      <td>16416.0</td>\n",
       "      <td>16328.0</td>\n",
       "      <td>16267.0</td>\n",
       "      <td>16264.0</td>\n",
       "      <td>16182.0</td>\n",
       "      <td>16134.0</td>\n",
       "      <td>16031.0</td>\n",
       "      <td>15989.0</td>\n",
       "      <td>15857.0</td>\n",
       "      <td>15771.0</td>\n",
       "      <td>...</td>\n",
       "      <td>14055.0</td>\n",
       "      <td>13960.0</td>\n",
       "      <td>13924.0</td>\n",
       "      <td>13865.0</td>\n",
       "      <td>13803.0</td>\n",
       "      <td>13723.0</td>\n",
       "      <td>13681.0</td>\n",
       "      <td>13613.0</td>\n",
       "      <td>13551.0</td>\n",
       "      <td>13498.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>서울특별시 광진구 (1121500000)</th>\n",
       "      <td>17564.0</td>\n",
       "      <td>17518.0</td>\n",
       "      <td>17436.0</td>\n",
       "      <td>17334.0</td>\n",
       "      <td>17312.0</td>\n",
       "      <td>17307.0</td>\n",
       "      <td>17233.0</td>\n",
       "      <td>17189.0</td>\n",
       "      <td>17119.0</td>\n",
       "      <td>17082.0</td>\n",
       "      <td>...</td>\n",
       "      <td>13099.0</td>\n",
       "      <td>12955.0</td>\n",
       "      <td>12803.0</td>\n",
       "      <td>12679.0</td>\n",
       "      <td>12570.0</td>\n",
       "      <td>12454.0</td>\n",
       "      <td>12369.0</td>\n",
       "      <td>12265.0</td>\n",
       "      <td>12180.0</td>\n",
       "      <td>12108.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 120 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                        2010-01  2010-02  2010-03  2010-04  2010-05  2010-06  \\\n",
       "행정구역                                                                           \n",
       "서울특별시 종로구 (1111000000)   6420.0   6416.0   6372.0   6388.0   6348.0   6354.0   \n",
       "서울특별시 중구 (1114000000)    5725.0   5721.0   5702.0   5680.0   5683.0   5644.0   \n",
       "서울특별시 용산구 (1117000000)  11797.0  11732.0  11677.0  11584.0  11532.0  11555.0   \n",
       "서울특별시 성동구 (1120000000)  16416.0  16328.0  16267.0  16264.0  16182.0  16134.0   \n",
       "서울특별시 광진구 (1121500000)  17564.0  17518.0  17436.0  17334.0  17312.0  17307.0   \n",
       "\n",
       "                        2010-07  2010-08  2010-09  2010-10  ...  2019-03  \\\n",
       "행정구역                                                        ...            \n",
       "서울특별시 종로구 (1111000000)   6311.0   6285.0   6259.0   6236.0  ...   4635.0   \n",
       "서울특별시 중구 (1114000000)    5628.0   5628.0   5636.0   5661.0  ...   4637.0   \n",
       "서울특별시 용산구 (1117000000)  11555.0  11617.0  11630.0  11643.0  ...   8496.0   \n",
       "서울특별시 성동구 (1120000000)  16031.0  15989.0  15857.0  15771.0  ...  14055.0   \n",
       "서울특별시 광진구 (1121500000)  17233.0  17189.0  17119.0  17082.0  ...  13099.0   \n",
       "\n",
       "                        2019-04  2019-05  2019-06  2019-07  2019-08  2019-09  \\\n",
       "행정구역                                                                           \n",
       "서울특별시 종로구 (1111000000)   4618.0   4589.0   4544.0   4534.0   4524.0   4502.0   \n",
       "서울특별시 중구 (1114000000)    4610.0   4597.0   4587.0   4587.0   4559.0   4539.0   \n",
       "서울특별시 용산구 (1117000000)   8438.0   8436.0   8424.0   8404.0   8369.0   8300.0   \n",
       "서울특별시 성동구 (1120000000)  13960.0  13924.0  13865.0  13803.0  13723.0  13681.0   \n",
       "서울특별시 광진구 (1121500000)  12955.0  12803.0  12679.0  12570.0  12454.0  12369.0   \n",
       "\n",
       "                        2019-10  2019-11  2019-12  \n",
       "행정구역                                               \n",
       "서울특별시 종로구 (1111000000)   4469.0   4442.0   4417.0  \n",
       "서울특별시 중구 (1114000000)    4504.0   4501.0   4459.0  \n",
       "서울특별시 용산구 (1117000000)   8278.0   8251.0   8256.0  \n",
       "서울특별시 성동구 (1120000000)  13613.0  13551.0  13498.0  \n",
       "서울특별시 광진구 (1121500000)  12265.0  12180.0  12108.0  \n",
       "\n",
       "[5 rows x 120 columns]"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pop_gu = pop_merge.T\n",
    "pop_gu.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "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>2010-01</th>\n",
       "      <th>2010-02</th>\n",
       "      <th>2010-03</th>\n",
       "      <th>2010-04</th>\n",
       "      <th>2010-05</th>\n",
       "      <th>2010-06</th>\n",
       "      <th>2010-07</th>\n",
       "      <th>2010-08</th>\n",
       "      <th>2010-09</th>\n",
       "      <th>2010-10</th>\n",
       "      <th>...</th>\n",
       "      <th>2019-03</th>\n",
       "      <th>2019-04</th>\n",
       "      <th>2019-05</th>\n",
       "      <th>2019-06</th>\n",
       "      <th>2019-07</th>\n",
       "      <th>2019-08</th>\n",
       "      <th>2019-09</th>\n",
       "      <th>2019-10</th>\n",
       "      <th>2019-11</th>\n",
       "      <th>2019-12</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gu</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>종로구</th>\n",
       "      <td>6420.0</td>\n",
       "      <td>6416.0</td>\n",
       "      <td>6372.0</td>\n",
       "      <td>6388.0</td>\n",
       "      <td>6348.0</td>\n",
       "      <td>6354.0</td>\n",
       "      <td>6311.0</td>\n",
       "      <td>6285.0</td>\n",
       "      <td>6259.0</td>\n",
       "      <td>6236.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4635.0</td>\n",
       "      <td>4618.0</td>\n",
       "      <td>4589.0</td>\n",
       "      <td>4544.0</td>\n",
       "      <td>4534.0</td>\n",
       "      <td>4524.0</td>\n",
       "      <td>4502.0</td>\n",
       "      <td>4469.0</td>\n",
       "      <td>4442.0</td>\n",
       "      <td>4417.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>중구</th>\n",
       "      <td>5725.0</td>\n",
       "      <td>5721.0</td>\n",
       "      <td>5702.0</td>\n",
       "      <td>5680.0</td>\n",
       "      <td>5683.0</td>\n",
       "      <td>5644.0</td>\n",
       "      <td>5628.0</td>\n",
       "      <td>5628.0</td>\n",
       "      <td>5636.0</td>\n",
       "      <td>5661.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4637.0</td>\n",
       "      <td>4610.0</td>\n",
       "      <td>4597.0</td>\n",
       "      <td>4587.0</td>\n",
       "      <td>4587.0</td>\n",
       "      <td>4559.0</td>\n",
       "      <td>4539.0</td>\n",
       "      <td>4504.0</td>\n",
       "      <td>4501.0</td>\n",
       "      <td>4459.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>용산구</th>\n",
       "      <td>11797.0</td>\n",
       "      <td>11732.0</td>\n",
       "      <td>11677.0</td>\n",
       "      <td>11584.0</td>\n",
       "      <td>11532.0</td>\n",
       "      <td>11555.0</td>\n",
       "      <td>11555.0</td>\n",
       "      <td>11617.0</td>\n",
       "      <td>11630.0</td>\n",
       "      <td>11643.0</td>\n",
       "      <td>...</td>\n",
       "      <td>8496.0</td>\n",
       "      <td>8438.0</td>\n",
       "      <td>8436.0</td>\n",
       "      <td>8424.0</td>\n",
       "      <td>8404.0</td>\n",
       "      <td>8369.0</td>\n",
       "      <td>8300.0</td>\n",
       "      <td>8278.0</td>\n",
       "      <td>8251.0</td>\n",
       "      <td>8256.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>성동구</th>\n",
       "      <td>16416.0</td>\n",
       "      <td>16328.0</td>\n",
       "      <td>16267.0</td>\n",
       "      <td>16264.0</td>\n",
       "      <td>16182.0</td>\n",
       "      <td>16134.0</td>\n",
       "      <td>16031.0</td>\n",
       "      <td>15989.0</td>\n",
       "      <td>15857.0</td>\n",
       "      <td>15771.0</td>\n",
       "      <td>...</td>\n",
       "      <td>14055.0</td>\n",
       "      <td>13960.0</td>\n",
       "      <td>13924.0</td>\n",
       "      <td>13865.0</td>\n",
       "      <td>13803.0</td>\n",
       "      <td>13723.0</td>\n",
       "      <td>13681.0</td>\n",
       "      <td>13613.0</td>\n",
       "      <td>13551.0</td>\n",
       "      <td>13498.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>광진구</th>\n",
       "      <td>17564.0</td>\n",
       "      <td>17518.0</td>\n",
       "      <td>17436.0</td>\n",
       "      <td>17334.0</td>\n",
       "      <td>17312.0</td>\n",
       "      <td>17307.0</td>\n",
       "      <td>17233.0</td>\n",
       "      <td>17189.0</td>\n",
       "      <td>17119.0</td>\n",
       "      <td>17082.0</td>\n",
       "      <td>...</td>\n",
       "      <td>13099.0</td>\n",
       "      <td>12955.0</td>\n",
       "      <td>12803.0</td>\n",
       "      <td>12679.0</td>\n",
       "      <td>12570.0</td>\n",
       "      <td>12454.0</td>\n",
       "      <td>12369.0</td>\n",
       "      <td>12265.0</td>\n",
       "      <td>12180.0</td>\n",
       "      <td>12108.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 120 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     2010-01  2010-02  2010-03  2010-04  2010-05  2010-06  2010-07  2010-08  \\\n",
       "gu                                                                            \n",
       "종로구   6420.0   6416.0   6372.0   6388.0   6348.0   6354.0   6311.0   6285.0   \n",
       "중구    5725.0   5721.0   5702.0   5680.0   5683.0   5644.0   5628.0   5628.0   \n",
       "용산구  11797.0  11732.0  11677.0  11584.0  11532.0  11555.0  11555.0  11617.0   \n",
       "성동구  16416.0  16328.0  16267.0  16264.0  16182.0  16134.0  16031.0  15989.0   \n",
       "광진구  17564.0  17518.0  17436.0  17334.0  17312.0  17307.0  17233.0  17189.0   \n",
       "\n",
       "     2010-09  2010-10  ...  2019-03  2019-04  2019-05  2019-06  2019-07  \\\n",
       "gu                     ...                                                \n",
       "종로구   6259.0   6236.0  ...   4635.0   4618.0   4589.0   4544.0   4534.0   \n",
       "중구    5636.0   5661.0  ...   4637.0   4610.0   4597.0   4587.0   4587.0   \n",
       "용산구  11630.0  11643.0  ...   8496.0   8438.0   8436.0   8424.0   8404.0   \n",
       "성동구  15857.0  15771.0  ...  14055.0  13960.0  13924.0  13865.0  13803.0   \n",
       "광진구  17119.0  17082.0  ...  13099.0  12955.0  12803.0  12679.0  12570.0   \n",
       "\n",
       "     2019-08  2019-09  2019-10  2019-11  2019-12  \n",
       "gu                                                \n",
       "종로구   4524.0   4502.0   4469.0   4442.0   4417.0  \n",
       "중구    4559.0   4539.0   4504.0   4501.0   4459.0  \n",
       "용산구   8369.0   8300.0   8278.0   8251.0   8256.0  \n",
       "성동구  13723.0  13681.0  13613.0  13551.0  13498.0  \n",
       "광진구  12454.0  12369.0  12265.0  12180.0  12108.0  \n",
       "\n",
       "[5 rows x 120 columns]"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 자치구 인덱스설정\n",
    "gu = []\n",
    "for i in pop_gu.index:\n",
    "    gu.append(i.split(' ')[1])\n",
    "    \n",
    "pop_gu['gu'] = gu\n",
    "pop_gu = pop_gu.set_index(['gu'])\n",
    "pop_gu.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2010-01-00</th>\n",
       "      <th>2010-02-00</th>\n",
       "      <th>2010-03-00</th>\n",
       "      <th>2010-04-00</th>\n",
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       "      <th>...</th>\n",
       "      <th>2019-05-00</th>\n",
       "      <th>2019-06-00</th>\n",
       "      <th>2019-07-00</th>\n",
       "      <th>2019-08-00</th>\n",
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       "      <th>gu</th>\n",
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       "      <th>행정구역</th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>서울특별시 종로구 청운효자동(1111051500)</th>\n",
       "      <td>710.0</td>\n",
       "      <td>709.0</td>\n",
       "      <td>699.0</td>\n",
       "      <td>691.0</td>\n",
       "      <td>684.0</td>\n",
       "      <td>680.0</td>\n",
       "      <td>678.0</td>\n",
       "      <td>667.0</td>\n",
       "      <td>656.0</td>\n",
       "      <td>676.0</td>\n",
       "      <td>...</td>\n",
       "      <td>492.0</td>\n",
       "      <td>483.0</td>\n",
       "      <td>493.0</td>\n",
       "      <td>493.0</td>\n",
       "      <td>491.0</td>\n",
       "      <td>486.0</td>\n",
       "      <td>487.0</td>\n",
       "      <td>481.0</td>\n",
       "      <td>종로구</td>\n",
       "      <td>청운효자동</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>서울특별시 종로구 사직동(1111053000)</th>\n",
       "      <td>446.0</td>\n",
       "      <td>436.0</td>\n",
       "      <td>437.0</td>\n",
       "      <td>442.0</td>\n",
       "      <td>443.0</td>\n",
       "      <td>455.0</td>\n",
       "      <td>452.0</td>\n",
       "      <td>453.0</td>\n",
       "      <td>452.0</td>\n",
       "      <td>446.0</td>\n",
       "      <td>...</td>\n",
       "      <td>346.0</td>\n",
       "      <td>340.0</td>\n",
       "      <td>336.0</td>\n",
       "      <td>338.0</td>\n",
       "      <td>337.0</td>\n",
       "      <td>336.0</td>\n",
       "      <td>337.0</td>\n",
       "      <td>343.0</td>\n",
       "      <td>종로구</td>\n",
       "      <td>사직동</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>서울특별시 종로구 삼청동(1111054000)</th>\n",
       "      <td>132.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>127.0</td>\n",
       "      <td>127.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>127.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>123.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>124.0</td>\n",
       "      <td>...</td>\n",
       "      <td>67.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>종로구</td>\n",
       "      <td>삼청동</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>서울특별시 종로구 부암동(1111055000)</th>\n",
       "      <td>334.0</td>\n",
       "      <td>331.0</td>\n",
       "      <td>333.0</td>\n",
       "      <td>340.0</td>\n",
       "      <td>340.0</td>\n",
       "      <td>344.0</td>\n",
       "      <td>340.0</td>\n",
       "      <td>342.0</td>\n",
       "      <td>340.0</td>\n",
       "      <td>336.0</td>\n",
       "      <td>...</td>\n",
       "      <td>286.0</td>\n",
       "      <td>285.0</td>\n",
       "      <td>282.0</td>\n",
       "      <td>281.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>287.0</td>\n",
       "      <td>294.0</td>\n",
       "      <td>종로구</td>\n",
       "      <td>부암동</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>서울특별시 종로구 평창동(1111056000)</th>\n",
       "      <td>724.0</td>\n",
       "      <td>734.0</td>\n",
       "      <td>722.0</td>\n",
       "      <td>722.0</td>\n",
       "      <td>727.0</td>\n",
       "      <td>727.0</td>\n",
       "      <td>713.0</td>\n",
       "      <td>705.0</td>\n",
       "      <td>703.0</td>\n",
       "      <td>691.0</td>\n",
       "      <td>...</td>\n",
       "      <td>678.0</td>\n",
       "      <td>677.0</td>\n",
       "      <td>673.0</td>\n",
       "      <td>672.0</td>\n",
       "      <td>676.0</td>\n",
       "      <td>672.0</td>\n",
       "      <td>655.0</td>\n",
       "      <td>654.0</td>\n",
       "      <td>종로구</td>\n",
       "      <td>평창동</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>서울특별시 강북구 번2동(1130560300)</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>485.0</td>\n",
       "      <td>488.0</td>\n",
       "      <td>489.0</td>\n",
       "      <td>478.0</td>\n",
       "      <td>474.0</td>\n",
       "      <td>471.0</td>\n",
       "      <td>458.0</td>\n",
       "      <td>434.0</td>\n",
       "      <td>강북구</td>\n",
       "      <td>번2동</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>서울특별시 강북구 번3동(1130560800)</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>599.0</td>\n",
       "      <td>594.0</td>\n",
       "      <td>583.0</td>\n",
       "      <td>583.0</td>\n",
       "      <td>577.0</td>\n",
       "      <td>577.0</td>\n",
       "      <td>584.0</td>\n",
       "      <td>579.0</td>\n",
       "      <td>강북구</td>\n",
       "      <td>번3동</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>서울특별시 강북구 수유1동(1130561500)</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>662.0</td>\n",
       "      <td>659.0</td>\n",
       "      <td>660.0</td>\n",
       "      <td>657.0</td>\n",
       "      <td>662.0</td>\n",
       "      <td>663.0</td>\n",
       "      <td>654.0</td>\n",
       "      <td>647.0</td>\n",
       "      <td>강북구</td>\n",
       "      <td>수유1동</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>서울특별시 강북구 수유2동(1130562500)</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>729.0</td>\n",
       "      <td>716.0</td>\n",
       "      <td>693.0</td>\n",
       "      <td>675.0</td>\n",
       "      <td>676.0</td>\n",
       "      <td>675.0</td>\n",
       "      <td>678.0</td>\n",
       "      <td>669.0</td>\n",
       "      <td>강북구</td>\n",
       "      <td>수유2동</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>서울특별시 강북구 수유3동(1130563500)</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>610.0</td>\n",
       "      <td>603.0</td>\n",
       "      <td>596.0</td>\n",
       "      <td>600.0</td>\n",
       "      <td>592.0</td>\n",
       "      <td>575.0</td>\n",
       "      <td>570.0</td>\n",
       "      <td>563.0</td>\n",
       "      <td>강북구</td>\n",
       "      <td>수유3동</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>437 rows × 122 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                             2010-01-00  2010-02-00  2010-03-00  2010-04-00  \\\n",
       "행정구역                                                                          \n",
       "서울특별시 종로구 청운효자동(1111051500)       710.0       709.0       699.0       691.0   \n",
       "서울특별시 종로구 사직동(1111053000)         446.0       436.0       437.0       442.0   \n",
       "서울특별시 종로구 삼청동(1111054000)         132.0       128.0       127.0       127.0   \n",
       "서울특별시 종로구 부암동(1111055000)         334.0       331.0       333.0       340.0   \n",
       "서울특별시 종로구 평창동(1111056000)         724.0       734.0       722.0       722.0   \n",
       "...                                 ...         ...         ...         ...   \n",
       "서울특별시 강북구 번2동(1130560300)           0.0         0.0         0.0         0.0   \n",
       "서울특별시 강북구 번3동(1130560800)           0.0         0.0         0.0         0.0   \n",
       "서울특별시 강북구 수유1동(1130561500)          0.0         0.0         0.0         0.0   \n",
       "서울특별시 강북구 수유2동(1130562500)          0.0         0.0         0.0         0.0   \n",
       "서울특별시 강북구 수유3동(1130563500)          0.0         0.0         0.0         0.0   \n",
       "\n",
       "                             2010-05-00  2010-06-00  2010-07-00  2010-08-00  \\\n",
       "행정구역                                                                          \n",
       "서울특별시 종로구 청운효자동(1111051500)       684.0       680.0       678.0       667.0   \n",
       "서울특별시 종로구 사직동(1111053000)         443.0       455.0       452.0       453.0   \n",
       "서울특별시 종로구 삼청동(1111054000)         125.0       127.0       125.0       123.0   \n",
       "서울특별시 종로구 부암동(1111055000)         340.0       344.0       340.0       342.0   \n",
       "서울특별시 종로구 평창동(1111056000)         727.0       727.0       713.0       705.0   \n",
       "...                                 ...         ...         ...         ...   \n",
       "서울특별시 강북구 번2동(1130560300)           0.0         0.0         0.0         0.0   \n",
       "서울특별시 강북구 번3동(1130560800)           0.0         0.0         0.0         0.0   \n",
       "서울특별시 강북구 수유1동(1130561500)          0.0         0.0         0.0         0.0   \n",
       "서울특별시 강북구 수유2동(1130562500)          0.0         0.0         0.0         0.0   \n",
       "서울특별시 강북구 수유3동(1130563500)          0.0         0.0         0.0         0.0   \n",
       "\n",
       "                             2010-09-00  2010-10-00  ...  2019-05-00  \\\n",
       "행정구역                                                 ...               \n",
       "서울특별시 종로구 청운효자동(1111051500)       656.0       676.0  ...       492.0   \n",
       "서울특별시 종로구 사직동(1111053000)         452.0       446.0  ...       346.0   \n",
       "서울특별시 종로구 삼청동(1111054000)         120.0       124.0  ...        67.0   \n",
       "서울특별시 종로구 부암동(1111055000)         340.0       336.0  ...       286.0   \n",
       "서울특별시 종로구 평창동(1111056000)         703.0       691.0  ...       678.0   \n",
       "...                                 ...         ...  ...         ...   \n",
       "서울특별시 강북구 번2동(1130560300)           0.0         0.0  ...       485.0   \n",
       "서울특별시 강북구 번3동(1130560800)           0.0         0.0  ...       599.0   \n",
       "서울특별시 강북구 수유1동(1130561500)          0.0         0.0  ...       662.0   \n",
       "서울특별시 강북구 수유2동(1130562500)          0.0         0.0  ...       729.0   \n",
       "서울특별시 강북구 수유3동(1130563500)          0.0         0.0  ...       610.0   \n",
       "\n",
       "                             2019-06-00  2019-07-00  2019-08-00  2019-09-00  \\\n",
       "행정구역                                                                          \n",
       "서울특별시 종로구 청운효자동(1111051500)       483.0       493.0       493.0       491.0   \n",
       "서울특별시 종로구 사직동(1111053000)         340.0       336.0       338.0       337.0   \n",
       "서울특별시 종로구 삼청동(1111054000)          67.0        63.0        61.0        62.0   \n",
       "서울특별시 종로구 부암동(1111055000)         285.0       282.0       281.0       280.0   \n",
       "서울특별시 종로구 평창동(1111056000)         677.0       673.0       672.0       676.0   \n",
       "...                                 ...         ...         ...         ...   \n",
       "서울특별시 강북구 번2동(1130560300)         488.0       489.0       478.0       474.0   \n",
       "서울특별시 강북구 번3동(1130560800)         594.0       583.0       583.0       577.0   \n",
       "서울특별시 강북구 수유1동(1130561500)        659.0       660.0       657.0       662.0   \n",
       "서울특별시 강북구 수유2동(1130562500)        716.0       693.0       675.0       676.0   \n",
       "서울특별시 강북구 수유3동(1130563500)        603.0       596.0       600.0       592.0   \n",
       "\n",
       "                             2019-10-00  2019-11-00  2019-12-00   gu   dong  \n",
       "행정구역                                                                         \n",
       "서울특별시 종로구 청운효자동(1111051500)       486.0       487.0       481.0  종로구  청운효자동  \n",
       "서울특별시 종로구 사직동(1111053000)         336.0       337.0       343.0  종로구    사직동  \n",
       "서울특별시 종로구 삼청동(1111054000)          61.0        57.0        57.0  종로구    삼청동  \n",
       "서울특별시 종로구 부암동(1111055000)         280.0       287.0       294.0  종로구    부암동  \n",
       "서울특별시 종로구 평창동(1111056000)         672.0       655.0       654.0  종로구    평창동  \n",
       "...                                 ...         ...         ...  ...    ...  \n",
       "서울특별시 강북구 번2동(1130560300)         471.0       458.0       434.0  강북구    번2동  \n",
       "서울특별시 강북구 번3동(1130560800)         577.0       584.0       579.0  강북구    번3동  \n",
       "서울특별시 강북구 수유1동(1130561500)        663.0       654.0       647.0  강북구   수유1동  \n",
       "서울특별시 강북구 수유2동(1130562500)        675.0       678.0       669.0  강북구   수유2동  \n",
       "서울특별시 강북구 수유3동(1130563500)        575.0       570.0       563.0  강북구   수유3동  \n",
       "\n",
       "[437 rows x 122 columns]"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# # 행정구역 세분화 및 칼럼추가\n",
    "# si = []\n",
    "# gu = []\n",
    "# dong = []\n",
    "# code = []\n",
    "\n",
    "# for i in pop_merge.index:\n",
    "#     si.append(i.split(' ')[0])\n",
    "#     gu.append(i.split(' ')[1])\n",
    "#     dong.append(i.split(' ')[2][:-12])\n",
    "#     code.append(i.split(' ')[2][-11:-1])\n",
    "\n",
    "# pop_merge['gu'] = gu\n",
    "# pop_merge['dong'] = dong\n",
    "\n",
    "# pop_merge"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 표로 살펴봅시다아아아"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x194bdf8fb88>"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 201912 기준 동별 아동수 bar chart\n",
    "plt.figure(figsize=(10,4))\n",
    "pop_gu['2019-12'].sort_values(ascending=False).plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x194bf020808>"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 종로구 인구변화 추세선\n",
    "plt.figure(figsize=(15,6))\n",
    "plt.title('종로구 인구변화 추세선', fontsize=15) \n",
    "plt.ylabel('num', fontsize=14)\n",
    "plt.xlabel('Date', fontsize=14)\n",
    "population['종로구'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 날짜타입 생성\n",
    "# population = population.reset_index()\n",
    "# population['index'] = pd.to_datetime(population['index'].astype(str), format='%Y-%m')\n",
    "# population['index']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 2010~2019 자치구별 line chart\n",
    "plt.figure(figsize=(15,6))\n",
    "plt.title('자치구별 인구변화 추세선', fontsize=15) \n",
    "plt.ylabel('num', fontsize=14)\n",
    "plt.xlabel('Date', fontsize=14)\n",
    "\n",
    "for gu in pop_gu.T.columns:\n",
    "    population[f'{gu}'].plot()\n",
    "    plt.xticks(rotation=40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x194bdeb0e48>"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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kaQBWniRJkiQVtanyFBH7Ax8H7kXnRoYvBlYA7wUOBP4uM8+rYt8CPJlOvrMmM78eEQ8bNLZuDK1NniLi14HPZebd88Q8Cdg/M6+b3MgkSZIkTcEu4Dczc3tEnAH8FvAk4BWZeUtEXB4RTwD2B47IzKdExCOBi4GTgUsaxBZNPXmKiHPpHeCjMvN+wEuBm4G7I+IS4NHAw4BbgJ8CvwuspJM5dvd3FZ0MtNuxwJGZuXMc6yBJkiQtRm2qPGXmLLC9ens0cBOwOjNvqdo+DhwP/CLwkWqer0XE4RGxHDhwkNj5xjD15Ckz3xkRfwwckpn/NyKuLcScAxARHwfenJn/WL1/VCH21P62iNhAJ1OVJEmS1EIRsQZY09W0LjPX9cWcV8V8C1gH3Nk1+U7g4cD9gDu62ncBRwwaGxEzVaK2h7bcMOIBwDur193VoY9ExCsAIuIQOiv4/H1ZQGaO4mH1kiRJ0tIRk/vJzHWZeVzXT0/iBJCZF2fm0cC76eQPh3ZNPoxOIvST6vWcWWDroLF1iRNMOXmKiCMi4nF0Tsk7IiKeAvzviPiVKuRFmfmn1eu3Ab8HPDAintpwUbtHMmBJkiRJUxER94qfn0d4K7AMOCAiVlZtpwHXATcAp1fzPAL4XmbuGDR2vjFM+7S9I4DH0UlurgRWATvoS3Yi4r3AP2Xmpoj4G+BPImJHX8xq4MKuplV0zoncWk3fDKzNzI19891THjzt3LfyxGe/aGQrJ0mSJC1kbbrmCfhl4JKI+CmdnOHVwH2AK6q2T2TmNyPiZuDkiLgBuAs4q5r/3AaxRdGGs9ki4mg6K/OQquk7wIfo3EJwd0Q8gE4itCszf9I13wvpXPh1aaHPc4At/cnSfC7e/O2BN8ZM4TgaxbE106CT0hhGpV2fk+maYbIbY5bpfyZHYVzbbTSfs1Jbs45L4U36GOdnbNhTCib9i7JuaaP4JAy7Jk2Pi2E12RajGFnd6pWOgSbH1SiOoSY9jGJ541q/usi6LkrfnbX7qUFsk36baNJHabxQ//dMk/UrxY6m37JG3/c17U986KEL4q+t+5x52cT+MPnhpS9s/TaZduWJ6s4XH6Zz28C5G0E8ErgU+FVgd2Z+NyJeBfwQuGJu3sy8bPIjliRJkpaGllWepq4NN4zYv/r3X7ra/oXOjSP23zNckiRJkiZv6pWn6iFXbwKuioi5O1vMAH+Ymdv6wt9YVaC6XZOZF499oJIkSdISY+Wp19STJ4DM/BTwqb3EvB94f4Nu19N723NJkiRJ2metSJ7GoVC1kiRJktSEhacebbjmSZIkSZJaz+RJkiRJkgawaE/bkyRJkjQcbxjRy+Spy8H7TbYQV37I5kSHwGyDx57Vja1JH000eX5z3YNl2/yg1jozOf0HrbbhOKzb/02Oi7rtUz5ma46hmk5K42j0gOOa0FE8aHe23FweRmkcNRt5tqZ90g+SLWkyhCbf9JN+UG+Tbdl0bKWe6z5PjR58WgiufzBszUNSB19co+WV5y+3j+L3RanvugfDTvphtqV93eT7tG4coziGRiELn4hdNV+GEaWNURM71sd4ayEyeZIkSZJUZOWpl9c8SZIkSdIArDxJkiRJKrLy1MvKkyRJkiQNwMqTJEmSpCIrT72sPEmSJEnSAFqZPEXEIRHx69MehyRJkrSkxQR/FoCxnrYXEd8Gbu1r3pGZz6ymb+waw9bMfEHV9jvAS4Hruvq6Gfi3vr5WZeZD+5Z5FbCiL+5Y4MjM3DnM+kiSJElausZ9zdOtmfnU+QIy88QB+/rXzDypu6FKtPr7O7W/LSI2ALsGXI4kSZIkvOap37iTp5mIuJZOJSiAu6r252bm3Q37elDVV7ejBp05s+lztCVJkiTp58aaPGXmkwEi4gxgeWZeOsBsjwHWA7f39fXLQwxl9xDzSpIkSUuSladeY0meIuKJdK5bmjPTaY4Tutr+rGb2rwBnAW+u+loNXNg1fVnVX/f1S2vpJEjdcauA7cDWqp/NwNrM7DnVLyLWAGsAXnjeH3LCc1+89xWUJEmStOSMJXnKzM8Bn4uIlcCrgWOqSV8H3p2Zt0Enk42Ih9JJiA4Bvg0kXZWizNwEbJp7HxGnAA/KzHcXFt0ddw6wpT9ZKox1HbAO4D033uKpfZIkSVLFylOvcd+q/GPARuD06ufTwOVd068EzgZ+C3gGcO+6jiLiY9XLHcC2cQxWkiRJkuqM7ZqniFgGHAB8MTN/VrV9Cdg/IvbLzJ2Z+YHCfHVdHg6QmdfVBUiSJEnSuIwtecrM3RFxIXB1X0J04T4+b+nYwt32AF6Wmd/fp0FKkiRJqudZez3Gfbe9a4BrGs4z9yynM/va79tw8evpvamEJEmSJO2zcT/naWoy0+uiJEmSpCF4w4he475hhCRJkiQtCou28iRJkiRpOFaeell5kiRJkqQBWHnq8o0f7Bhq/rpMtEnGPlMTWtfexGL+n4NRbJ9xabrZc8hHNY9zN88UOp+tGXApdiRjaBBbN4QmY6tbv5Jh9x2Ux1zX7+zwiyuu37j2HTTbf6X1a/o/fqVVqVu/YbfFeD97TWKHH0h5uzWYv+b2XOP8HTeout+FdWNocgyV+qj9HqrZRssKB3mTfbqsZkVKXSxreKyUouvGVmqtW1ztPhlsWLV91K1dKbbp33BN1q9uXy8Ui/nvx31h5UmSJEmSBmDlSZIkSVKRladeVp4kSZIkaQBWniRJkiSVWXjqYeVJkiRJkgawoJOniHjutMcgSZIkLVYRMbGfhWDqp+1FxLOA86q3R9EpDt5SvX9nZn4iIi4CfqVqOwy4MjPfCrwGuLqvv6uAFX2LORY4MjN3jn4NJEmSJC0FU0+eMnNDRFwHPB94Mp1q2A3ARzNz7sFL7wIOqV4/HnjgPP2d2t8WERuAXaMctyRJkrTYLZSK0KRMPXmKiBcDDwY+nZkfqtqeBrw9Im7NzP8F/Dnwua7ZNlX/LouIzVQVqvmWkzmKx1dKkiRJWqqmmjxFxGpgTfV2dSGzPSYivlq9voTOKX3LgMMi4l7A7sw8cYBF7R7FeCVJkiQtXVNNnjJzEz+vIhERZwDLM/PS7riIuD9wDjAL7AS20rnWaVtXzGrgwq7ZVgHbq1iqCtXazNzY1/caqgTuKa/6A455+gtGs3KSJEnSAudZe72mftoeQEQcBbwOeFrnbRwHXJyZ3wHIzEsj4r7Aq4HH0hn3LwGvneujkIidA2zpT5b6ZeY6YB3Af7nym57aJ0mSJKmoLbcqvwy4gs4d9R4DXA58tC/mw3Sue3oh8Lxq+mURsd8ExylJkiQtGd6qvFcrKk90rmP6cmb+DCAivsSeid29gc9l5n9UMV8BfgYcSOdUPkmSJEkam7YkT+cBl3dlnAG8vi/mXDqVprn3y4F3ZOZdExmhJEmStMQskILQxLQiecrM64Hr9xJzI/CMBt2ux4qUJEmSpBFpRfI0Dpm5be9RkiRJkuoslGuRJqUtN4yQJEmSpFZbtJUnSZIkScOx8NTLypMkSZIkDcDKkyRJkqSimRlLT91Mnrp87mu379HWpFTZ9IK6UnhmOTbrJhTMzpZjS1006bfOuC4kHEW3pbGNq99R9T2KcZRjJ9tvk20/imNo2OU1HduyBr9MSl00Wee6ZdWOmT0n1A23NI4m6zbfOAZdXpP5676ymvQx0+T4Hrzbfdhuw22LumOoyb6uW1xpG9X9AVUaxii2RZNTY5psi6Z/B5bWpcl+Wt7w90Vp29fFLm8wttJ6L2t4DDXZFsVjqPbYLLcP+ydKk35r16PmUzLL8H8/aWEyeZIkSZJU5DVPvbzmSZIkSZIGYOVJkiRJUpHPeepl5UmSJEmSBmDyJEmSJEkDaO1pexFxH+DwzPzWtMciSZIkLUWetderNZWniPhkX9MjgRd0Tb+2MM/GQttVEXFt388dEbHf6EctSZIkaaloReUpIpYBx1UJzmnA2cChwBVdYQ8qJFBH9feVmacW+t8A7BrdiCVJkqTFzxtG9GpF8kQnWfoQ8KbM/O/ARyPiqcAJXTE/AS7rm2/NoAvIUTwNVpIkSdKSNdXkKSJm6CROKzPzvIh4eUT8KfDqQviLgf5T724YcFG7hximJEmStCRZeeo17crT/sAPMvM9AJn55xGxKTN3RMR24IcRsRq4sGueI4AAbod7duhaOglSd9wqYDuwtYrbDKzNzJ7rpCJiDVUF64HPOZf7HHfKqNdRkiRJ0iIw1eQpM/8DuLyqQF0APB3YXV0D9WXgDZm5A9g0N09EnAEsz8xLC112x50DbOlPlgpjWAesA3jsW/6Pp/ZJkiRJFQtPvaZdeZrzMuAXgKdm5izcUxF6I3B+RNwXuLx7hog4Ezg6M1dOeKySJEmSlqC2JE8A2+cSp8pdcy8y8w7gqf0zlG5VLkmSJGk0vOapV1uSpw8Cb4yIz9K5pfgy4GbgdVMdlSRJkiRVWpE8VbcRf/M+zPqaeaatB3bu24gkSZIkWXjq1YrkaV9l5j/NM23bJMciSZIkaXFb0MmTJEmSpPHxmqdeM9MegCRJkiQtBFaeJEmSJBVZeOpl8tTl3JOP3qOt7oBpUsKsK+/tzsGfydsgVANosjlnCxu/bn6/X35uZkzftk16bfL5rfucNvms10VO+pSHJqcUjGL9yv2W22ca9DKKzRZjWl7deoxrV5f6rVu3mbrjfsht0WR5dbFj2z4N40vfT7V9NOi8tN5N17nJti/H1nU8XL8Nuh3Z8orNYzu+64Jr2kt/CIxgbGo/T9vTkmMeKmkc/N/ZhWFc/7EjaWmw8iRJkiSpyBtG9LLyJEmSJEkDsPIkSZIkqcjCUy8rT5IkSZI0ACtPkiRJkoq85qlXaytPEXHWtMcgSZIkafoi4tCIuCwiNkfE30TEgyPipRHxjartM12xb4mI6yPixog4pmp7WERcV7VdPF/sfKZeeYqItwGPq94+ALg4M9cDzwM+0BV3bWae2Dfvxsw8qa/tKmBF32KOBY7MzJ2jHr8kSZK0WLWo8HQwcG5mfj8ingW8DtgCnJ+ZV88FRcSTgCMy8ykR8UjgYuBk4BLgFZl5S0RcHhFPAPavia019eQpM8+fex0RlwHX1IQ+KCKu7Ws7qtDfqf1tEbEB2DXMOCVJkiRNR2Z+v+vtj4C7gUOBr/aFPh34SDXP1yLi8IhYDhyYmbdUMR8Hjgd+sT92b+NozWl7EXEacGtmfrer7W+rdoCfAJf1/fxo0P4z02ejSpIkSQ1ExCR/1kTETV0/awrjWUmn6nQJnULQRRFxQ1fs/YA7umbZBRwB3NnVdidwWCk2IubNj6ZeeaoG+Ht0Ttk7r3taZp7Q9fbFwH59s98w4GJ27/MAJUmSJI1dZq4D1tVNj4hTgGcDr8zMO4E3AW+KiIOBqyPiRjoFl8O6ZpsFttKpUs05jE7SdFB/bGbOzjfGqSZPEXEo8JfAn2fmH/VN/pcqZjVwYVf7EUAAt1fTAdbSSZC641YB2+lsLCJiM7A2Mzf2jWENsAbgty9Yy6+d9pIRrJkkSZK08LXlmqeIeBTw7Mw8q6tteWbuAnYAdwFJp7hyOnBDRDwC+F5m7oiIAyJiZWbeBpwGvBl4aH/s3sYx1eQpM38MPCsiHh0Rn6Rz0RZ0Tid8RxWzCdg0N09EnAEsz8xLC112x50DbOlPlgpjuCfD/csvfs9T+yRJkqT2OQl4UlUQAbgV+PeIeDydnObKzPxGRGwBTo6IG+gkVHPJ1rnAFRHxU+ATmfnNiLi5JrbW1E/bq7wf+I0qEyQiVgCfiYh/yMytEXFf4PLuGSLiTODozFw58dFKkiRJS0BbnvOUmRcBFw0QNwucXWj/Ap2bROw1dj5tSZ6y+ul+f8+/mXkH8NT+mSJi3qqSJEmSJI1KW5Kns4E/iYi5G0IsA/4wMwe+m54kSZKk0WpJ4ak1WpE8ZeZXgGftw6yvmWfaesCH4kqSJEkaiVYkT/sqM/9pnmnbJjkWSZIkSYvbgk6eJEmSJI1PW24Y0RbzPkFXkiRJktRh5UmSJElSkZWnXiZPXW7f9rM92uqOl5kGB9JMTWiTY3GG8Ry4s5SfCzyu5Wm06vZfyaT3aaPjuxDb5DPWfHmDB9d+BxRjJ7yNR9BH0+08rnEMqnSKRtcAACAASURBVO6IbzKGce2mce7/UZwm0mR8pci6+ZuMra6P8vIG77fJ91tdv1HTRyl+JMtrMI7avyMKsU22W906j295ZXXfQ6NYl0H7rZ17TGPQwmXyJEmSJKnIwlMvr3mSJEmSpAFYeZIkSZJU5DVPvaw8SZIkSdIArDxJkiRJKrLw1MvKkyRJkiQNoBWVp4j4FvD9vuY7MvP5XTGbgJf2xfxFZp7Y19dVwIq+uGOBIzNz54iGLEmSJC16XvPUqxXJE3BrfxJUcC/g9L62e/cHZeap/W0RsQHYte/DkyRJkrTUtSV5GsS9gP7EaI/kqU5mDv40UUmSJEle89Sn9clTRMwA+wOPrZpeRGfcH6qmHwjszMzd83Qz3zRJkiRJ2qu2JE8/iojNwBFAALdX7c8E/jPw24V5Htv1+oMRsQK4sKttFbAd2ApQ9b82MzeOdOSSJEnSIjVj6alHK5KnuRtDRMQZwPLMvLRr8ueBz0fEkcCrgYfTSbC+Abw7M7tvNLFp7kVEnANs2VuyFBFrgDUAp7/2rTzx2S8afoUkSZIkLToL6Vbll9FJjl5M59S9zwAfHbbTzFyXmcdl5nEmTpIkSZLqTLXyFBGr6T3Vbq79zK63c6faHQR8PjN3VDGfBw6MiPBmEJIkSdLoedZer6kmT5m5ia5T7fbiAuDKiJhLlAK4wMRJkiRJ0iS04pqnQTRMtADWAz4UV5IkSdpHPiS314JJnprKzG3THoMkSZKkxWPRJk+SJEmShjNj4anHQrrbniRJkiRNjZUnSZIkSUVe89TLypMkSZIkDcDKU5ff/9+b92irzbYLzXWxTdpHsbwm2jy2OuPqu8n6jaLfNt9lf1zHVpN+o+Yk60Z9NDm+Gy5vZqbwf091h9CQY56pHdvwy2vSb9NxlGP3DK77LIzmOBxXv6Xju1kf4/oKaHJc1MUuW7bn8d2k31Ecs81iB19e/dgG72NZgz7qYmdqlleKr91PxdjB+20yBihvu7rrYYrbovb7dPhtUTwOy6HF2OU1wfX7es+2uu1Z57gH37tR/LRYeOpl5UmSJEmSBmDlSZIkSVJRDHsKziJj5UmSJEmSBmDlSZIkSVKRz3nqZeVJkiRJkgawoCtPEbESOCoz/27aY5EkSZIWG5/z1KsVyVNEbMzMk/YS8ybg14BdwO3AK4EHAycCf9cVdxWwom/2Y4EjM3PnKMctSZIkaeloRfK0NxHxcODozHxK9f53gZcA3+yPzcxTC/NvoJN0SZIkSdI+WRDJE/Ad4N4R8SzgLjoVqD8ADh20g2zzE0klSZKkFvKsvV5tSZ4eGxGbC+2/k5n/nJnbI+L5wK8DK4ELgB3A4QP2v3s0w5QkSZK0VLUiecrM+wJExBnA8sy8dG5aRBwAvJ9OAnQ4cH/g/wDbgC93xa0GLuzqdhWwHdhaTd8MrM3Mjd3Ljog1wBqA/R7zMpY/+CmjXTlJkiRpgZqx9NSjFcnTfDLzp8DLASLiicBJwIeBc4EnAF+p4jYBm+bmi4hzgC39yVKh/3XAOoCDf+PPPLVPkiRJUtGCec5TRLwbmAU+D3wPuAT42FQHJUmSJC1iEZP7WQimWnkqnGo3135m19u5U+0OBW7NzM9X7VuqH0mSJEkau6kmT/2n2g3gYxHxs762L2XmfxvhsCRJkiThQ3L7tf6apzmZeUbDWdYDPhRXkiRJ0kgsmOSpqczcNu0xSJIkSQuZhadeC+aGEZIkSZI0TYu28iRJkiRpOD7nqZeVJ0mSJEkagJUnSZIkSUXWnXqZPHV5yonH7NE2m+NbXuaenReaqnE0iK0ZdJPllWPr+i33May65Q07hib91vVd10dp29fvj7rljWeDlrptWokv3a50FOMdxW1QS1006bcutK6PmZk925v00WSV68bQbHmD9zHp29KOYluMaxx1p6s02W6FQ2WeMTTb18XYmj91SuMoHced2D3b62IbbbdiKywrfp6G3xalfuvULm/Ifuu2W10fpXHUnSZUil1eE1zst/Y7pNxePC5qx1YzYcB+69SPbbjlLavbbg1ShybbXguXyZMkSZKkIpO/Xl7zJEmSJEkDMHmSJEmSpAF42p4kSZKkoibXky0FVp4kSZIkaQALuvIUEUcBD8jMv532WCRJkqTFxhtG9GpF8hQRxwP/g04lLIBZ4A/mkqKIeAdwbN9sb63+PQG4J3mKiKuAFX2xxwJHZubO0Y9ekiRJ0lLQiuQJeCfw3Mz8AUBE3A/4FHAcQGa+NiI+nZnPjIgzgbuA3wV+Cbiyu6PMPLW/84jYAOwa6xpIkiRJi4yFp15tuebpRuDZEfHgiHgIcErV1m139e8BwI7MfAHw2kEXkON68qgkSZKkJaEVlafMfF1EHAf8OpDAP2bmn/WFzVb/Hgj8R0R8jELlqcbuvYdIkiRJ6uY1T72mmjxFxGrgwpppcy/XAr8C3BUR/x1YCRwJ/BVwO51rnkp9rQK2A1ur6ZuBtZm5sW85a4A1AMe8+A084IQ9zvqTJEmSpOkmT5m5Cdg0QOhGgIg4FXgY8BDgKOAzwNtLfUXEOcCW/mSpMIZ1wDqAZ77vHzy1T5IkSar4nKderbjmqbqb3t5iTgOeD/w+nWui/l/g4cDZ4x2dJEmSJLXkmifgsQPEHAnclJm3VO//LSJuAJ48tlFJkiRJS5jXPPVqS/L0wOqapH4vyczbqtfrgLURcR2dG0DMALcA505khJIkSZKWtFYkT5n5kAFifkazRGk94ENxJUmSpH1k3alXK5KnccjMbdMegyRJkqTFY9EmT5IkSZKGM+M1Tz1acbc9SZIkSWo7kydJkiRJGoCn7XV5yeNXDjV/06JmqQpadzvIJlluXR9NxtfktpSlsTW9rWUpukkXMw3Wrmn1OQp9j2tsTfvOBo91nmXP4Lr5sxDbdHnjMumzB0r7v3EfYxpz02OrpPw9VBNbs7wmD1As9VF3vI1i2w9rV84W22cLzaXPGDT73NRtiybGdczWHW9tPqOnydiaHN9NtvEofufUGfazV/9ZH4+6079maz4kpfiRjG3IfQo13537OJy2a/NnfBqsPEmSJEnSAKw8SZIkSSryIbm9rDxJkiRJ0gCsPEmSJEkqsvDUy8qTJEmSJA3AypMkSZKkIh+S22vilaeIWBkRx+0l5pCI+PVJjUmSJElSe0XEoRFxWURsjoi/iYgHR8TDIuK6iLgxIi7uin1LRFxftR9TtQ0cO5+xV54iYmNmntTVdDRwAnBTRGzsGsPWzHxB1fY7wEuB67r6uRn4t77uV2XmQ/uWdxWwoi/uWODIzNw59ApJkiRJS0SLCk8HA+dm5vcj4lnA64CHAK/IzFsi4vKIeAKwP3BEZj4lIh4JXAycDFzSILbWQMlTRPw9kHSe/3UU8OPMfMSAK7r/fBMz88QB+/nXviSMKtHq7+/U/raI2ADsGnA5kiRJklokM7/f9fZHwE+BAzPzlqrt48DxwC8CH6nm+VpEHB4RyweN3ds4BkqeMvP4udcRsR/whkHmi86N4Y+LiP2BxwMnAg8C/nmQ+fs8KCKu7Ws7atCZM5s8412SJElS257zFBEr6VSdXgP8cdekO4GHA/cD7uhq3wUcUU3fa2xEzGTmbN3yG5+2l5k7I+JeA4avBr4HPI/OKXg/Bh4HrJxnnscA64Hb+5b7y03H2mX3EPNKkiRJGrOIWAOs6Wpal5nruqafAjwbeCWwHTi0K/YwOonQQdXrObPA1kFj50ucYPDT9t5G57Q96CQ+PxtgnuXAfwWeAfw5sLEqh92H+ZOnrwBnAW+u+lkNXNg1fRmdG110X7+0lk6C1B23is5G3Vr1sxlYm5k9p/p176SXX7CWXzvtJXtbNUmSJGlJmOTd5apEaV1pWkQ8Cnh2Zp7V1XZARKzMzNuA0+jkDw8FTgduiIhHAN/LzB2Dxu5tjINWnm6hkzx9k06Za1tERN2pcFXi9D5gfWZ+NyLOBy6PiDMKsQ+lkxAdAny7Ws49laLM3ARs6oo/BXhQZr67sOjuuHOALf3JUr/unfQXX/yep/ZJkiRJ7XMS8KSqIAJwK3AucEVE/BT4RGZ+s7rJ3MkRcQNwF52iDA1jaw2aPJ0G3Af4GvAEOknO/SLiDZnZfx0SwJHAdZl5FUBmfiEiLuDn1as5VwJn07ng6y7gL+sGEBEfy8wXADuAbQOOW5IkSdI+ass1T5l5EXBRYdLxfXGzdPKL/vm/MGjsfAZNnu4GTqmudzoY+FPghcAngT2Sp8y8lU422N12E/TugMz8QP+88+ygw6t5rqsLkCRJkqRxGTR5ut/cM5Iyc3tEPDAz/29ETPL238cW7rYH8LK+WxdKkiRJGoGZdhSeWmPQ5OnGiPgA8CngycC/Vu3z3o2iJDM3A5vnmT73LKcz+9rv23BR6+m9qYQkSZIk7bNBn/P0+og4kc5txD9P52KrGWCPG0C0RWZ6XZQkSZKkkRn4OU/VjSH6T5u7vRQrSZIkaeHztL1ek7x1uyRJkiQtWANXniRJkiQtLW25VXlbmDx1+czNW/doW1ZTq2xyGM2M4KCbLTyPuOkTfUvPNJ5t0EnNM5EbjqHc3mQTlT7ETUrKdfujbu2a7L2646WJNnxJNRlC3fYcdjXqtkOTcnltH4Xmul3XpI9R7LrS9qxb51Hsp92FL4G6OwGVvofq+q6LLakLbfL9NC7LGhxwo/iub6LJ/mhqWfEzMvhnfXnd785Gx+zgsdHgm7qu30bLG8H3Qt32bDaOwvwNtkXd2Or2X+mYa3K8Nd0W5fUbXN1+KrXWjWGcn7PH8QtD96HJM3mSJEmSVOQ1T7285kmSJEmSBmDlSZIkSVJRC64maBUrT5IkSZI0ACtPkiRJkoomfTOctrPyJEmSJEkDaGXyFBHPnfYYJEmSpKVuZoI/C8FUT9uLiM/S+0iRXZl5EvAa4Ooq5jTgd2u6+EBmXt7V31XAir6YY4EjM3PnyAYuSZIkacmZ9jVPs5l54tybiNjYH5CZf1UlWc/IzI9Wcc8HrsvMrX2xp/bPHxEbgF0jH7kkSZK0yHnJU6+FUiHbD3h21/tTgP0HnTmzwaPuJUmSJKmgrcnTTERcERGvqt7fTe/peCuqtkHsnm9iRKyJiJsi4qZvffaKfRiqJEmStDjNREzsZyGY9ml7dWYz83SAiDgb+E3g3hHxZSDoXCf11xFxRWa+OyJWAxd2zb8K2A5srfrYDKzNzNJpgeuAdQAv+/A/WqGSJEmSVDTt5GkmIt4+X0Bmvg94H0BEnAEsz8xL+2I2AZvm3kfEOcCWUrIkSZIkSfti2snTi4EDu97vmNZAJEmSJPVaIGfTTcxUk6fMvH2+6YXT8ebaz+x6WzwdT5IkSZJGadqVp3n1n44nSZIkaXJmrDz1aGXy1P3sp320HvChuJIkSZJGppXJ07Ayc9u0xyBJkiQtdAvlFuKT0tbnPEmSJElSqyzKypMkSZKk4Vl46mXlSZIkSZIGYOWpy7//ePDHTC1rcOuRJhl7UA5ucqeTaLDAUfxvQpNtkVluny1MmJ0tB++u66TB8pposo1Ky2vL/9jUHVslyfAbrsnyRnF8l5qbnKfddD/NDHn7oVGcQ17XQ2kbjes7pKkc8kNZ87VQa1x3iSqNY9J3pBrnfip1XfddXxrHKEZW9xkpNY/i994ollf6H+mm+2l5oZPa5TXYT+WxlcdQ20eD79nyd3J5eU3+9qnto8H3Xmlsy2uP73IfM4Ux1x9b5faFYqGPf9SsPEmSJEnSAKw8SZIkSSpqcibJUmDlSZIkSZIGYOVJkiRJUpHXPPWy8iRJkiRJA2hl8hQRj61p/2Df++dExPMnMypJkiRpaZmJyf0sBFM9bS8ijgf+J7ALuBtYk5k/AP4QOKkr7k+Aw4ATIuKKqvls4GDgwL4+rwJW9C3qWODIzNw5jvWQJEmStPhN+5qntwPPycw7I+JJwP8AXgUQEdcC78rMq4Hfo1Ml2wCcWc17d6nDzDy1vy0iNtBJ0CRJkiRpn0w7ebo7M++sXn+FTpIEQGae2PV6e0QsBx4OrAKOAV4J3B+4ZJAF5bBPZpQkSZKWmHE+kHshmvY1T5+KiDdHxMnAu4B3zk2IiGsj4rldsa8F3ge8A9iQmScBawdczu5RDViSJEnS0jTVylNmXhIRDwIeDJybmVurSf+emb8FEBH7AecCv5SZayLiGXSSrpd39xURq4ELu5pWAduBrdX0zcDazNzYN98aYA3AI170BladsMdZf5IkSdKStFBu5DAp0z5tD+AAOsnRwdGpCwZwUdf0BG4DPhARh2TmNRHxZeCHwPH3BGVuAjbNvY+Ic4At/clSv8xcB6wDeMZ7/8FT+yRJkiQVtSF5eh/wqsz8FkBErAA+GxHXZ+b2zNwF/EVEvIpOwnRFdUc+gMumM2RJkiRp8fOSp17TvuYJOpWl2WkPQpIkSZLm04bK0+8CfxQRh1TvA3hTZm4vxL6xqkB1uyYzLx7rCCVJkqQlaMbSU4+pJ0+ZeTPw7AHi3g+8v0HX6wEfiitJkiRpJKaePI1LZm6b9hgkSZKkhcy77fVqwzVPkiRJktR6i7byJEmSJGk4XvLUy8qTJEmSJA3AypMkSZKkohksPXUzeeryN5/+4p6NdbXKKBTtmsTWxEdNH8X2usU16WMESv2OZFkN1m+c65yZQy2vNP+ojLPvkibrN67jrU6Tzwij2GylxY3gOCzF7t61uxi7a9euYnspvm4/5ezgG2Mkx1upj7rt02R5OYLHBdZ9Vw9rFGNrom49SuMY13fIzLJic8yUxzbRz29NvzMNxlb7mS40N+kXIApX5zcaW83V/cuW7blP6mJrlzfk2GaW1cUWm1lWiK+LnSmMrUls3XqUYuv7GDwW4FXHP6g8QLWayZMkSZKkIq956uU1T5IkSZI0AJMnSZIkSRqAp+1JkiRJKvIhub2sPEmSJEnSAFqbPEXEWQPErIyI4yYxHkmSJGmpmYmY2M9CMPXkKSLeFhHXVj83R8TvVJOeV4jd2Nd0NHBSX8xVXf3N/dwREfuNaRUkSZIkLQFTv+YpM8+fex0RlwHXzBO+/wD9ndrfFhEbgPIDUSRJkiQVLZCC0MRMvfI0JyJOA27NzO92tf1t1U50nrZ2XETsHxEnRMQfAGcO2n9O+mmikiRJkhaVqVeeImIG+D3gAcB53dMy84Sut6uB79E5ne864MfA44CVAyxm90gGK0mSJC0hC+VapEmZauUpIg4F/hr4bmaem5ndSc6/dMUtB/4r8AzglcDOzPwa8K9dMasjYnPXzz9HxD9GxGbg3lVbz/VR1XxrIuKmiLhp13f+djwrKkmSJGnBm2rlKTN/DDwrIh4dEZ/k59c0zQDvgHsSp/cB6zPzuxFxPnB5RJzR19cmYNPc+4g4B9iSmf03megfwzpgHcBBz36vp/ZJkiRJFQtPvaZ+2l7l/cBvZOZtABGxAvhMRPwDsAK4LjOvAsjML0TEBYCJjiRJkqSJaUvylPQmQ3OvMzNvBW7tCc68CSBMhSVJkqSxac3d5VqiLcnT2cCfdD2LaRnwh5n5oymOSZIkSZLu0YrkKTO/AjxrH+bbDGyumbwe2Lnvo5IkSZKWNs/06tWK5GkcMnPbtMcgSZIkafFYtMmTJEmSpOFYd+rlNWCSJEmSNACTJ0mSJEkagKftdXnMkx45cGzp4rmm19NlgydVTfpavXFdHJgNVroudHZ2zwl1/ZZiS23zLW9YTda5ed9j63popUOo6XHVpI9hY+vHMHjwKPZ1qYtRfBwnvd1mZtr7f3Pj2v91oSPZniMYx7KZ0u+tcnBpeaX565ZXN95lywZfXt0a142jpHb9Cn3UdVvqY1mDfVpaFsDyZeXPyH7Djq3mo1faxnWf0rpjqHxcDB5be2yO4EuutI0aHCqNLaWbKDT5/lkK2vvbTdKS5ne1pHGoS2YkaRBWniRJkiQV+d8Nvaw8SZIkSdIArDxJkiRJKvI0+l5WniRJkiRpAFaeJEmSJBUtpTsLDsLKkyRJkiQNoBWVp4i4CljR13wscGRm7oyI/YBPF2Z9NHD/zNw1aF8jHLYkSZK0qLWp0hIR9wXOAWYz8/cj4qXA+cAPgJ9l5tOruLcAT6aT66zJzK9HxMOA9wIHAn+XmefVxc43hlYkT5l5an9bRGwAdlXTdwInFmI2Nu1LkiRJ0oL0DuCfgYOr94cC52fm1XMBEfEk4IjMfEpEPBK4GDgZuAR4RWbeEhGXR8QTgP1rYmu1Inmqk5kJEBEHARuA2b6QRwHZpC9JkiRJg2nTNU+Z+bKIeCpwUtV0KPDVvrCnAx+p4r8WEYdHxHLgwMy8pYr5OHA88Iv9sXsbQ5uTp91dr5cB2zPzlBH0JUmSJKllImINsKaraV1mrptnluXARRGxE/hQFXs/4I6umF3AEcCdXW13Ag8vxUbETGb2F2x6Fjg1EbEauLCraRWwHdhaTd8MrAX+FnhCRFxb6OYNmXnToH1lZs+pft076SGnvZYjnvCc4VdMkiRJWgQmWXeqkp/5kqX++DcBb4qIg4GrI+JG4CfAYV1hs3TygUO72g6jkzQd1B87X+IEU06eMnMTsGnufUScA2zpT3Aq961izgCWZ+alQ/TVPd89O+lXL/obT+2TJEmSFoCIWF7dOG4HcBedy3luAE4HboiIRwDfy8wdEXFARKzMzNuA04A3Aw/tj93bMtt82p4kSZKkKWrTNU8Fb4uIx9PJaa7MzG9ExBbg5Ii4gU5CdVYVey5wRUT8FPhEZn4zIm6uia3V+uSpcDreXPuZXW/3OB1PkiRJ0uKSmZuBzdXr8wrTZ4GzC+1foHOTiL3Gzqf1yVP/6XiSJEmSNA1tS57WA6N6kO0o+5IkSZKWnDY9JLcNWpU8Zea2NvYlSZIkSa1KniRJkiS1R8tvGDFxVuIkSZIkaQBWniRJkiQVWXfqZfLU5Y9PP3bPxpojJkZwKE26CtpocS34pIxiG49LMvzzlJusX93yhu1j0tu4LZX/cX0W2nzMtmXbt8HEN8WYFtj0eGtyDDTpeXbCj5cf9lhuPPvQyyt3MJL9MeT3U90YGi1vFPt/BJ+RJp+HNmx7LVwmT5IkSZKK/M+3Xl7zJEmSJEkDsPIkSZIkqWjG0w57WHmSJEmSpAFYeZIkSZJU5DVPvRZU5SkiPtj3/jkR8fxpjUeSJEnS0tGKylNEfAw4vK/56Mw8qpr+J8BhwAkRcUU1/WzgYODAvr6uAlb09XUscGRm7hz12CVJkqTFylut92pF8pSZL+hv60qSAH6PTpVsA3Bm1XZ3TV+nFvraAOwaeqCSJEmSlqxWJE817jmlMDO3R8Ry4OHAKuAY4JXA/YFLBuksMyf8GD9JkiRpYfOap15TTZ4iYjVwYc3k/xQRm4G1mbkReC3wPuAdwG9k5scj4oX0nbZXY/coxitJkiRp6Zpq8pSZm4BN88VExH4R8XrglzJzTUQ8A/hURLy8L64/EVsFbAe2VtM38/NErHu+NcAagPPfegnPe9GZQ62TJEmSpMWpFaftRcRXgB/2Na/KzF8GErgN+EBEHJKZ10TEl6v44+eC+xOxiDgH2NKfLPXLzHXAOoAvfPsnntonSZIkVXxIbq9WJE/ADzPzxO6GiNgIkJm7gL+IiFfRSZiuyMwfVGGXTXaYkiRJkpaqtiRPkiRJklrGG0b0akvy9MDqmqRuDynEvbGqQHW7JjMvHs+wJEmSJKmjFclTZv4/A8S8H3h/g27XAz4UV5IkSdpHVp56tSJ5GofM3DbtMUiSJElaPBZt8iRJkiRpOOHd9nrMTHsAkiRJkrQQWHmSJEmSVDRj4amHlSdJkqT/v717j5KsLO89/n26ZwYQREBRGEBJJMe7aJQEDRBEwfvhoibGsLIw0fFyvBANUUK8LXVJVJScZQyORM1FMEIUFRQcwEFQQdF4TiRALi4DkuRIRFTuM93P+aN2Z6pr9p6p3dXV9e6a74dVi6pdv3r3s9/a1dNvv7v2lqQhOPPU512X/fNWy5rOMDLbYhgeLU5T0uaMJjMN4TZ/IMimOlq00Wb72vz1oqndUc/60qZeqK+5bRu1dYzcQvM+ULu+ZVjhqPty4z67wrXV/dWozWca2u0Xo/7Vrm3/tNkval+/THUMK5t+EC2DUWtejs/YuLavaX1t9rem7Zut2QnafO9hVcNO1OYzMq7+bNvuqP8uzzc03NT3bfqi9vUN71P9z+Th2+3lt35B837YItuwvvkW2VE1/s7RkG/1s6Fh+cMeuP/QbUyS33lazJknSZIkSRqCM0+SJEmSanmdp8WceZIkSZKkITjzJEmSJKmW33lazJknSZIkSRrCig6eImL3iNhvJdcpSZIkScthLIftRcT9gbOAhwAPBX4O/ATYE/gs8K4qdyNw88DLf5qZL+hr6wTg1Q2r+khmnteXvQDYbSBzMLA2MzcteYMkSZKkHZAXyV1sXN95+mPgU5n5hYjYCbgCeD2wN3BYX+7mzHzGthrKzM9ExOXAMzPzbwEi4kXAZZl520D2uMHXR8RFwOaRtkaSJEnSDm9cg6cDgXcCZOa9EfE16gcwe0fExprlT8/Mub7Hq4HnA39bPX4ecOWwxWSO8xKMkiRJ0nTyhBGLjWvw9JfAOyLifcDDgafSO3xuT+CzEbEzsAfwzIbX7x0RP83Mu6vHd7L4cLzdqmXDmNt+RJIkSZK2bSyDp8z8YkT8EPht4FbgqMy8OyIOB54CPBJ48XaaOQ/4dkS8CvhNYPeI+HsggHngCxFxfmZ+KCKOBk7re+3+wF3AbQDV7NbpmXnx4EoiYh2wDuDxJ57KgUecsNTNliRJkqaKF8ldLMZ5RFtEvAY4Fkh6Z/b7LvDWzLyren4/eieDeHT1kn8EPpyZtzS0dyKwKjM/sZ31ngzcUDdY2pZjP3rtVp3RtMPMtvj2XLTY69rsoDMN4Tb7eNO736aNNtvX5kuHTe2O+iFuXtANqAAAIABJREFUUy/U19y2jdo6Rm6heR+oXd8yrHDUfblxn13h2upOM9rmMw3t9otRv2zbtn/a7Be1r1+mOoY1zgOrR615OT5j49q+pvW12d+atm+2Zidoc+jOqoadqM1nZFz92bbdUf9dnm9ouKnv2/RF7esb3qf6n8nDt9vLb/2C5v2wRbZhffMtsqNq/J2jId/qZ0PD8t9+0v6dGJZc9c8/WbGvvxz2S3sW3ydjO1V5RPwGcBDw7Mw8pjoxxHeB9/TFzgMuA15Cb5ZqA3D+uGqSJEmSNLxYwVsXjOs7TwC7AD/LzP4TRdxWLSciZoE1wDcXvtsUEdcCqyNidWZuqjkcjyp3Ut/D2sPxJEmSJGk5jXPw9NfAW6vvG90HzNK7ptPvA2TmXEScSu8EEv0zYKctXJMpMzfQm42SJEmStMJGPRR82oxt8JSZ88Dbt5MZ1+DobMCL4kqSJElaNuOceZqYzLxj0jVIkiRJXee802JjO2GEJEmSJE2TqZx5kiRJkrQMnHpaxJknSZIkSRqCM0+SJEmSarW5QPaOwMFTn3323GXo7HJcdb7OuK5E37aOktVdBTzH2HF1TZfSl202e25+9D5q189bd9Jcw+vr+rP9Wzra9rV9T0c9des496G6z8hyaHOowrhqaDLTYnVtssuxHe3WV798OX4O1e+zw39umt7/umabPh9NNbd7/0b797dpXW1+QWxqYya27s/Z5hWOxXzDezqTW69wvqGNNu9HYx11O23D7pYt95dR1X2um1bVuC/Xtjv8+tRdHrYnSZIkSUNw5kmSJElSLSfOFnPmSZIkSZKG4MyTJEmSpFpOPC3mzJMkSZIkDaHTg6eIeP6ka5AkSZKmVqzgrQOKOGwvIr4P3DSw+O7MfHb1/MZq2eOAfwBuyczfBv4X8IWBti4Adhto62BgbWZuWubSJUmSJO0gihg8ATdl5pFNT2bmkRGxC3DdtnJV9rjBZRFxEbB51CIlSZKkHYkXyV2slMP2ZiLi0oi4OiKuqe5fGhG79mXeCFwQEa/uf2FEbIyI47e3ghznVVQlSZIkTb0iZp4y8wiAiDgRWJWZn1h4LiJ2At4M/Dgz3xURb4iIM+kNptjeTFRlbtmLliRJkqac13labKKDp4g4FHhZ36KZ3uI4rG/Zx4ENmfl1gMz8QETsm5lzEfG9vraOBk7re93+wF3AbdXzG4HTM/PigRrWAesADl/3Nh519IuWa/MkSZIkTZGJDp4y82rg6ojYD3gN8JjqqeuAD2XmLQvZvpNGLDwebGsDsKHv+ZOBGwYHSzU1rAfWA7zi/Os8tE+SJEmqOPG0WBGH7QGfBv4IeFv1+FDgPOCpC4G6w/MiYpsDI0mSJElaLhMfPEXELLAT8O3MvK9a9h1gTUSs9vTikiRJ0oQ49bTIxAdP1XeXTgM+N3Ao3mlDDJyuH19lkiRJkrTFxAdPAJl5CXDJEl73+9t4+mzAWStJkiRpibzO02JFDJ7GITPvmHQNkiRJkqZHKRfJlSRJkqSiTe3MkyRJkqTReJHcxZx5kiRJkqQhOPPU5zs33rrVspmZ+uF2m1H44AV9t9VGm2yTmZnhx8SZ9dcFbqqjPjt0dBttjLa+cdZb10XLsc0zBfdxk4aPw9Dra1tC3ZdUR62hqY7Zxs/68G00vafj+qNdU211mzLfcAnwumzbfWU5fh6Oy8j7bGO7o39+R+2Lti+vq7mpjdna7PD7W1O/N7Ux2+JPuauWoe/r+6L+Q7KqZmOa2916WdNnr2l5XV807W/1n9/6dsel8ede3c/Zpve/1fY1ZGtraLGvNC1fhvV1feKm6/UvN2eeJBXJs/tIGoc2A19JGuTMkyRJkqR6/r1hEWeeJEmSJGkIzjxJkiRJquVh9Is58yRJkiRJQ3DmSZIkSVItz7GyWLEzTxFxdkQcuJ3Mk1amGkmSJEk7uonPPEXEm4Fn9S16JPCEgcxTgNOBeeBnwMsz80fAuwdeS0RcAOw2sJqDgbWZuWl5q5ckSZKmV0kTTxGxN3AyMJ+Zb4mIRwAfBnYGvp6Zp1S5dwJH0BvrrMvM69pkt1XDxAdPmXk6vYERABHxaWBwkPN+4NjM/K+I+HXgXcC6hvaOG1wWERcBm5etaEmSJEkr7QzgX4D7VY/PBH4vM38QEedFxK8Ca4CHZOavR8RjgfcBz2mZbVTEYXsR8aCIeG/1cFd6s0sAu0fEzsCdmflf1bK/B/bse+3GiDh+e+vIzIZreUuSJEmqFSt4247M/B3gqwARsQrYOTN/UD39d8BTgGOAc6v894C92mS3V8PEZ54qq4C11f0zMnNT9L6d9lbgb4ELIuIM4NvA84CFgRaZeeQQ7c8ta7WSJEmSJmlv4Md9j38MPAp4MHBr3/LNwEOGzUbETGbON610ooOniHgqcBJwf+DQiDgX2CkiDqsif7AwQoyItcDDgFdm5sLM1DV9bR0NnNbX/P7AXcBt1fMbgdMz8+KBGtZRHQL4sGPfwN6HPH8Zt1CSJEnqrpW8zlP/7+WV9Zm5viF+O7BH3+M96Q2EdqHvKDV650y4bdjstgZOMPmZp/9D76QPm+l9z+k+4J7MvCcizh7InrNwJ7acM3FX4G0AmbkB2NCXORm4YXCwNKh6Q9YDHPLujR7aJ0mSJE1A/+/lQ2TvjoidImK/zLwFOAF4B3AQ8ELgyoh4NPDDNtntrXeig6fMvBO4MyLOysxXDjz9SvoOt6s7PC8itjkwkiRJkrR0hV/n6Q3A+RFxL/D5zLw+Im4EnhMRVwI/B16xhGyjSc88LThocEFmenY8SZIkSf8tMzcCG6v736J34of+5+eBV9W8bujstpQyeJqtvpM0aKvvKA346ZjqkSRJkqRFihg8ZebTlvi639zG02ez9fWiJEmSJA2p7KP2Vl4Rg6dxyMw7Jl2DJEmSpOkxtYMnSZIkSSNy6mmRmUkXIEmSJEld4MyTJEmSpForeZHcLnDw1Ocxv7jXSK+fb7jEbubo195dhiZqzTUVXVvDyl5DeFxrm2/YjqbNm6/po6Z+q+ujpi6eafhZVJfPMfVG0w/EpvXV9dGmzfUX4p6vuUB3Ux/X9Vs0XFiiaT9syo+6vs0N2zdsu+PUtL65uZX9XNf1XZv3qc01RNrsQ8uxvjb71UzTh7pF223bqG93tBqa2phpkZ1t2I6m9dW13dQVbd7TunYbsw0rrK+tKbv1sjZ93JRv6os2tdW+py22uSnfdAhT3Xasanm8U91+1Gb/btqO2RH377aa3r/jHr/P6I1rxTl4kiRJklSr8Ivkrji/8yRJkiRJQ3DmSZIkSVItJ54Wc+ZJkiRJkobgzJMkSZKkek49LeLMkyRJkiQNodiZp4jYD9g3M6+ddC2SJEnSjsjrPC1WzOApIi7OzGf1Lfol4DDg2oHcicDOmXl2QzsXALsNLD4YWJuZm5axZEmSJEk7kGIGT8Capici4tnAG6uHBwAZES+uHp+ZmRcuZDPzuJrXXwRsXsZaJUmSpKnndZ4WK2LwFL3LUD85ItYAvwI8AzgQ+BeAzPwS8KUqdwEwD5yQTZeUr9EmK0mSJEmDSjlhxNHAD4HjgRuA84Er+gMRsRb4BPDXwKeAv4mIhw7Z/lzTExGxLiKujYhr/+my85dQuiRJkqQdwcRnniJiFfA64JnAx4GLM/N7EfEgYL8q8/bq/juB26qXfht4S0TcnpmnRMTRwGl9Te8P3LWQj4iNwOmZeXH/+jNzPbAe4KRz/6+zU5IkSVLFo/YWm+jgqRo4/TlwdmbeHBGnAudVJ4X4b5n59r7XnFQt+wTw8r7MBmBDX+5k4IbBwZIkSZIkLcWkZ57WApdl5gUAmfmtiPgjwBkgSZIkadKcelpkooOnzLwJuGlg2bUAUZ3ao+ZwvIXlJ/U93OpwPEmSJElaTpOeedquwcPxJEmSJK0ML5K7WLGDp8zcCGwcoYmzAS+KK0mSJGlZFDt4GlVm3jHpGiRJkqQu8yK5i5VynSdJkiRJKtrUzjxJkiRJGo0TT4s58yRJkiRJQ3Dmqc+/33bXSK/PhqtTtTlWtKmNOvMN4aY2suaJ+fnhV9iUrWu3TQ1N+absuLRZ3bje0+Y2hu+3ptrG1cfRsMJRj5FuandcNTfV21THzMzwG1j32Wnajrm54bdvOfqizX7R5ufFcmjq+zpzc/NjqaGpL9rU1uYzshz7W5t9uandujaas1svn51t97fZNn3RprY6sy22oyk/0/Q+tdiO5jrGU1tdtmlfaaqtru02/dm0vqaa2/RFbbsNy2s/C421Db26VrV1ypRu1lI58yRJkiRJQ3DmSZIkSVItr/O0mDNPkiRJkjQEZ54kSZIk1ZrWr3ItlTNPkiRJkjSEYgdPEfGKITLPXolaJEmSJGnig6eIeE9EXFrdboyIl1VPHd+XOSsiDqx5+Rtr2rugr72F260RsXo8WyBJkiRNp1jBWxdM/DtPmXnqwv2I+BRwSZvXR8STgX/LzFur9o6ryVwEbB6xVEmSJEk7sIkPnhZExAnATZl5c9+yq4APNOR3pjdIfTJwF3DrttrPlb7iqiRJktRxnjBisYkPniJiBng9cABwSv9zmXlYlXk88FcRcR+Q1e3TvUieNcRq5pa1aEmSJEk7nIkOniJiD+CTwMcz84MDT//rwp3MfDvw9prXH9B3/2jgtL6n96c3I3Vb9fxG4PTMvHigjXXAOoBHvfhN7H/YVkf9SZIkSTsop576TXTwlJm3A8+NiCdExIXAmuqpGeCM/mxEfAx46EATuwNvq9raAGzoy58M3DA4WKqpYT2wHuCYP7vaQ/skSZIk1Zr4YXuVs4AXZOYtABGxG/DliLgmM2+rMmsz8xn9L4qIbQ6MJEmSJC2d33labOKnKq8sfI+p/zEDyyRJkiRpYkqZeXoV8NG+azHNAu/OzJ/0ZVZHxKUDr3vcilQnSZIk7YCceFqsiMFTZn4XeO52Mk9v2ezZwKYlFyVJkiRJfYoYPI1DZt4x6RokSZKkLvM7T4uV8p0nSZIkSSra1M48SZIkSRpN+K2nRZx5kiRJkqQhOPMkSZIkqZ4TT4tEppdSWrDLk17frc6IhonDpm/2zcxuvazp/a9rYzmybeT86G20Wl+LmtvUVkpf1O0v4/wW6Kjra9q/G/M1bbf5jDRlN99Xv7zuPZmfa1hfAX3Rpu22+0Xb+kbR9FlYjvd65H5raHemxc/kVusb4/s0rp8N4/qdo6neNv02rv246X1usx+2aaPFexcN2WjYZ+vyMzP121GXbfqdc26u4WfniBq3r812tOiLpvU1ue2vX9KJYcl//mzTiv1+vM/uq4vvEw/bkyRJkqQheNieJEmSpFrFTwWtMGeeJEmSJGkIzjxJkiRJquVFchdz5kmSJEmShtDpwVNEHBttT20iSZIkaSixgv91QVGH7UXEHwIzmXl637IDgI/XxN8IvBa4CNjcl78A2G0gezCwNjM3LXvRkiRJknYIRQyeIuL+wDpgb2BTRJwMfDQz78zMmyPiOODgzPxaRDwRuCUzf9RwDYHjatpfNMCSJEmSNIRuTAitmIkPniLiNcAvAufQG+DMVrfTI+KWahZqD+ClwNeA44ELgR+1WU96NWBJkiRJI5jo4Cki9gSuqm4Az6FX0+eBv6gyewGbgDVVZjVwX18zn4yIL2fmX2xjVeO5dLUkSZI0xZx4WmzSM09rgWf1PZ6nNzDqX3YRcAtbBk9r6A2mFrwkM+ci4mjgtL7l+wN3AbcBRMRG4PTMvLi/gIhYR++QQVY99ChWPeixI26SJEmSpGk00cFTZl4HXBcR+wFvAB5dPXU98MHq+077A08AHhIRhwIHAI+LiJ0Wmqna2gBsWGi7+t7UDYODpZoa1gPrAXZ50us9tE+SJEmqeF7rxSY987TgHOAPgW9Wj38FOBc4jN4M0uOBzwGHAl8H9mTLGfUc8EiSJEkau1IGT7sA1y+c1CEi/hHYCSAzrwaurntRRHgiCEmSJGlMunL9pZVSyuDpFOC8vlOPB/CmyZUjSZIkSYsVMXjKzCuAK5bwumds4+mzWXxiCUmSJEkt+J2nxYoYPI1DZt4x6RokSZIkTY+ZSRcgSZIkSV3g4EmSJEmShuDgSZIkSZKGMLXfeVqKJ/3WC7dattInQm8683q0+LZeU7TNttTVMTdX38D8/PANt8m2OQt9m/5ZDk2rq6tjnKWNun8uR21NNczMjN4Xo+73y/G5aWpjJd/r5hra5YdtY6blhrTp+5rdotX2NZ0yt65dqN8Pm7Zv1H1otqGIphbq2m7ajjp127YtdfU1bd9sXW1N29eijKZ/A+Za/NvQRpv3umn7Ru23VQ1/pm73Galvo81ndbbFZ322oea6fFMJtftQ43Y0tNFiH1/pkxrMzdcta9i/O35VHU8YsZgzT5KKtNIDYkk7Bn+0SBqFM0+SJEmSanmR3MWceZIkSZKkITjzJEmSJKmWh7ou5syTJEmSJA3BmSdJkiRJtZx4WqzYmaeIeMWka5AkSZKkBROfeYqI9wCHVA8PAN6XmWcDxwMf6ct9H7hp4OV3Z+azB9q7ANhtIHcwsDYzNy1n7ZIkSdJUc+ppkYkPnjLz1IX7EfEp4JKG6E2ZeeQQ7R03uCwiLgI2L7VGSZIkSZMXEf8A/Lh6uB74NvBhYGfg65l5SpV7J3AEvfHOusy8LiIeUZdtY+KDpwURcQK9AdLNfcuuAj6QmZ8BZiLiUnqzSgH8vIodm5l3bq/9zI5f3lmSJElaYQVe5+n/ZeYzFh5ExJeA38vMH0TEeRHxq8Aa4CGZ+esR8VjgfcBzgDMHs5l5TZuVT3zwFBEzwOvpHbK3aPSXmYf13T+iyp8IrMrMT7RYzdzolUqSJEmasPmFOxGxCtg5M39QLfo74CnAA4FzATLzexGx1zay3Rk8RcQewCeBj2fmBwee/tcqcyjwsr7lM73FcVjfso8BuwKn9S3bH7gLuK1qZyNwemZePFDDOmAdwMNf8Afsc+j/HHGrJEmSpOmwktd56v+9vLI+M9f3Pb8r8PCI+Crwn8Ab2XIIH9X9RwEPBm7tW74ZeEhDtpWJDp4y83bguRHxhIi4kN4UG/QGSGdUmauBqyNiP+A1wGOqzHXAhzLzlr4mNyzciYiTgRsGB0s1Naynd7wkh73/Sg/tkyRJkiag//fyhufvBB4OEBFHAx8A9uiL7Elv0LRLdX/BPL0JlbpsK6Wcqvws4BWZeUxmHgMcB7wlIvbqy3wauBh4YXX7EnDeilcqSZIkacVFxGzfw1uBBHaqJlkATgAuA66kN14gIh4N/DAz727ItjLx7zxVsrr1P/7v/1cdtRPw7cy8r1r2HWBNRKz2FOSSJEnS8ivsdBEHRcTHgPuq26vofb/p/Ii4F/h8Zl4fETcCz4mIK+mdZG7h+rFvGMy2LaCUwdOrgI9GxOrq8Szw7sz8CUBmzkXEacDnYvGBl6c5cJIkSZKmX2beCPzawOLv0zvxQ39unt74YvD13xrMtlXE4Ckzvws8dzuZS2i+BlSdswEHVpIkSdJSFTb1NGlFDJ7GITPvmHQNkiRJkqbH1A6eJEmSJI2mwIvkTlQpZ9uTJEmSpKI58yRJkiSp1kpeJLcLnHmSJEmSpGFkpreaG7CuS9lS6ighW0odJWRLqaOEbCl1dC1bSh0lZEupo4RsKXWUkC2ljq5lS6mjhKy3bt0mXkCpN+DaLmVLqaOEbCl1lJAtpY4SsqXU0bVsKXWUkC2ljhKypdRRQraUOrqWLaWOErLeunXzsD1JkiRJGoKDJ0mSJEkagoOnZus7li2ljhKypdRRQraUOkrIllJH17Kl1FFCtpQ6SsiWUkcJ2VLq6Fq2lDpKyKpDojouU5IkSZK0Dc48SZIkSdIQHDxJkiRJ0hAcPEmSJEnSEBw8LUFE7BMRq6v7R0TEcxpyeww8flZEPKsh+/6I2K9FDasj4qSI+LOI+EhEnBoRD2uzHcvBvlhUh32xpQ77olfDTEQcGhEPrh7/TkS8eqFvBrKP7Ls/GxGvrG512fMj4klD1vCAiHhHRHwxIi6p+uKwUbZrqSa9X5SwT/TVYl9sqcW+2FKLfbGlFvtCRfKEEUBE/I+m5zLznway7wWeCGwGPgMcBvwImMvMNw9kL8/Mo6r7pwEHAbcDmzLzDwey1wH/APwQODMzf7idmv8G+D5wIXAUcH/gAOCSzPzkCNtnXywta19syU57XxyzjeyXB7J/BdwL7FnVvgb4d+CJmfmygWx/X3wAuAP4MfCEzHzpQPbbwFXA3sCfZuY1TTVFxOeBC+j1w9OBXwB2B27NzDNq8m22r1P7RZt9YgnbZ18sLWtfbMl2qi/abFvb/LT3hbpt1aQLKMRZbPlFpV8Cvzuw7IjMPDQidgduBA7IzM0R8ZXtrONZwJGZORcRX615/keZ+eKIeApwZkQAfBb4Wmb+oCZ/QGaeWN3/ZkR8EXgecCUw+MO+zfbZF0vL2hdbTHtfvBz4OfBvNdkvDyz7hcw8PCJ2Br6fmWsBIuKymnr7HZKZh1fZun77aWa+PiIeCrwhIk4HLqXXFxsHsrtn5seq++dGxCWZ+cyI+Bqw1eCp5fZ1bb9os0+03T77YmlZ+2KLrvVFm21rm5/2vlCHOXjqeTNwYma+bojszwEy82cRcUNmbq6Wz9ZkHxYRr6X3F5CdM3OuWl433ZdVu98AXhgR+wK/CXwQOL4mf0dEPCEzvxsRRwF3Z+Z8RNS13Wb77IulZe2LLaa9L94CvDkz3zFE9u6q5nsG/tHfqSb7iIg4g15f3L9veePP6cy8CTg5InYCng0cC2wciN0aEccBXwFeQG/ma1vabF/X9os2+0Tb7bMvlpa1L7boWl+02ba2+WnvC3VZZnrrHbr4q0Pmng7sOrBsd+BtNdnD6P2V4kTg96pls8Dv12S/0LLe/YFzgK/T+wvIPtXyl4yyffaFfWFfDNX2Q4fMPRa4X01fvLyh3sdWfXJMX1+8uCb70Ra1PgB4D/AF4N0L7w3wtFG3r2v7Rdt9Ygn7hX1hX+xQfdGmH+wLb9Ny8ztPfarp4T3pfRfgrtKzVf4BwB7D5odVwvbZF0vLVnn7Ykt+2fsiItYAj1yoA7ghM+cLzz6Kqh+2lV2KEt7rltmxfD6WUEcJWftiS9a+2JK1L7Zkx9YX6iYHT0BEHAG8j96xqrfR+0DNAm/MzOsGsocD759ktqbmHwN7bStvX9gX9sWy9cVv0DtE4+q+Oh5D7y+iVwyZfWtmfnWEdl8EnNqihjcB12wvu4S+6NR+Ma59wr6wL+wL+0I7kHFNaXXpBnwDePDAsgcDG0rMLqHtj9Kbeu6/nQucU+L22Rf2RQf6YvBQvPsBl3c9Wz33eXqHqfTfvgF8vcT3esz70NR+RuwL+2IZskP3Q0E1F9EX3rp984QRPfdl5o/6F2TmjyLifoVm2+avp3e6z881tLUSNdsX48+2zdsXW7Tpi03AfQPL7qU3IOl6FnqnNSe3nKFvW0p4r8e5D03zZ8S+WFoW7IsFbfqhlJpL6Qt1mIOnnnMj4kLgPHrfB9iL3lmpLi402za/HnhnZg6eenjUdkvIts3bF1vYF1u06YszgK9ExKV97T6T3l8eu56F3hej/6DhuUElvNfj3Iem+TNiXywtC/bFgjb9UErNpfSFOszvPFWid72UZ9Cbkr0duCozv1dqdin5YZWwffbF0rJLyQ+rhO0rqC/uBxza1+63MnPw+h6dzLZVwns9zn2ojRJqti/Gn22rhJrti/FntQPJAo4dLO0GvKhL2VLqKCFbSh0lZEupo4TsmOt47bRml9D2xN/rju5DncqWUkcJ2VLq6Fq2lDpKyHrr3m0G1XlVx7Kl1FFCtpQ6SsiWUkcJ2XG2XXeB3mnJts2X8F53cR/qWraUOkrIllJH17Kl1FFCVh3j4KledCxbSh0lZEupo4RsKXWUkC2ljq5lS6mjhGwpdZSQLaWOErKl1NG1bCl1lJBV10x66qvEG3BIl7Kl1FFCtpQ6SsiWUkcJ2THXccC0ZpfQ9sTf647uQ53KllJHCdlS6uhatpQ6Ssh6697NmadKRBwSEa+LiLcBh0TEI0rOjrvtBZn5rWFypWSXu+2IeGBE3L8/GxFHlpodd9t9fhwRjx4iV0p2bG1n5s0Rceo0ZpeQvyMiHjml2W3mI2LPvvvHA8dExPOnIbuEtl8Tfadz3tbP2a5ll5h/YkQcWD18ckScHhEP2tGyVf7IiDil+plyUETsuiNm1W2ebQ+IiD8BDgQ+Q+8q0nsCJwDXZOaZpWXHXMeapn7KzPtKy465jjcBxwH/BVyZme+tll+emUeVlh1zHScDvwv8G/AR4DXAT4Fv1OxDE8+OuY5vAAs/OBcOzTgI+OfMfGqXs0to++LMfFZ1/2XAbwA/o3dB3Q90ObuEtq/MzMMj4r3AzsAXgSOBnTPz5C5nl9D2vwL/BFwE/EVm3j3YXlezS2j7TOCBwP3pna3tWuA/gJdm5vN2lGyV/2CVvxB4GnAPvd9NPpyZG3aUrKbASkxvlX6j94tj3fLLS8yOuY6/B64GLge+0v//ErNjruObffdfBZxQ3f9Kidkx13Ft9f99gVuAXarHV5SYHXMdxwLnA8f1LTu3oYZOZZfQ9uV996+g90t1UPMzp2vZJbT9tbp9hvrPU6eyS2j7K1U//Ra9n7FnAr8GrO56dgltX1H9fwa4vm7f2hGy1fKrBh5/nt5nqu7zNLVZb92/edhez70RcUD/gojYl96OX2J2nG2fAlyamUdl5tP6/19odpxt37lwJzP/HDg6Ig5iy1/lS8uOs+2fVLn/AH6Q2/7LbAnZsbWdmZ+jNwPx0Ig4JyL2puH96Fp2CfmHRMTzI+JwYE1m3pOZCcxNQbZt/psRcRLw9Yj4ZYCIeCzUfnG8a9m2+aycW/1sPQd4EXDVFGTb5u+KiKPoXVz1fhGxNnqH/NUdBTHNWej9LrJhAB1HAAAEWklEQVQWIHqHv67OzHuo34emOauOWzXpAgrxauAjEbETWw5rWwO8rtDs2NrOzEtjyON0S8iOue2zIuKXM/M71eNTgA8Bjys0O862Px4Ru2bmnfRmJoiI3YHrC82Ote3MnAf+d0RcBpwLzDbU0Llsy/x7gIcBBwOXAETELPDNKci2zb8BeAXwGOClEfEfwE3AuinIts0v+oUxM79Jcx93Lds2/zJ6ffczeheq/hiwD/DHO1gW4GTgr6oB1j3AK6vln93Bsuo4v/PUp/pHcW/g9uovBkVnx922pO2r/ijx9Mz84rRll5KX+v4QMXXZpeQlTRcHT5IkSZI0BA/bAyLiHcDquucy849Ky5ZSRwnZUuooIVtKHSVkS6mja9lS6ighW0odJWRLqaOEbCl1dC1bSh0lZNV9Dp56ZoG7gCs7ki2ljhKypdRRQraUOkrIllJH17Kl1FFCtpQ6SsiWUkcJ2VLq6Fq2lDpKyKrrsoBT/k36BjwI+MuuZEupo4RsKXWUkC2ljhKypdTRtWwpdZSQLaWOErKl1FFCtpQ6upYtpY4Sst66f/M7T5IkSZI0BA/bq0TEIcBT6J3G+1bgssy8sdRsKXWUkC2ljhKypdRRQraUOrqWLaWOErKl1FFCtpQ6SsiWUkfXsqXUUUJW3ebMExARfwL8AvB3bLkO0gnANZl5ZmnZUuooIVtKHSVkS6mjhGwpdXQtW0odJWRLqaOEbCl1lJAtpY6uZUupo4SspkDT8Xw70g24smH55SVmS6mjhGwpdZSQLaWOErKl1NG1bCl1lJAtpY4SsqXUUUK2lDq6li2ljhKy3rp/m0EA90bEAf0LImJfYOdCs6XUUUK2lDpKyJZSRwnZUuroWraUOkrIllJHCdlS6ighW0odXcuWUkcJWXWc33nqeTXwkYjYiS3TrWuA1xaaLaWOErKl1FFCtpQ6SsiWUkfXsqXUUUK2lDpKyJZSRwnZUuroWraUOkrIquMcPPXcC3y/+v/7gZ9k5j0R8QngpAKzpdRRQraUOkrIllJHCdlS6uhatpQ6SsiWUkcJ2VLqKCFbSh1dy5ZSRwlZdd2kjxss4QZsBJ5N7ywp5wN7VMu/UmK2lDpKyJZSRwnZUuooIVtKHV3LllJHCdlS6ighW0odJWRLqaNr2VLqKCHrrfu3iRdQwq1/5wb2A/68ul/3pcCJZ0upo4RsKXWUkC2ljhKypdTRtWwpdZSQLaWOErKl1FFCtpQ6upYtpY4Sst66f/OEET3/GRH7AGTmLcCFEXEW8OBCs6XUUUK2lDpKyJZSRwnZUuroWraUOkrIllJHCdlS6ighW0odXcuWUkcJWXXdpEdvJdyABwD7Dix7HHBKidlS6ighW0odJWRLqaOEbCl1dC1bSh0lZEupo4RsKXWUkC2ljq5lS6mjhKy37t+8SK4kSZIkDcHD9iRJkiRpCA6eJEmSJGkIDp4kSZIkaQgOniRJkiRpCA6eJEmSJGkI/x8RVBXOzUBw7QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 2010~2019 구별 변화추세 Heatmap\n",
    "plt.figure(figsize=(15,10))\n",
    "sns.heatmap(pop_gu,cmap='Blues')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 파일저장하기 \n",
    "pop_gu.to_csv('./data/pop_gu.csv')\n",
    "population.to_csv('./data/pop_day.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 시계열분석_ARIMA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 회귀분석을 먼저 해볼꽈??\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import classification_report"
   ]
  },
  {
   "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>gu</th>\n",
       "      <th>종로구</th>\n",
       "      <th>중구</th>\n",
       "      <th>용산구</th>\n",
       "      <th>성동구</th>\n",
       "      <th>광진구</th>\n",
       "      <th>동대문구</th>\n",
       "      <th>중랑구</th>\n",
       "      <th>성북구</th>\n",
       "      <th>강북구</th>\n",
       "      <th>도봉구</th>\n",
       "      <th>...</th>\n",
       "      <th>강서구</th>\n",
       "      <th>구로구</th>\n",
       "      <th>금천구</th>\n",
       "      <th>영등포구</th>\n",
       "      <th>동작구</th>\n",
       "      <th>관악구</th>\n",
       "      <th>서초구</th>\n",
       "      <th>강남구</th>\n",
       "      <th>송파구</th>\n",
       "      <th>강동구</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-01</th>\n",
       "      <td>6420.0</td>\n",
       "      <td>5725.0</td>\n",
       "      <td>11797.0</td>\n",
       "      <td>16416.0</td>\n",
       "      <td>17564.0</td>\n",
       "      <td>16889.0</td>\n",
       "      <td>19965.0</td>\n",
       "      <td>25735.0</td>\n",
       "      <td>15480.0</td>\n",
       "      <td>17426.0</td>\n",
       "      <td>...</td>\n",
       "      <td>32152.0</td>\n",
       "      <td>24889.0</td>\n",
       "      <td>11604.0</td>\n",
       "      <td>22597.0</td>\n",
       "      <td>20383.0</td>\n",
       "      <td>25387.0</td>\n",
       "      <td>21112.0</td>\n",
       "      <td>23460.0</td>\n",
       "      <td>35186.0</td>\n",
       "      <td>24642.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-02</th>\n",
       "      <td>6416.0</td>\n",
       "      <td>5721.0</td>\n",
       "      <td>11732.0</td>\n",
       "      <td>16328.0</td>\n",
       "      <td>17518.0</td>\n",
       "      <td>16796.0</td>\n",
       "      <td>20036.0</td>\n",
       "      <td>25625.0</td>\n",
       "      <td>15407.0</td>\n",
       "      <td>17338.0</td>\n",
       "      <td>...</td>\n",
       "      <td>31992.0</td>\n",
       "      <td>24800.0</td>\n",
       "      <td>11529.0</td>\n",
       "      <td>22534.0</td>\n",
       "      <td>20352.0</td>\n",
       "      <td>25233.0</td>\n",
       "      <td>21125.0</td>\n",
       "      <td>23531.0</td>\n",
       "      <td>35195.0</td>\n",
       "      <td>24714.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-03</th>\n",
       "      <td>6372.0</td>\n",
       "      <td>5702.0</td>\n",
       "      <td>11677.0</td>\n",
       "      <td>16267.0</td>\n",
       "      <td>17436.0</td>\n",
       "      <td>16819.0</td>\n",
       "      <td>20038.0</td>\n",
       "      <td>25565.0</td>\n",
       "      <td>15320.0</td>\n",
       "      <td>17248.0</td>\n",
       "      <td>...</td>\n",
       "      <td>31816.0</td>\n",
       "      <td>24687.0</td>\n",
       "      <td>11441.0</td>\n",
       "      <td>22351.0</td>\n",
       "      <td>20205.0</td>\n",
       "      <td>25137.0</td>\n",
       "      <td>21135.0</td>\n",
       "      <td>23511.0</td>\n",
       "      <td>35136.0</td>\n",
       "      <td>24698.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "gu          종로구      중구      용산구      성동구      광진구     동대문구      중랑구      성북구  \\\n",
       "2010-01  6420.0  5725.0  11797.0  16416.0  17564.0  16889.0  19965.0  25735.0   \n",
       "2010-02  6416.0  5721.0  11732.0  16328.0  17518.0  16796.0  20036.0  25625.0   \n",
       "2010-03  6372.0  5702.0  11677.0  16267.0  17436.0  16819.0  20038.0  25565.0   \n",
       "\n",
       "gu           강북구      도봉구  ...      강서구      구로구      금천구     영등포구      동작구  \\\n",
       "2010-01  15480.0  17426.0  ...  32152.0  24889.0  11604.0  22597.0  20383.0   \n",
       "2010-02  15407.0  17338.0  ...  31992.0  24800.0  11529.0  22534.0  20352.0   \n",
       "2010-03  15320.0  17248.0  ...  31816.0  24687.0  11441.0  22351.0  20205.0   \n",
       "\n",
       "gu           관악구      서초구      강남구      송파구      강동구  \n",
       "2010-01  25387.0  21112.0  23460.0  35186.0  24642.0  \n",
       "2010-02  25233.0  21125.0  23531.0  35195.0  24714.0  \n",
       "2010-03  25137.0  21135.0  23511.0  35136.0  24698.0  \n",
       "\n",
       "[3 rows x 25 columns]"
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 파일 불러오기\n",
    "population = pd.read_csv('./data/pop_gu.csv', index_col=0)\n",
    "population = population.T\n",
    "population.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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3sDD8rYUm5sp874kpbn9gjAeOzs7fQdKs0zVzQLhW/qnX6PUAeTkg3EzYHwJeA5AdgH24NkNSD0AW/kezybcCL5fUK2kLcBHw3ZU02hYqJymPjM/xzQfHuPvRSZ6cdpnG8sHXAyxfM2WczwNXSjpEtfd+laQPANdSLeG8FUiA+4H9EVGUdAPwNWAOeG9EbLwblbfBxGyZJ6YKjLkGbznl6wGWr+Gwz3rtVy+aXDvk/onstXidjwAfabh19hQRUEpSvvngGIU1eNaqWadr5uyfdj0joJ03ifMVtOtEqZLy2MRcdtOxcNCbrUC7HhHZzr0CX0Hb4YqVhH97coZvPjjGo+MFwlc9mbXEWh4QhvYfFHbYd6hCOeH7o9N888FxHp8ouCZv1gGaPSAMKzso3Aou43SYuVLCI+OzPDld8q0LzDrMSp4RsJInh7WCe/YdYqZY4V+fmOJbD48zOuWgN+tUzZR/YGV7Ba3gnn2bTRXKPDI+x9hMud1NMbNVtJK9gpZ8/pp8ij1FkgazpYQ7H5l00JvlRLN7Ba3gnv0aqyQpD43NMVOqVG9DaWa2Bhz2a+jodDE7Ih8OejNbUw77NVAoJ9x/dMblGjNrG4f9KooIHpso8PDYHIlPlDezNnLYr5LpYoX7RqeZKfoWw2bWfg77FkvS4MFjszwxWfC58mbWMRz2LVQ9ADv7lEuizczazWHfAmkE9zw+xbGZUrubYma2JIf9CkQEpUpKsZI66M2soznsmzRTrHDf6AyFsg/Amlnnc9g3KE2Dh8fmeHRizgdgzWzdcNg3YGK2zH1PTvspUWa27jQV9pKuA16Yrb8/Iu7Kpj8H+B1gM/AY8PMRUZL0UeB8oATcGhHvakXj10o5SXng6CyjU8V2N8XMrCkNh72kFwB7I+IySRcB1wNXZrMDeEVEFCVdD/wk8ClgCLgiIiZa1O41MzpV5IGjM5QT12zMbP1qpmf/UuATABFxp6SdtRkR8e265caA2vO2BoHJk72ppP3AfoAzzjijiWa1VqFcffbr+KzvZ2Nm618z97MfBkbrxiuSFryPpH8PXAh8IZsUwEFJX8z2DJ4iIg5ExEhEjOzZs6eJZrVGRPDo+Bz/7+EJB72ZbRjN9OwngB1142lEpACSBFwD9AKvj4gEICJels3fB3weeM5KGr1aaqdTThcr7W6KmVlLNdOzPwS8BkDSBcDDdfPeAjwWEdfVgj5brrZRGQM6rrscVMs2335kwkFvZhtSMz37zwNXSjoETAFXSfoAcC3wCmBI0i9my/59RPwu8I9Z4HcD72lBu1tmYrbMTLFCmobPmzezDavhsM9KNlcvmnxN9u+VLCEiXtLo56y26umUM4xOlUh9r3kz2+ByeVGVT6c0s7zJVdj7dEozy6tmDtCuWw+PzTnozSyXchX2ZmZ55bA3M8sBh72ZWQ447M3McsBhb2aWAw57M7MccNibmeWAw97MLAcc9mZmOeCwNzPLAYe9mVkOOOzNzHLAYW9mlgMOezOzHHDYm5nlgMPezCwHmgp7SddJuknSzZIurJs+IOkTkr4q6TOStmXTXynpkKRbJL22VY03M7PlUURjz2GV9ALgdRGxX9JFwAcj4sps3rXA9yPiLyW9FRgA/gj4AvBiqo9B/Brw/IgonOgzdp55fvz4ez7W8B9zx7fuAOCSiy9Zcv5cOaGcpE+Z/r277wTg3Asuavgzm13X63XOZ3q9zvnMjb7eqdbt7e5ic293w+9Z88m3/OhtETGy1Lxmwv464MsR8ZVs/BsR8bxs+CvASyOiLOk04MPAHwIvjIj3Zst8GPhwRNyx6H33A/sBBp529g9f+d6PN9Su5ThR2JuZdYLVDPtmHjg+DIzWjVckdUVECvRHRO0hr0eBHUssX5u+QEQcAA4AjIyMxF9f9fwmmnZy9x6ZZnSq2PL3NTNrhd0DfZy7d7Dp9T/5lhPPa6ZmP8HCsE6zoAdIJdXecwfVkF+8fG26mZmtkWbC/hDwGgBJFwAP1827BfjJbPjVwJeAW4GXS+qVtAW4CPhu0y02M7OGNRP2nwf6JB0Cfge4RtIHJPUB7wf2SzoI/DDw5xHxJHAD1QOz/wd4b0RUWtF4MzNbnoZr9lnJ5upFk6/J/n0SuGKJdT4CfKTh1pmZWUv4oiozsxxw2JuZ5YDD3swsBxz2ZmY54LA3M8sBh72ZWQ447M3McsBhb2aWAw57M7MccNibmeWAw97MLAcc9mZmOeCwNzPLgVyFfW+32t0EM7O2yFXYn7lrK2fv2UqPQ9/MciZXYQ8wvG0TFz9jiN0Dfe1uipnZmsld2AP09XRx7t5Bnn3aIP29ufwKzCxnGn5S1UayY2sf2zb38tCxWR6fLBDR7haZma2O3Hdru7vEWbu3ctHTt7O1v7vdzTEzWxUNh72kqyV9VdItki5bNO8MSZ+TdJOkL0rakU2/VtLtkg5K+t+tanwrDfT38INP384Zu7bQ5eO3ZrbBNFTGkXQm8ArgMmAY+Bxwad0i/cDPR8SEpLcCbwKuB4aAX4yIb7Wk1atEEk8f2syurX3cNzrDxFy53U0yM2uJRnv2LwE+FVVPAMckDdVmRsT3ImIiGx0DZrLhoWz8hCTtl3RY0uHR0dEGm9Vam3q7ueD0bZw9vNXn5pvZhtBo2A8D9Ul8FNixeCFJ5wOvAWolGwF/kZVxXrXUG0fEgYgYiYiRPXv2NNis1TE8uImL9w2xZ9CnaZrZ+nbKMo6kS4EPZqNfYWG472Bh+CPpDcCFVMs5swAR8YZs3g7gy5K+XLcH0NF6u7s4Z3iQ3QMl7ntyhmI5bXeTzMwadsqefUTcGhGXR8TlwN8CrwaQNAz0RMR0bVlJVwDPiIh31oI+m17bqEwBBWDdneQ4tKWPi58xxOlDm5ArO2a2zjR0gDYivi3pm5K+DswBbweQ9G7gBqoHb58n6UXZKrdGxLuAGyTtyz7vjyNislV/wFrq7hJn7trKroF+7hudZqaYtLtJZmbLoujAK4lGRkbi8OHD7W7GSUUEj00UeOjYLGnnfYVmtg7tHujj3L2DTa8v6baIGFlqXu4vqmqWJE4f2szF+4YY2tLb7uaYmZ2Uw36FNvV2c/7TtnHO8AB9PS7mm1lncti3yJ7Bfi5+xhDD2/rb3RQzs6dw2LdQT3cXZ+8Z4MKnb2NLn++zY2adw2G/CrZt6uU5z9jOvp2bfZ8dM+sIDvtVIoln7NjCxfuG2L7ZB3DNrL0c9qusdp+dc4YHfJ8dM2ubXD+8ZC3tGexnaEsvDx6b5chksd3NMbOccc9+DfVmB3AvOH0bm30A18zWkMO+DbZv7uXiZ2zn7D1b2eRn4JrZGnAZp00kMbxtE3sG+3lyusQj43PMlXyvHTNbHQ77NpPEnsF+9gz2c3S6yCPjc77Bmpm1nMO+g+wa6GfXQD9jM9We/lSh0u4mmdkG4bDvQDu29rFjax8Ts2UeHp9lcs6hb2Yr47DvYNu39LJ9y3YmC2UeGZtjfNYPQDez5jjs14Ftm3rZ9rReposVHhmb49hMqd1NMrN1xmG/jgz093DeaYMUyglHJouMThcoVfzkFDM7NYf9OrSpt5szdm1h387NjM2WeWKywMRcmQ586JiZdQiH/TomiZ1b+9i5tY9COWF0qsiRqSKlStrupplZh2n48k1JV0v6qqRbJF22xPwjkg5mrxdl0340W+efJb29FQ23hTb1drNv5xaee8YQ5502yI6tvcj3XTOzTEM9e0lnAq8ALgOGgc8Bl9bNHwRujohX1U0T8MFsvUngJkmfjIhHV958W6y+t1+sVGv77u2bWaM9+5cAn4qqJ4Bjkobq5g8BY4vW+QHg+xExFhEJ8A/UbSBqJO2XdFjS4dHR0QabZUvp7zne23/2aYPsGujzw1TMcqrRsB8G6pP4KLCjbnwAeL6kr0n6WLYhONU6AETEgYgYiYiRPXv2NNgsOxlJ7Njax7P2DjJy1k7O3TvAzq0OfrM8OWUZR9KlVMswAF9hYVDvoC7II+I7wPnZem8G3gPcsMQ6d6+k0da87i6xe6Cf3QP9JGlwbKbE0ZkiE7NlUp/NY7ZhnTLsI+JW4HIAST8IXAd8XNIw0BMR07VlJfVERO3a/lHgXOB7wHOyev4s8FLgI638I6w53V3Hb8JWSVKOzZY4NlNifNancZptNA0doI2Ib0v6pqSvA3PA2wEkvZtqD/7Zkn4bKAHjwBsjoizpfcCN2TofjojFdX1rs57uLoYHNzE8uKka/DMljs6UfP6+2Qah6MD/k0dGRuLw4cPtboYB5SRlbLba25+YK1NJOu/3YrZR7B7o49y9g02vL+m2iBhZap4vqrKT6q3r8UcEk4UK41n4z/phK2brhsPelk0S2zf3sn1zL2fugkI5YXy2zPhcyQd4zTqcw96atqm3m9O2d3Pa9k2kaTBZKDM2W2ZstkSx7Iu4zDqJw95aoqtLDG3pY2hLH89kK3OlpNrjnyszVai41m/WZg57WxWb+7rZ3LeZp23fTEQwU0qYnCvPh3/imo/ZmnLY26qTxEB/DwP9PZw+VA3/6WKFyUKFSYe/2Zpw2Nuak8Tgpl4GN/Xy9Cz8p4rV4J+cqzBV8MFes1Zz2FvbSao+enFTL+yANA1mShWmixWmCtWX79pptjIOe+s4XV3He/5P216dVqwkTGfBP12sMFOsuPdv1gCHva0L/T3d9A90s2ugH6j2/qdLFaYLtT2Asp/Ha3YSDntbl7q66ko/mUI5YaZYYaaYMFOq9v7LPuXTDHDY2wayqbebTb3d7Bo4Pq22AZgtJfPlH28ALI8c9rahzW8A6qYVK0m191+sloBmSxWXgGzDc9hb7vT3dNPf083OrX3z00qVlNlSdQ9gtpQwV0qYLfkgsG0cDnszoK+ni76ePoa2HJ8WERTKCzcCs6UKBd/3x9Yhh73ZCUjKbvuwsAyUpMFsqZL1/hPmytWXb/5mncxhb9ag7rrrAOolaVSDv5RQyDYAtWGXg6zdHPZmLdLddfweQPUigmIlre4J1G0AipXEB4ZtzTjszVaZpPmzgnYsmldJUgqVlEI5yV6pNwS2KhoOe0lXAz8H9APvioib6ub9d+DHstE+YFNEPFfStcCrgEngwYh4/YpbbrYB9HR3MdDd9ZS9AaiWheY3AnUbhGIlpVRJ/SB4a0hDYS/pTOAVwGXAMPA54NLa/Ij4zbpl3wk8kI0OAb8YEd9aaYPN8qK7S2zt72HrEhuCWmmo+qoeHC5W0vmNQTnxxsAWarRn/xLgUxERwBOSjkkaiojx+oUkDQIvi4iXZpOGgLGTvbGk/cB+gDPOOKPBZpnlS31pCHqfMn9+Y1DONgaV4/+WspcPGudLo2E/DHy7bvwosAMYX7Tcm4A/rxsX8BeSEuD3I+LvFr9xRBwADgCMjIz4Z2i2AqfaGED1QrJSUg3+6jGC2nDqvYMN6JRhL+lS4IPZ6FdgwTGmHcDoEqu9GvgPtZGIeEP2XjuAL0v6ckRMNNtoM1u56oVkXdWjb0uIiPmNQamSUk5iwQainFSH/Xzh9eGUYR8RtwKXA0j6QeA64OOShoGeiJiuX17SDwH3RES5blpPRFSAKaAA+Ndh1uEkzd9a4mSSNCgnx/cGahuC6nDMD/sGdO3VUBknIr4t6ZuSvg7MAW8HkPRu4IaIeJzqhuHmRaveIGlf9nl/HBGTK265mXWE7i7R3VUrGZ1YbU+hnATluj2DchJU6obL3ltYFYoOLMqNjIzE4cOH290MM2uTiFgQ/OU0PT5cv1FIN9bGYfdAH+fuHWx6fUm3RcTIUvN8UZWZdRxJ9PWoekxhGWobh0pa3QBUsr2Fchok2caitqFI0upy5SRydQDaYW9m6978xoHlbRxqKtneQSXbKMxvLBaPJ1E9NpFmG4t1uCfhsDez3Orp7uIUx5+XFFEN/ySCNIU0qsORUp0WQVqbH9VnJqfZOmlUl58fr1u/u0ut/yMzDnszswZJoqdb6ypAG9vnMTOzdclhb2aWAw57M7MccNibmeWAw97MLAcc9mZmOeCwNzPLAYe9mVkOOOzNzHKgI+96KWmU48+vbdRu4MkWNmcj8XdzYv5uTszfzdI68Xs5MyL2LDWjI8N+JSQdPtEtPvPO382J+bs5MX83Sy+p2ekAAAJuSURBVFtv34vLOGZmOeCwNzPLgY0Y9gfa3YAO5u/mxPzdnJi/m6Wtq+9lw9XszczsqTZiz97MzBZx2JuZ5cCGCntJ10m6SdLNki5sd3s6iaRvSzqYvf5Tu9vTTpL2SPptSddl4+dJujH73Vzf7va10xLfzesk3Z39br7Y7va1i6QhSX+VfQ9flfTM9fa7WU9P1TopSS8A9kbEZZIuAq4HrmxzszrJExHxknY3okN8CLgX2JKN/x7wxoi4X9KnJP1IRNzSvua11eLvZgj49Yj4bPua1BG2AO+IiEcl/UfgV4EfYB39bjZSz/6lwCcAIuJOYGd7m9Nx0nY3oFNExOuBrwJI6gE2RcT92exPA89vU9Parv67yQwBY21qTseIiEcj4tFsdAwoss5+Nxsp7IeB0brxiqSN9Pc1TdJW4Oxs9/OTkva1u00dZA9wtG78KLCjTW3pRD3AByUdkrS/3Y1pN0lPp9qr/xDr7HezYco4wAQLv+w0ItybBSJiBjgbQNKPU/2h/kxbG9U5xqn2Xmt2sLDTkGsR8V7gvZK2AJ+VdHNE3NXudrWDpJ8AXgG8GZhlnf1uNlLP9xDwGgBJFwAPt7c5nUNSd91oR/8g11pEzAH9WY8N4KeAG9vYpI6SlbkA5oApIJcX5kh6DvCKiLgqIo6ux9/NRurZfx64UtIhqj/Kq9rcnk5yjqSPAaXsdXWb29Np3gH8jaQi8PcR8Z12N6iDvF/SpVSz4u8i4u52N6hNXg68QNLBbPxB1tnvxlfQmpnlwEYq45iZ2Qk47M3McsBhb2aWAw57M7MccNibmeWAw97MLAcc9mZmOfD/AdJhDJOp90xmAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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": [
    "from statsmodels.graphics.tsaplots import plot_acf, plot_pacf\n",
    "\n",
    "plt.figure(figsize=(10,6))\n",
    "diff_1=population['종로구'].diff(periods=1).iloc[1:]\n",
    "diff_1.plot()\n",
    "plot_acf(population['종로구'])\n",
    "plot_pacf(population['종로구'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                             ARIMA Model Results                              \n",
      "==============================================================================\n",
      "Dep. Variable:                  D.종로구   No. Observations:                  119\n",
      "Model:                 ARIMA(0, 1, 1)   Log Likelihood                -553.736\n",
      "Method:                       css-mle   S.D. of innovations             25.387\n",
      "Date:                Mon, 05 Oct 2020   AIC                           1113.471\n",
      "Time:                        15:08:01   BIC                           1121.809\n",
      "Sample:                             1   HQIC                          1116.857\n",
      "                                                                              \n",
      "===============================================================================\n",
      "                  coef    std err          z      P>|z|      [0.025      0.975]\n",
      "-------------------------------------------------------------------------------\n",
      "const         -16.8202      2.643     -6.364      0.000     -22.000     -11.640\n",
      "ma.L1.D.종로구     0.1368      0.076      1.796      0.073      -0.013       0.286\n",
      "                                    Roots                                    \n",
      "=============================================================================\n",
      "                  Real          Imaginary           Modulus         Frequency\n",
      "-----------------------------------------------------------------------------\n",
      "MA.1           -7.3086           +0.0000j            7.3086            0.5000\n",
      "-----------------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "from statsmodels.tsa.arima_model import ARIMA\n",
    "\n",
    "model = ARIMA(population['종로구'], order=(0,1,1))\n",
    "model_fit = model.fit(trend='c',full_output=True, disp=1)\n",
    "print(model_fit.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "model_fit.plot_predict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(array([4399.21177245]), array([25.38661601]), array([[4349.45491939, 4448.96862552]]))\n"
     ]
    }
   ],
   "source": [
    "fore = model_fit.forecast(steps=1)\n",
    "print(fore)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "\"['2019-12-00'] not found in axis\"",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-45-d87e3d57ec80>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpopulation\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'2019-12-00'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpopulation\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'2019-12-00'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mx_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx_test\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_test\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtrain_test_split\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtest_size\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0.2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36mdrop\u001b[1;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[0;32m   3995\u001b[0m             \u001b[0mlevel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3996\u001b[0m             \u001b[0minplace\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0minplace\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3997\u001b[1;33m             \u001b[0merrors\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3998\u001b[0m         )\n\u001b[0;32m   3999\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36mdrop\u001b[1;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[0;32m   3934\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabels\u001b[0m \u001b[1;32min\u001b[0m \u001b[0maxes\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3935\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mlabels\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3936\u001b[1;33m                 \u001b[0mobj\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mobj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_drop_axis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3937\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3938\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0minplace\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36m_drop_axis\u001b[1;34m(self, labels, axis, level, errors)\u001b[0m\n\u001b[0;32m   3968\u001b[0m                 \u001b[0mnew_axis\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3969\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3970\u001b[1;33m                 \u001b[0mnew_axis\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3971\u001b[0m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreindex\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[1;33m{\u001b[0m\u001b[0maxis_name\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mnew_axis\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3972\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mdrop\u001b[1;34m(self, labels, errors)\u001b[0m\n\u001b[0;32m   5016\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mmask\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0many\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5017\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0merrors\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[1;34m\"ignore\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 5018\u001b[1;33m                 \u001b[1;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mf\"{labels[mask]} not found in axis\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   5019\u001b[0m             \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m~\u001b[0m\u001b[0mmask\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5020\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdelete\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: \"['2019-12-00'] not found in axis\""
     ]
    }
   ],
   "source": [
    "x = population.drop(['2019-12-00'], axis=1)\n",
    "y = population['2019-12-00']\n",
    "x_train, x_test, y_train, y_test = train_test_split(x,y, test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
       "                   multi_class='auto', n_jobs=None, penalty='l2',\n",
       "                   random_state=None, solver='lbfgs', tol=0.0001, verbose=0,\n",
       "                   warm_start=False)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log = LogisticRegression()\n",
    "log.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([11365.,  4417.,  8038.,  4459., 18455.])"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions = log.predict(x_test)\n",
    "predictions[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "      4417.0       0.00      0.00      0.00       0.0\n",
      "      4459.0       0.00      0.00      0.00       0.0\n",
      "      8038.0       0.00      0.00      0.00       0.0\n",
      "     11365.0       0.00      0.00      0.00       0.0\n",
      "     12801.0       0.00      0.00      0.00       1.0\n",
      "     14221.0       0.00      0.00      0.00       1.0\n",
      "     15527.0       0.00      0.00      0.00       1.0\n",
      "     18455.0       0.00      0.00      0.00       0.0\n",
      "     18989.0       0.00      0.00      0.00       1.0\n",
      "     19341.0       0.00      0.00      0.00       1.0\n",
      "\n",
      "    accuracy                           0.00       5.0\n",
      "   macro avg       0.00      0.00      0.00       5.0\n",
      "weighted avg       0.00      0.00      0.00       5.0\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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