입국통계.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "fb6dcbe5",
   "metadata": {},
   "source": [
    "## 외국에서 한국으로 입국 통계 ( 2019/06 ~ 2021.12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>국적별(1)</th>\n",
       "      <th>국적별(2)</th>\n",
       "      <th>Jun-19</th>\n",
       "      <th>Jul-19</th>\n",
       "      <th>Aug-19</th>\n",
       "      <th>Sep-19</th>\n",
       "      <th>Oct-19</th>\n",
       "      <th>Nov-19</th>\n",
       "      <th>Dec-19</th>\n",
       "      <th>Jan-20</th>\n",
       "      <th>...</th>\n",
       "      <th>Mar-21</th>\n",
       "      <th>Apr-21</th>\n",
       "      <th>May-21</th>\n",
       "      <th>Jun-21</th>\n",
       "      <th>Jul-21</th>\n",
       "      <th>Aug-21</th>\n",
       "      <th>Sep-21</th>\n",
       "      <th>Oct-21</th>\n",
       "      <th>Nov-21</th>\n",
       "      <th>Dec-21</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>총계</td>\n",
       "      <td>소계</td>\n",
       "      <td>1237840</td>\n",
       "      <td>1218767</td>\n",
       "      <td>1251764</td>\n",
       "      <td>1178421</td>\n",
       "      <td>1403576</td>\n",
       "      <td>1234503</td>\n",
       "      <td>1247337</td>\n",
       "      <td>1030497</td>\n",
       "      <td>...</td>\n",
       "      <td>11169</td>\n",
       "      <td>11492</td>\n",
       "      <td>15381</td>\n",
       "      <td>17847</td>\n",
       "      <td>20297</td>\n",
       "      <td>18452</td>\n",
       "      <td>19611</td>\n",
       "      <td>25698</td>\n",
       "      <td>28734</td>\n",
       "      <td>25630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>아시아주</td>\n",
       "      <td>소계</td>\n",
       "      <td>1047692</td>\n",
       "      <td>1032024</td>\n",
       "      <td>1080414</td>\n",
       "      <td>983538</td>\n",
       "      <td>1185096</td>\n",
       "      <td>1066538</td>\n",
       "      <td>1094768</td>\n",
       "      <td>906808</td>\n",
       "      <td>...</td>\n",
       "      <td>7010</td>\n",
       "      <td>6514</td>\n",
       "      <td>7376</td>\n",
       "      <td>8355</td>\n",
       "      <td>9714</td>\n",
       "      <td>9965</td>\n",
       "      <td>8303</td>\n",
       "      <td>10367</td>\n",
       "      <td>12968</td>\n",
       "      <td>15790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>아시아주</td>\n",
       "      <td>중국</td>\n",
       "      <td>395196</td>\n",
       "      <td>439699</td>\n",
       "      <td>451570</td>\n",
       "      <td>432018</td>\n",
       "      <td>476460</td>\n",
       "      <td>426849</td>\n",
       "      <td>433577</td>\n",
       "      <td>393336</td>\n",
       "      <td>...</td>\n",
       "      <td>1360</td>\n",
       "      <td>1590</td>\n",
       "      <td>1233</td>\n",
       "      <td>1243</td>\n",
       "      <td>1031</td>\n",
       "      <td>1011</td>\n",
       "      <td>1264</td>\n",
       "      <td>1366</td>\n",
       "      <td>1700</td>\n",
       "      <td>2151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>아시아주</td>\n",
       "      <td>일본</td>\n",
       "      <td>275286</td>\n",
       "      <td>267816</td>\n",
       "      <td>318985</td>\n",
       "      <td>242475</td>\n",
       "      <td>241484</td>\n",
       "      <td>251663</td>\n",
       "      <td>248793</td>\n",
       "      <td>194840</td>\n",
       "      <td>...</td>\n",
       "      <td>31</td>\n",
       "      <td>40</td>\n",
       "      <td>36</td>\n",
       "      <td>44</td>\n",
       "      <td>54</td>\n",
       "      <td>97</td>\n",
       "      <td>72</td>\n",
       "      <td>176</td>\n",
       "      <td>273</td>\n",
       "      <td>177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>아시아주</td>\n",
       "      <td>대만</td>\n",
       "      <td>116323</td>\n",
       "      <td>112231</td>\n",
       "      <td>106682</td>\n",
       "      <td>100888</td>\n",
       "      <td>126421</td>\n",
       "      <td>100595</td>\n",
       "      <td>88881</td>\n",
       "      <td>108623</td>\n",
       "      <td>...</td>\n",
       "      <td>55</td>\n",
       "      <td>47</td>\n",
       "      <td>64</td>\n",
       "      <td>33</td>\n",
       "      <td>47</td>\n",
       "      <td>64</td>\n",
       "      <td>63</td>\n",
       "      <td>69</td>\n",
       "      <td>61</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 33 columns</p>\n",
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      "text/plain": [
       "  국적별(1) 국적별(2)   Jun-19   Jul-19   Aug-19   Sep-19   Oct-19   Nov-19  \\\n",
       "0     총계     소계  1237840  1218767  1251764  1178421  1403576  1234503   \n",
       "1   아시아주     소계  1047692  1032024  1080414   983538  1185096  1066538   \n",
       "2   아시아주     중국   395196   439699   451570   432018   476460   426849   \n",
       "3   아시아주     일본   275286   267816   318985   242475   241484   251663   \n",
       "4   아시아주     대만   116323   112231   106682   100888   126421   100595   \n",
       "\n",
       "    Dec-19   Jan-20  ...  Mar-21  Apr-21  May-21  Jun-21  Jul-21  Aug-21  \\\n",
       "0  1247337  1030497  ...   11169   11492   15381   17847   20297   18452   \n",
       "1  1094768   906808  ...    7010    6514    7376    8355    9714    9965   \n",
       "2   433577   393336  ...    1360    1590    1233    1243    1031    1011   \n",
       "3   248793   194840  ...      31      40      36      44      54      97   \n",
       "4    88881   108623  ...      55      47      64      33      47      64   \n",
       "\n",
       "   Sep-21  Oct-21  Nov-21  Dec-21  \n",
       "0   19611   25698   28734   25630  \n",
       "1    8303   10367   12968   15790  \n",
       "2    1264    1366    1700    2151  \n",
       "3      72     176     273     177  \n",
       "4      63      69      61      58  \n",
       "\n",
       "[5 rows x 33 columns]"
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     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "dataset = pd.read_csv('foreign_to_korea.csv', encoding='cp949')\n",
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "cf082444",
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Jun-19</th>\n",
       "      <th>Jul-19</th>\n",
       "      <th>Aug-19</th>\n",
       "      <th>Sep-19</th>\n",
       "      <th>Oct-19</th>\n",
       "      <th>Nov-19</th>\n",
       "      <th>Dec-19</th>\n",
       "      <th>Jan-20</th>\n",
       "      <th>Feb-20</th>\n",
       "      <th>Mar-20</th>\n",
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       "      <th>Mar-21</th>\n",
       "      <th>Apr-21</th>\n",
       "      <th>May-21</th>\n",
       "      <th>Jun-21</th>\n",
       "      <th>Jul-21</th>\n",
       "      <th>Aug-21</th>\n",
       "      <th>Sep-21</th>\n",
       "      <th>Oct-21</th>\n",
       "      <th>Nov-21</th>\n",
       "      <th>Dec-21</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1237840</td>\n",
       "      <td>1218767</td>\n",
       "      <td>1251764</td>\n",
       "      <td>1178421</td>\n",
       "      <td>1403576</td>\n",
       "      <td>1234503</td>\n",
       "      <td>1247337</td>\n",
       "      <td>1030497</td>\n",
       "      <td>503976</td>\n",
       "      <td>31497</td>\n",
       "      <td>...</td>\n",
       "      <td>11169</td>\n",
       "      <td>11492</td>\n",
       "      <td>15381</td>\n",
       "      <td>17847</td>\n",
       "      <td>20297</td>\n",
       "      <td>18452</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1047692</td>\n",
       "      <td>1032024</td>\n",
       "      <td>1080414</td>\n",
       "      <td>983538</td>\n",
       "      <td>1185096</td>\n",
       "      <td>1066538</td>\n",
       "      <td>1094768</td>\n",
       "      <td>906808</td>\n",
       "      <td>423296</td>\n",
       "      <td>19719</td>\n",
       "      <td>...</td>\n",
       "      <td>7010</td>\n",
       "      <td>6514</td>\n",
       "      <td>7376</td>\n",
       "      <td>8355</td>\n",
       "      <td>9714</td>\n",
       "      <td>9965</td>\n",
       "      <td>8303</td>\n",
       "      <td>10367</td>\n",
       "      <td>12968</td>\n",
       "      <td>15790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>395196</td>\n",
       "      <td>439699</td>\n",
       "      <td>451570</td>\n",
       "      <td>432018</td>\n",
       "      <td>476460</td>\n",
       "      <td>426849</td>\n",
       "      <td>433577</td>\n",
       "      <td>393336</td>\n",
       "      <td>49520</td>\n",
       "      <td>5040</td>\n",
       "      <td>...</td>\n",
       "      <td>1360</td>\n",
       "      <td>1590</td>\n",
       "      <td>1233</td>\n",
       "      <td>1243</td>\n",
       "      <td>1031</td>\n",
       "      <td>1011</td>\n",
       "      <td>1264</td>\n",
       "      <td>1366</td>\n",
       "      <td>1700</td>\n",
       "      <td>2151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>275286</td>\n",
       "      <td>267816</td>\n",
       "      <td>318985</td>\n",
       "      <td>242475</td>\n",
       "      <td>241484</td>\n",
       "      <td>251663</td>\n",
       "      <td>248793</td>\n",
       "      <td>194840</td>\n",
       "      <td>205007</td>\n",
       "      <td>6478</td>\n",
       "      <td>...</td>\n",
       "      <td>31</td>\n",
       "      <td>40</td>\n",
       "      <td>36</td>\n",
       "      <td>44</td>\n",
       "      <td>54</td>\n",
       "      <td>97</td>\n",
       "      <td>72</td>\n",
       "      <td>176</td>\n",
       "      <td>273</td>\n",
       "      <td>177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>116323</td>\n",
       "      <td>112231</td>\n",
       "      <td>106682</td>\n",
       "      <td>100888</td>\n",
       "      <td>126421</td>\n",
       "      <td>100595</td>\n",
       "      <td>88881</td>\n",
       "      <td>108623</td>\n",
       "      <td>51881</td>\n",
       "      <td>274</td>\n",
       "      <td>...</td>\n",
       "      <td>55</td>\n",
       "      <td>47</td>\n",
       "      <td>64</td>\n",
       "      <td>33</td>\n",
       "      <td>47</td>\n",
       "      <td>64</td>\n",
       "      <td>63</td>\n",
       "      <td>69</td>\n",
       "      <td>61</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
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      "text/plain": [
       "    Jun-19   Jul-19   Aug-19   Sep-19   Oct-19   Nov-19   Dec-19   Jan-20  \\\n",
       "0  1237840  1218767  1251764  1178421  1403576  1234503  1247337  1030497   \n",
       "1  1047692  1032024  1080414   983538  1185096  1066538  1094768   906808   \n",
       "2   395196   439699   451570   432018   476460   426849   433577   393336   \n",
       "3   275286   267816   318985   242475   241484   251663   248793   194840   \n",
       "4   116323   112231   106682   100888   126421   100595    88881   108623   \n",
       "\n",
       "   Feb-20  Mar-20  ...  Mar-21  Apr-21  May-21  Jun-21  Jul-21  Aug-21  \\\n",
       "0  503976   31497  ...   11169   11492   15381   17847   20297   18452   \n",
       "1  423296   19719  ...    7010    6514    7376    8355    9714    9965   \n",
       "2   49520    5040  ...    1360    1590    1233    1243    1031    1011   \n",
       "3  205007    6478  ...      31      40      36      44      54      97   \n",
       "4   51881     274  ...      55      47      64      33      47      64   \n",
       "\n",
       "   Sep-21  Oct-21  Nov-21  Dec-21  \n",
       "0   19611   25698   28734   25630  \n",
       "1    8303   10367   12968   15790  \n",
       "2    1264    1366    1700    2151  \n",
       "3      72     176     273     177  \n",
       "4      63      69      61      58  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = dataset.drop(['국적별(1)','국적별(2)'], axis=1)\n",
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c3c229ed",
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   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.arange(31)\n",
    "a = dataset.columns\n",
    "a = list(a)\n",
    "b = dataset.iloc[0,:]\n",
    "b = list(b)\n",
    "plt.figure(figsize=(15,6))\n",
    "plt.bar(x, b)\n",
    "plt.xticks(x, a, rotation=45)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e87ddfc6",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>58</th>\n",
       "      <th>59</th>\n",
       "      <th>60</th>\n",
       "      <th>61</th>\n",
       "      <th>62</th>\n",
       "      <th>63</th>\n",
       "      <th>64</th>\n",
       "      <th>65</th>\n",
       "      <th>66</th>\n",
       "      <th>67</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Jun-19</th>\n",
       "      <td>1237840</td>\n",
       "      <td>1047692</td>\n",
       "      <td>395196</td>\n",
       "      <td>275286</td>\n",
       "      <td>116323</td>\n",
       "      <td>60526</td>\n",
       "      <td>3556</td>\n",
       "      <td>22430</td>\n",
       "      <td>19928</td>\n",
       "      <td>30596</td>\n",
       "      <td>...</td>\n",
       "      <td>11804</td>\n",
       "      <td>2949</td>\n",
       "      <td>503</td>\n",
       "      <td>2511</td>\n",
       "      <td>689</td>\n",
       "      <td>1822</td>\n",
       "      <td>33</td>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jul-19</th>\n",
       "      <td>1218767</td>\n",
       "      <td>1032024</td>\n",
       "      <td>439699</td>\n",
       "      <td>267816</td>\n",
       "      <td>112231</td>\n",
       "      <td>57868</td>\n",
       "      <td>5207</td>\n",
       "      <td>13795</td>\n",
       "      <td>11279</td>\n",
       "      <td>30132</td>\n",
       "      <td>...</td>\n",
       "      <td>10403</td>\n",
       "      <td>2176</td>\n",
       "      <td>464</td>\n",
       "      <td>3011</td>\n",
       "      <td>647</td>\n",
       "      <td>2364</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aug-19</th>\n",
       "      <td>1251764</td>\n",
       "      <td>1080414</td>\n",
       "      <td>451570</td>\n",
       "      <td>318985</td>\n",
       "      <td>106682</td>\n",
       "      <td>64438</td>\n",
       "      <td>6258</td>\n",
       "      <td>13342</td>\n",
       "      <td>8337</td>\n",
       "      <td>24857</td>\n",
       "      <td>...</td>\n",
       "      <td>12019</td>\n",
       "      <td>2436</td>\n",
       "      <td>519</td>\n",
       "      <td>2715</td>\n",
       "      <td>634</td>\n",
       "      <td>2081</td>\n",
       "      <td>36</td>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sep-19</th>\n",
       "      <td>1178421</td>\n",
       "      <td>983538</td>\n",
       "      <td>432018</td>\n",
       "      <td>242475</td>\n",
       "      <td>100888</td>\n",
       "      <td>44708</td>\n",
       "      <td>2687</td>\n",
       "      <td>18539</td>\n",
       "      <td>11858</td>\n",
       "      <td>28877</td>\n",
       "      <td>...</td>\n",
       "      <td>15818</td>\n",
       "      <td>3393</td>\n",
       "      <td>413</td>\n",
       "      <td>2992</td>\n",
       "      <td>743</td>\n",
       "      <td>2249</td>\n",
       "      <td>33</td>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oct-19</th>\n",
       "      <td>1403576</td>\n",
       "      <td>1185096</td>\n",
       "      <td>476460</td>\n",
       "      <td>241484</td>\n",
       "      <td>126421</td>\n",
       "      <td>64439</td>\n",
       "      <td>4484</td>\n",
       "      <td>37353</td>\n",
       "      <td>20527</td>\n",
       "      <td>58290</td>\n",
       "      <td>...</td>\n",
       "      <td>14247</td>\n",
       "      <td>3111</td>\n",
       "      <td>234</td>\n",
       "      <td>2933</td>\n",
       "      <td>771</td>\n",
       "      <td>2162</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 68 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             0        1       2       3       4      5     6      7      8   \\\n",
       "Jun-19  1237840  1047692  395196  275286  116323  60526  3556  22430  19928   \n",
       "Jul-19  1218767  1032024  439699  267816  112231  57868  5207  13795  11279   \n",
       "Aug-19  1251764  1080414  451570  318985  106682  64438  6258  13342   8337   \n",
       "Sep-19  1178421   983538  432018  242475  100888  44708  2687  18539  11858   \n",
       "Oct-19  1403576  1185096  476460  241484  126421  64439  4484  37353  20527   \n",
       "\n",
       "           9   ...     58    59   60    61   62    63  64  65  66  67  \n",
       "Jun-19  30596  ...  11804  2949  503  2511  689  1822  33  33   0   0  \n",
       "Jul-19  30132  ...  10403  2176  464  3011  647  2364  40  40   0   0  \n",
       "Aug-19  24857  ...  12019  2436  519  2715  634  2081  36  36   0   0  \n",
       "Sep-19  28877  ...  15818  3393  413  2992  743  2249  33  33   0   0  \n",
       "Oct-19  58290  ...  14247  3111  234  2933  771  2162  25  25   0   0  \n",
       "\n",
       "[5 rows x 68 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = dataset.T\n",
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "65181284",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array(['Jun-19', 'Jul-19', 'Aug-19', 'Sep-19', 'Oct-19', 'Nov-19',\n",
       "        'Dec-19', 'Jan-20', 'Feb-20', 'Mar-20', 'Apr-20', 'May-20',\n",
       "        'Jun-20', 'Jul-20', 'Aug-20', 'Sep-20', 'Oct-20', 'Nov-20',\n",
       "        'Dec-20', 'Jan-21', 'Feb-21', 'Mar-21', 'Apr-21', 'May-21',\n",
       "        'Jun-21', 'Jul-21', 'Aug-21', 'Sep-21', 'Oct-21', 'Nov-21',\n",
       "        'Dec-21'], dtype=object),\n",
       " array([1237840, 1218767, 1251764, 1178421, 1403576, 1234503, 1247337,\n",
       "        1030497,  503976,   31497,    5135,    6111,    9744,   12732,\n",
       "           8634,   10619,   11119,   11514,   11893,    9035,    8500,\n",
       "          11169,   11492,   15381,   17847,   20297,   18452,   19611,\n",
       "          25698,   28734,   25630], dtype=int64))"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = dataset.index.values\n",
    "y = dataset.iloc[:,0].values\n",
    "X, y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "47ae2163",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10, 7))\n",
    "ax.scatter(x=X, y=y)\n",
    "plt.xticks(rotation=45)\n",
    "plt.grid()\n",
    "plt.plot(X,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f924dce9",
   "metadata": {},
   "source": [
    "## 해외에서 우리나라 입국 통계(2021~2021.06)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7449f516",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>국적별(1)</th>\n",
       "      <th>국적별(2)</th>\n",
       "      <th>2022.01</th>\n",
       "      <th>2022.02</th>\n",
       "      <th>2022.03</th>\n",
       "      <th>2022.04</th>\n",
       "      <th>2022.05</th>\n",
       "      <th>2022.06</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>국적별(1)</td>\n",
       "      <td>국적별(2)</td>\n",
       "      <td>관광</td>\n",
       "      <td>관광</td>\n",
       "      <td>관광</td>\n",
       "      <td>관광</td>\n",
       "      <td>관광</td>\n",
       "      <td>관광</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>총계</td>\n",
       "      <td>소계</td>\n",
       "      <td>16961</td>\n",
       "      <td>16512</td>\n",
       "      <td>22492</td>\n",
       "      <td>57448</td>\n",
       "      <td>89179</td>\n",
       "      <td>133883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>아시아주</td>\n",
       "      <td>소계</td>\n",
       "      <td>10529</td>\n",
       "      <td>8841</td>\n",
       "      <td>12170</td>\n",
       "      <td>18923</td>\n",
       "      <td>32742</td>\n",
       "      <td>60845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>아시아주</td>\n",
       "      <td>중국</td>\n",
       "      <td>1641</td>\n",
       "      <td>1194</td>\n",
       "      <td>1480</td>\n",
       "      <td>1857</td>\n",
       "      <td>1703</td>\n",
       "      <td>3234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>아시아주</td>\n",
       "      <td>일본</td>\n",
       "      <td>103</td>\n",
       "      <td>91</td>\n",
       "      <td>112</td>\n",
       "      <td>535</td>\n",
       "      <td>668</td>\n",
       "      <td>2042</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   국적별(1)  국적별(2) 2022.01 2022.02 2022.03 2022.04 2022.05 2022.06\n",
       "0  국적별(1)  국적별(2)      관광      관광      관광      관광      관광      관광\n",
       "1      총계      소계   16961   16512   22492   57448   89179  133883\n",
       "2    아시아주      소계   10529    8841   12170   18923   32742   60845\n",
       "3    아시아주      중국    1641    1194    1480    1857    1703    3234\n",
       "4    아시아주      일본     103      91     112     535     668    2042"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_post = pd.read_csv('foreign_to_korea1.csv', encoding='cp949')\n",
    "dataset_post.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "227e2fbc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2022.01</th>\n",
       "      <th>2022.02</th>\n",
       "      <th>2022.03</th>\n",
       "      <th>2022.04</th>\n",
       "      <th>2022.05</th>\n",
       "      <th>2022.06</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>16961</td>\n",
       "      <td>16512</td>\n",
       "      <td>22492</td>\n",
       "      <td>57448</td>\n",
       "      <td>89179</td>\n",
       "      <td>133883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10529</td>\n",
       "      <td>8841</td>\n",
       "      <td>12170</td>\n",
       "      <td>18923</td>\n",
       "      <td>32742</td>\n",
       "      <td>60845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1641</td>\n",
       "      <td>1194</td>\n",
       "      <td>1480</td>\n",
       "      <td>1857</td>\n",
       "      <td>1703</td>\n",
       "      <td>3234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>103</td>\n",
       "      <td>91</td>\n",
       "      <td>112</td>\n",
       "      <td>535</td>\n",
       "      <td>668</td>\n",
       "      <td>2042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>64</td>\n",
       "      <td>50</td>\n",
       "      <td>49</td>\n",
       "      <td>122</td>\n",
       "      <td>178</td>\n",
       "      <td>377</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  2022.01 2022.02 2022.03 2022.04 2022.05 2022.06\n",
       "1   16961   16512   22492   57448   89179  133883\n",
       "2   10529    8841   12170   18923   32742   60845\n",
       "3    1641    1194    1480    1857    1703    3234\n",
       "4     103      91     112     535     668    2042\n",
       "5      64      50      49     122     178     377"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_post = dataset_post.drop(['국적별(1)','국적별(2)'], axis=1)\n",
    "dataset_post = dataset_post.drop(0, axis=0)\n",
    "dataset_post.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "732977b5",
   "metadata": {},
   "source": [
    "## 우리나라 여행 수지 데이터(2019.01 ~ 2022.07)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "887a2f7e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2019.01</th>\n",
       "      <th>2019.02</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>...</th>\n",
       "      <th>2021.10</th>\n",
       "      <th>2021.11</th>\n",
       "      <th>2021.12</th>\n",
       "      <th>2022.01</th>\n",
       "      <th>2022.02</th>\n",
       "      <th>2022.03</th>\n",
       "      <th>2022.04</th>\n",
       "      <th>2022.05</th>\n",
       "      <th>2022.06</th>\n",
       "      <th>2022.07</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1518.7</td>\n",
       "      <td>1643.9</td>\n",
       "      <td>1894.9</td>\n",
       "      <td>1821.9</td>\n",
       "      <td>1829.6</td>\n",
       "      <td>1684.1</td>\n",
       "      <td>1783.7</td>\n",
       "      <td>1710.8</td>\n",
       "      <td>1618.2</td>\n",
       "      <td>1706.4</td>\n",
       "      <td>...</td>\n",
       "      <td>913.8</td>\n",
       "      <td>963.6</td>\n",
       "      <td>864.3</td>\n",
       "      <td>819.1</td>\n",
       "      <td>907.7</td>\n",
       "      <td>844.2</td>\n",
       "      <td>819.7</td>\n",
       "      <td>938.9</td>\n",
       "      <td>1106.6</td>\n",
       "      <td>933.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2702.2</td>\n",
       "      <td>2631.5</td>\n",
       "      <td>2610.4</td>\n",
       "      <td>2738.2</td>\n",
       "      <td>2792.2</td>\n",
       "      <td>2960.6</td>\n",
       "      <td>2854.9</td>\n",
       "      <td>2653.5</td>\n",
       "      <td>2645.1</td>\n",
       "      <td>2645.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1409.9</td>\n",
       "      <td>1438.7</td>\n",
       "      <td>1344.4</td>\n",
       "      <td>1181.3</td>\n",
       "      <td>1325.2</td>\n",
       "      <td>1598.8</td>\n",
       "      <td>1620.1</td>\n",
       "      <td>1679.1</td>\n",
       "      <td>1756.0</td>\n",
       "      <td>1748.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 43 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   2019.01  2019.02  2019.03  2019.04  2019.05  2019.06  2019.07  2019.08  \\\n",
       "0   1518.7   1643.9   1894.9   1821.9   1829.6   1684.1   1783.7   1710.8   \n",
       "1   2702.2   2631.5   2610.4   2738.2   2792.2   2960.6   2854.9   2653.5   \n",
       "\n",
       "   2019.09  2019.10  ...  2021.10  2021.11  2021.12  2022.01  2022.02  \\\n",
       "0   1618.2   1706.4  ...    913.8    963.6    864.3    819.1    907.7   \n",
       "1   2645.1   2645.0  ...   1409.9   1438.7   1344.4   1181.3   1325.2   \n",
       "\n",
       "   2022.03  2022.04  2022.05  2022.06  2022.07  \n",
       "0    844.2    819.7    938.9   1106.6    933.3  \n",
       "1   1598.8   1620.1   1679.1   1756.0   1748.6  \n",
       "\n",
       "[2 rows x 43 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_tour = pd.read_csv('tour_data.csv', encoding='cp949')\n",
    "dataset_tour = dataset_tour.drop('계정코드별', axis=1)\n",
    "dataset_tour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "fda0b611",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((43,), (43,))"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_export = dataset_tour.iloc[0,:].values\n",
    "X = dataset_tour.columns.values\n",
    "X.shape, y_export.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "023b58c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10, 7))\n",
    "ax.scatter(x=X, y=y_export)\n",
    "plt.xticks(rotation=45)\n",
    "plt.grid()\n",
    "plt.plot(X,y_export)\n",
    "plt.title('tour_revenue')\n",
    "plt.xlabel('year')\n",
    "plt.ylabel('million', rotation=0, loc='top')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a29fa806",
   "metadata": {},
   "source": [
    "## 신종플루 세계 데이터(2009~2010)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d6919f16",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Country</th>\n",
       "      <th>Cumulative no. of cases</th>\n",
       "      <th>Cumulative no. of deaths</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2486</th>\n",
       "      <td>2009-07-06</td>\n",
       "      <td>Viet Nam</td>\n",
       "      <td>181.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2487</th>\n",
       "      <td>2009-07-06</td>\n",
       "      <td>Virgin Islands</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2488</th>\n",
       "      <td>NaN</td>\n",
       "      <td>West Bank and Gaza Strip</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2489</th>\n",
       "      <td>2009-07-06</td>\n",
       "      <td>Yemen</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2490</th>\n",
       "      <td>2009-07-06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Date                   Country  Cumulative no. of cases  \\\n",
       "2486  2009-07-06                  Viet Nam                    181.0   \n",
       "2487  2009-07-06            Virgin Islands                      1.0   \n",
       "2488         NaN  West Bank and Gaza Strip                     60.0   \n",
       "2489  2009-07-06                     Yemen                      8.0   \n",
       "2490  2009-07-06                       NaN                      NaN   \n",
       "\n",
       "      Cumulative no. of deaths  \n",
       "2486                       0.0  \n",
       "2487                       0.0  \n",
       "2488                       0.0  \n",
       "2489                       0.0  \n",
       "2490                       NaN  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_h1n1 = pd.read_csv('H1N1_data.csv')\n",
    "dataset_h1n1 = dataset_h1n1.drop('Link', axis=1)\n",
    "dataset_h1n1.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "876d607f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 신종플루 한국 누적 확진자\n",
    "df_kh = dataset_h1n1[dataset_h1n1['Country'] == 'Republic of Korea']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "85ce93ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X = df_kh.iloc[:,0].values\n",
    "y = df_kh.iloc[:,2].values\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10, 7))\n",
    "ax.scatter(x=X, y=y)\n",
    "plt.xticks(rotation=45)\n",
    "plt.grid()\n",
    "plt.plot(X,y)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "d2b03340",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>WHOREGION</th>\n",
       "      <th>ITZ</th>\n",
       "      <th>COUNTRY/AREA/TERRITORY</th>\n",
       "      <th>COUNTRY_CODE</th>\n",
       "      <th>ORIGIN_SOURCE</th>\n",
       "      <th>ISO_YEAR</th>\n",
       "      <th>ISO_WEEK</th>\n",
       "      <th>ISO_SDATE</th>\n",
       "      <th>AH1N12009</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AMR</td>\n",
       "      <td>FLU_CNT_AMC</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>MEX</td>\n",
       "      <td>NOTDEFINED</td>\n",
       "      <td>2009</td>\n",
       "      <td>6</td>\n",
       "      <td>2009-02-01T15:00:00.000Z</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AMR</td>\n",
       "      <td>FLU_CNT_AMC</td>\n",
       "      <td>Panama</td>\n",
       "      <td>PAN</td>\n",
       "      <td>NOTDEFINED</td>\n",
       "      <td>2009</td>\n",
       "      <td>6</td>\n",
       "      <td>2009-02-01T15:00:00.000Z</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AMR</td>\n",
       "      <td>FLU_TEMP_SAMR</td>\n",
       "      <td>Paraguay</td>\n",
       "      <td>PRY</td>\n",
       "      <td>NOTDEFINED</td>\n",
       "      <td>2009</td>\n",
       "      <td>6</td>\n",
       "      <td>2009-02-01T15:00:00.000Z</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AMR</td>\n",
       "      <td>FLU_TRP_SAMR</td>\n",
       "      <td>Peru</td>\n",
       "      <td>PER</td>\n",
       "      <td>NOTDEFINED</td>\n",
       "      <td>2009</td>\n",
       "      <td>6</td>\n",
       "      <td>2009-02-01T15:00:00.000Z</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AMR</td>\n",
       "      <td>FLU_CNT_AMC</td>\n",
       "      <td>Saint Barthélemy</td>\n",
       "      <td>BLM</td>\n",
       "      <td>NOTDEFINED</td>\n",
       "      <td>2009</td>\n",
       "      <td>6</td>\n",
       "      <td>2009-02-01T15:00:00.000Z</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  WHOREGION            ITZ COUNTRY/AREA/TERRITORY COUNTRY_CODE ORIGIN_SOURCE  \\\n",
       "0       AMR    FLU_CNT_AMC                 Mexico          MEX    NOTDEFINED   \n",
       "1       AMR    FLU_CNT_AMC                 Panama          PAN    NOTDEFINED   \n",
       "2       AMR  FLU_TEMP_SAMR               Paraguay          PRY    NOTDEFINED   \n",
       "3       AMR   FLU_TRP_SAMR                   Peru          PER    NOTDEFINED   \n",
       "4       AMR    FLU_CNT_AMC       Saint Barthélemy          BLM    NOTDEFINED   \n",
       "\n",
       "   ISO_YEAR  ISO_WEEK                 ISO_SDATE  AH1N12009  \n",
       "0      2009         6  2009-02-01T15:00:00.000Z        1.0  \n",
       "1      2009         6  2009-02-01T15:00:00.000Z        NaN  \n",
       "2      2009         6  2009-02-01T15:00:00.000Z        0.0  \n",
       "3      2009         6  2009-02-01T15:00:00.000Z        NaN  \n",
       "4      2009         6  2009-02-01T15:00:00.000Z        NaN  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt_h1n1 = pd.read_csv('h1n1_table.csv')\n",
    "dt_h1n1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "8f3f46da",
   "metadata": {},
   "outputs": [],
   "source": [
    "dt_h1n1 = dt_h1n1.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "8c432dde",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>WHOREGION</th>\n",
       "      <th>ITZ</th>\n",
       "      <th>COUNTRY/AREA/TERRITORY</th>\n",
       "      <th>COUNTRY_CODE</th>\n",
       "      <th>ORIGIN_SOURCE</th>\n",
       "      <th>ISO_YEAR</th>\n",
       "      <th>ISO_WEEK</th>\n",
       "      <th>ISO_SDATE</th>\n",
       "      <th>AH1N12009</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7953</th>\n",
       "      <td>WPR</td>\n",
       "      <td>FLU_EST_ASIA</td>\n",
       "      <td>China, Hong Kong SAR</td>\n",
       "      <td>HKG</td>\n",
       "      <td>NOTDEFINED</td>\n",
       "      <td>2010</td>\n",
       "      <td>41</td>\n",
       "      <td>2010-10-10T15:00:00.000Z</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7955</th>\n",
       "      <td>WPR</td>\n",
       "      <td>FLU_EST_ASIA</td>\n",
       "      <td>Japan</td>\n",
       "      <td>JPN</td>\n",
       "      <td>NOTDEFINED</td>\n",
       "      <td>2010</td>\n",
       "      <td>41</td>\n",
       "      <td>2010-10-10T15:00:00.000Z</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7956</th>\n",
       "      <td>WPR</td>\n",
       "      <td>FLU_SE_ASIA</td>\n",
       "      <td>Malaysia</td>\n",
       "      <td>MYS</td>\n",
       "      <td>NOTDEFINED</td>\n",
       "      <td>2010</td>\n",
       "      <td>41</td>\n",
       "      <td>2010-10-10T15:00:00.000Z</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7961</th>\n",
       "      <td>WPR</td>\n",
       "      <td>FLU_SE_ASIA</td>\n",
       "      <td>Singapore</td>\n",
       "      <td>SGP</td>\n",
       "      <td>NOTDEFINED</td>\n",
       "      <td>2010</td>\n",
       "      <td>41</td>\n",
       "      <td>2010-10-10T15:00:00.000Z</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7962</th>\n",
       "      <td>WPR</td>\n",
       "      <td>FLU_SE_ASIA</td>\n",
       "      <td>Viet Nam</td>\n",
       "      <td>VNM</td>\n",
       "      <td>NOTDEFINED</td>\n",
       "      <td>2010</td>\n",
       "      <td>41</td>\n",
       "      <td>2010-10-10T15:00:00.000Z</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     WHOREGION           ITZ COUNTRY/AREA/TERRITORY COUNTRY_CODE  \\\n",
       "7953       WPR  FLU_EST_ASIA   China, Hong Kong SAR          HKG   \n",
       "7955       WPR  FLU_EST_ASIA                  Japan          JPN   \n",
       "7956       WPR   FLU_SE_ASIA               Malaysia          MYS   \n",
       "7961       WPR   FLU_SE_ASIA              Singapore          SGP   \n",
       "7962       WPR   FLU_SE_ASIA               Viet Nam          VNM   \n",
       "\n",
       "     ORIGIN_SOURCE  ISO_YEAR  ISO_WEEK                 ISO_SDATE  AH1N12009  \n",
       "7953    NOTDEFINED      2010        41  2010-10-10T15:00:00.000Z        7.0  \n",
       "7955    NOTDEFINED      2010        41  2010-10-10T15:00:00.000Z        9.0  \n",
       "7956    NOTDEFINED      2010        41  2010-10-10T15:00:00.000Z        1.0  \n",
       "7961    NOTDEFINED      2010        41  2010-10-10T15:00:00.000Z       15.0  \n",
       "7962    NOTDEFINED      2010        41  2010-10-10T15:00:00.000Z        2.0  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "con = dt_h1n1[dt_h1n1['AH1N12009'] == 0].index\n",
    "dt_h1n1 = dt_h1n1.drop(con)\n",
    "dt_h1n1.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "fb2c3866",
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'Series' object has no attribute 'feature_names'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "Input \u001b[1;32mIn [18]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mdt_h1n1\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mCOUNTRY/AREA/TERRITORY\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfeature_names\u001b[49m\n",
      "File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\generic.py:5575\u001b[0m, in \u001b[0;36mNDFrame.__getattr__\u001b[1;34m(self, name)\u001b[0m\n\u001b[0;32m   5568\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (\n\u001b[0;32m   5569\u001b[0m     name \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_internal_names_set\n\u001b[0;32m   5570\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m name \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_metadata\n\u001b[0;32m   5571\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m name \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_accessors\n\u001b[0;32m   5572\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_info_axis\u001b[38;5;241m.\u001b[39m_can_hold_identifiers_and_holds_name(name)\n\u001b[0;32m   5573\u001b[0m ):\n\u001b[0;32m   5574\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m[name]\n\u001b[1;32m-> 5575\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mobject\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;21;43m__getattribute__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mname\u001b[49m\u001b[43m)\u001b[49m\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'Series' object has no attribute 'feature_names'"
     ]
    }
   ],
   "source": [
    "dt_h1n1['COUNTRY/AREA/TERRITORY'].feature_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cdeaa846",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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