dataset-detailed-description.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {},
   "source": [
    "# 1. The Lists of Data Table\n",
    "### 1) Case Data\n",
    "- **Case**: Data of COVID-19 infection cases in South Korea\n",
    "\n",
    "### 2) Patient Data\n",
    "- **PatientInfo**: Epidemiological data of COVID-19 patients in South Korea\n",
    "- **PatientRoute**: Route data of COVID-19 patients in South Korea (currently unavailable)\n",
    "\n",
    "### 3) Time Series Data\n",
    "- **Time**: Time series data of COVID-19 status in South Korea\n",
    "- **TimeAge**: Time series data of COVID-19 status in terms of the age in South Korea\n",
    "- **TimeGender**: Time series data of COVID-19 status in terms of gender in South Korea\n",
    "- **TimeProvince**: Time series data of COVID-19 status in terms of the Province in South Korea\n",
    "\n",
    "### 4) Additional Data\n",
    "- **Region**: Location and statistical data of the regions in South Korea\n",
    "- **Weather**: Data of the weather in the regions of South Korea\n",
    "- **SearchTrend**: Trend data of the keywords searched in NAVER which is one of the largest portals in South Korea\n",
    "- **SeoulFloating**: Data of floating population in Seoul, South Korea (from SK Telecom Big Data Hub)\n",
    "- **Policy**: Data of the government policy for COVID-19 in South Korea"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {},
   "source": [
    "# 2. The Structure of our Dataset\n",
    "- What color means is that they have similar properties.\n",
    "- If a line is connected between columns, it means that the values of the columns are partially shared.\n",
    "- The dotted lines mean weak relevance.\n",
    "![db_0701](https://user-images.githubusercontent.com/50820635/86225695-8dca0580-bbc5-11ea-9e9b-b0ca33414d8a.PNG)"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {},
   "source": [
    "# 3. The Detailed Description of each Data Table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/kaggle/input/coronavirusdataset/TimeAge.csv\n",
      "/kaggle/input/coronavirusdataset/Region.csv\n",
      "/kaggle/input/coronavirusdataset/Time.csv\n",
      "/kaggle/input/coronavirusdataset/Weather.csv\n",
      "/kaggle/input/coronavirusdataset/SearchTrend.csv\n",
      "/kaggle/input/coronavirusdataset/TimeProvince.csv\n",
      "/kaggle/input/coronavirusdataset/TimeGender.csv\n",
      "/kaggle/input/coronavirusdataset/PatientInfo.csv\n",
      "/kaggle/input/coronavirusdataset/Policy.csv\n",
      "/kaggle/input/coronavirusdataset/SeoulFloating.csv\n",
      "/kaggle/input/coronavirusdataset/Case.csv\n"
     ]
    }
   ],
   "source": [
    "# This Python 3 environment comes with many helpful analytics libraries installed\n",
    "# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n",
    "# For example, here's several helpful packages to load in \n",
    "\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "\n",
    "# Input data files are available in the \"../input/\" directory.\n",
    "# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
    "\n",
    "import os\n",
    "for dirname, _, filenames in os.walk('/kaggle/input'):\n",
    "    for filename in filenames:\n",
    "        print(os.path.join(dirname, filename))\n",
    "\n",
    "# Any results you write to the current directory are saved as output."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "path = '/kaggle/input/coronavirusdataset/'\n",
    "\n",
    "case = p_info = pd.read_csv(path+'Case.csv')\n",
    "p_info = pd.read_csv(path+'PatientInfo.csv')\n",
    "#p_route = pd.read_csv(path+'PatientRoute.csv')\n",
    "time = pd.read_csv(path+'Time.csv')\n",
    "t_age = pd.read_csv(path+'TimeAge.csv')\n",
    "t_gender = pd.read_csv(path+'TimeGender.csv')\n",
    "t_provin = pd.read_csv(path+'TimeProvince.csv')\n",
    "region = pd.read_csv(path+'Region.csv')\n",
    "weather = pd.read_csv(path+'Weather.csv')\n",
    "search = pd.read_csv(path+'SearchTrend.csv')\n",
    "floating = pd.read_csv(path+'SeoulFloating.csv')\n",
    "policy = pd.read_csv(path+'Policy.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {},
   "source": [
    "##### Before the Start..\n",
    "- We make a structured dataset based on the report materials of KCDC and local governments.\n",
    "- In Korea, we use the terms named '-do', '-si', '-gun' and '-gu',\n",
    "- The meaning of them are explained below.\n",
    "\n",
    "***\n",
    "\n",
    "\n",
    "### Levels of administrative divisions in South Korea\n",
    "#### Upper Level (Provincial-level divisions)\n",
    "- **Special City**:\n",
    "*Seoul*\n",
    "- **Metropolitan City**:\n",
    "*Busan / Daegu / Daejeon / Gwangju / Incheon / Ulsan*\n",
    "- **Province(-do)**:\n",
    "*Gyeonggi-do / Gangwon-do / Chungcheongbuk-do / Chungcheongnam-do / Jeollabuk-do / Jeollanam-do / Gyeongsangbuk-do / Gyeongsangnam-do*\n",
    "\n",
    "#### Lower Level (Municipal-level divisions)\n",
    "- **City(-si)**\n",
    "[List of cities in South Korea](https://en.wikipedia.org/wiki/List_of_cities_in_South_Korea)\n",
    "- **Country(-gun)**\n",
    "[List of counties of South Korea](https://en.wikipedia.org/wiki/List_of_counties_of_South_Korea)\n",
    "- **District(-gu)**\n",
    "[List of districts in South Korea](https://en.wikipedia.org/wiki/List_of_districts_in_South_Korea)\n",
    "\n",
    "***\n",
    "\n",
    "<img src=\"https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F2815958%2F1c50702025f44b0c1ce92460bd2ea3f9%2Fus_hi_30-1.jpg?generation=1582819435038273&amp;alt=media\">\n",
    "\n",
    "***\n",
    "\n",
    "Sources\n",
    "- http://nationalatlas.ngii.go.kr/pages/page_1266.php\n",
    "- https://en.wikipedia.org/wiki/Administrative_divisions_of_South_Korea"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {},
   "source": [
    "### 1) Case\n",
    "#### Data of COVID-19 infection cases in South Korea\n",
    "1. case_id: the ID of the infection case\n",
    "  > - case_id(7) = region_code(5) + case_number(2)  \n",
    "  > - You can check the region_code in 'Region.csv'\n",
    "- province: Special City / Metropolitan City / Province(-do)\n",
    "- city: City(-si) / Country (-gun) / District (-gu)\n",
    "  > - The value 'from other city' means that where the group infection started is other city.\n",
    "- group: TRUE: group infection / FALSE: not group\n",
    "  > - If the value is 'TRUE' in this column, the value of 'infection_cases' means the name of group.  \n",
    "  > - The values named 'contact with patient', 'overseas inflow' and 'etc' are not group infection. \n",
    "- infection_case: the infection case (the name of group or other cases)\n",
    "  > - The value 'overseas inflow' means that the infection is from other country.  \n",
    "  > - The value 'etc' includes individual cases, cases where relevance classification is ongoing after investigation, and cases under investigation.\n",
    "- confirmed: the accumulated number of the confirmed\n",
    "- latitude: the latitude of the group (WGS84)\n",
    "- longitude: the longitude of the group (WGS84)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>case_id</th>\n",
       "      <th>province</th>\n",
       "      <th>city</th>\n",
       "      <th>group</th>\n",
       "      <th>infection_case</th>\n",
       "      <th>confirmed</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000001</td>\n",
       "      <td>Seoul</td>\n",
       "      <td>Yongsan-gu</td>\n",
       "      <td>True</td>\n",
       "      <td>Itaewon Clubs</td>\n",
       "      <td>139</td>\n",
       "      <td>37.538621</td>\n",
       "      <td>126.992652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1000002</td>\n",
       "      <td>Seoul</td>\n",
       "      <td>Gwanak-gu</td>\n",
       "      <td>True</td>\n",
       "      <td>Richway</td>\n",
       "      <td>119</td>\n",
       "      <td>37.48208</td>\n",
       "      <td>126.901384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1000003</td>\n",
       "      <td>Seoul</td>\n",
       "      <td>Guro-gu</td>\n",
       "      <td>True</td>\n",
       "      <td>Guro-gu Call Center</td>\n",
       "      <td>95</td>\n",
       "      <td>37.508163</td>\n",
       "      <td>126.884387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1000004</td>\n",
       "      <td>Seoul</td>\n",
       "      <td>Yangcheon-gu</td>\n",
       "      <td>True</td>\n",
       "      <td>Yangcheon Table Tennis Club</td>\n",
       "      <td>43</td>\n",
       "      <td>37.546061</td>\n",
       "      <td>126.874209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1000005</td>\n",
       "      <td>Seoul</td>\n",
       "      <td>Dobong-gu</td>\n",
       "      <td>True</td>\n",
       "      <td>Day Care Center</td>\n",
       "      <td>43</td>\n",
       "      <td>37.679422</td>\n",
       "      <td>127.044374</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    case_id province          city  group               infection_case  \\\n",
       "0   1000001    Seoul    Yongsan-gu   True                Itaewon Clubs   \n",
       "1   1000002    Seoul     Gwanak-gu   True                      Richway   \n",
       "2   1000003    Seoul       Guro-gu   True          Guro-gu Call Center   \n",
       "3   1000004    Seoul  Yangcheon-gu   True  Yangcheon Table Tennis Club   \n",
       "4   1000005    Seoul     Dobong-gu   True              Day Care Center   \n",
       "\n",
       "   confirmed   latitude   longitude  \n",
       "0        139  37.538621  126.992652  \n",
       "1        119   37.48208  126.901384  \n",
       "2         95  37.508163  126.884387  \n",
       "3         43  37.546061  126.874209  \n",
       "4         43  37.679422  127.044374  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "case.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {},
   "source": [
    "### 2) PatientInfo\n",
    "#### Epidemiological data of COVID-19 patients in South Korea\n",
    "1. patient_id: the ID of the patient\n",
    "  > - patient_id(10) = region_code(5) + patient_number(5)\n",
    "  > - You can check the region_code in 'Region.csv'\n",
    "  > - There are two types of the patient_number  \n",
    "      1) local_num: The number given by the local government.  \n",
    "      2) global_num: The number given by the KCDC  \n",
    "- sex: the sex of the patient\n",
    "- age: the age of the patient\n",
    "  > - 0s: 0 ~ 9  \n",
    "  > - 10s: 10 ~ 19  \n",
    "  ...  \n",
    "  > - 90s: 90 ~ 99  \n",
    "  > - 100s: 100 ~ 109\n",
    "- country: the country of the patient\n",
    "- province: the province of the patient\n",
    "- city: the city of the patient\n",
    "- infection_case: the case of infection\n",
    "- infected_by: the ID of who infected the patient\n",
    "  > - This column refers to the  'patient_id' column. \n",
    "- contact_number: the number of contacts with people\n",
    "- symptom_onset_date: the date of symptom onset\n",
    "- confirmed_date: the date of being confirmed\n",
    "- released_date: the date of being released\n",
    "- deceased_date: the date of being deceased\n",
    "- state: isolated / released / deceased\n",
    "  > - isolated: being isolated in the hospital\n",
    "  > - released: being released from the hospital\n",
    "  > - deceased: being deceased"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>patient_id</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>country</th>\n",
       "      <th>province</th>\n",
       "      <th>city</th>\n",
       "      <th>infection_case</th>\n",
       "      <th>infected_by</th>\n",
       "      <th>contact_number</th>\n",
       "      <th>symptom_onset_date</th>\n",
       "      <th>confirmed_date</th>\n",
       "      <th>released_date</th>\n",
       "      <th>deceased_date</th>\n",
       "      <th>state</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000000001</td>\n",
       "      <td>male</td>\n",
       "      <td>50s</td>\n",
       "      <td>Korea</td>\n",
       "      <td>Seoul</td>\n",
       "      <td>Gangseo-gu</td>\n",
       "      <td>overseas inflow</td>\n",
       "      <td>NaN</td>\n",
       "      <td>75</td>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>2020-01-23</td>\n",
       "      <td>2020-02-05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>released</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1000000002</td>\n",
       "      <td>male</td>\n",
       "      <td>30s</td>\n",
       "      <td>Korea</td>\n",
       "      <td>Seoul</td>\n",
       "      <td>Jungnang-gu</td>\n",
       "      <td>overseas inflow</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-01-30</td>\n",
       "      <td>2020-03-02</td>\n",
       "      <td>NaN</td>\n",
       "      <td>released</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1000000003</td>\n",
       "      <td>male</td>\n",
       "      <td>50s</td>\n",
       "      <td>Korea</td>\n",
       "      <td>Seoul</td>\n",
       "      <td>Jongno-gu</td>\n",
       "      <td>contact with patient</td>\n",
       "      <td>2002000001</td>\n",
       "      <td>17</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-01-30</td>\n",
       "      <td>2020-02-19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>released</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1000000004</td>\n",
       "      <td>male</td>\n",
       "      <td>20s</td>\n",
       "      <td>Korea</td>\n",
       "      <td>Seoul</td>\n",
       "      <td>Mapo-gu</td>\n",
       "      <td>overseas inflow</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9</td>\n",
       "      <td>2020-01-26</td>\n",
       "      <td>2020-01-30</td>\n",
       "      <td>2020-02-15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>released</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1000000005</td>\n",
       "      <td>female</td>\n",
       "      <td>20s</td>\n",
       "      <td>Korea</td>\n",
       "      <td>Seoul</td>\n",
       "      <td>Seongbuk-gu</td>\n",
       "      <td>contact with patient</td>\n",
       "      <td>1000000002</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-01-31</td>\n",
       "      <td>2020-02-24</td>\n",
       "      <td>NaN</td>\n",
       "      <td>released</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   patient_id     sex  age country province         city  \\\n",
       "0  1000000001    male  50s   Korea    Seoul   Gangseo-gu   \n",
       "1  1000000002    male  30s   Korea    Seoul  Jungnang-gu   \n",
       "2  1000000003    male  50s   Korea    Seoul    Jongno-gu   \n",
       "3  1000000004    male  20s   Korea    Seoul      Mapo-gu   \n",
       "4  1000000005  female  20s   Korea    Seoul  Seongbuk-gu   \n",
       "\n",
       "         infection_case infected_by contact_number symptom_onset_date  \\\n",
       "0       overseas inflow         NaN             75         2020-01-22   \n",
       "1       overseas inflow         NaN             31                NaN   \n",
       "2  contact with patient  2002000001             17                NaN   \n",
       "3       overseas inflow         NaN              9         2020-01-26   \n",
       "4  contact with patient  1000000002              2                NaN   \n",
       "\n",
       "  confirmed_date released_date deceased_date     state  \n",
       "0     2020-01-23    2020-02-05           NaN  released  \n",
       "1     2020-01-30    2020-03-02           NaN  released  \n",
       "2     2020-01-30    2020-02-19           NaN  released  \n",
       "3     2020-01-30    2020-02-15           NaN  released  \n",
       "4     2020-01-31    2020-02-24           NaN  released  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p_info.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {},
   "source": [
    "### 3) PatientRoute\n",
    "#### Route data of COVID-19 patients in South Korea\n",
    "- patient_id: the ID of the patient\n",
    "- date: YYYY-MM-DD\n",
    "- province: Special City / Metropolitan City / Province(-do)\n",
    "- city: City(-si) / Country (-gun) / District (-gu)\n",
    "- latitude: the latitude of the visit (WGS84)\n",
    "- longitude: the longitude of the visit (WGS84)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#p_route.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {},
   "source": [
    "### 4) Time\n",
    "#### Time series data of COVID-19 status in South Korea\n",
    "- date: YYYY-MM-DD\n",
    "- time: Time (0 = AM 12:00 / 16 = PM 04:00)\n",
    "  > - The time for KCDC to open the information has been changed from PM 04:00 to AM 12:00 since March 2nd.\n",
    "- test: the accumulated number of tests\n",
    "  > - A test is a diagnosis of an infection.\n",
    "- negative: the accumulated number of negative results\n",
    "- confirmed: the accumulated number of positive results\n",
    "- released: the accumulated number of releases\n",
    "- deceased: the accumulated number of deceases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "      <th>date</th>\n",
       "      <th>time</th>\n",
       "      <th>test</th>\n",
       "      <th>negative</th>\n",
       "      <th>confirmed</th>\n",
       "      <th>released</th>\n",
       "      <th>deceased</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-01-20</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-01-21</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>16</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-01-23</td>\n",
       "      <td>16</td>\n",
       "      <td>22</td>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-01-24</td>\n",
       "      <td>16</td>\n",
       "      <td>27</td>\n",
       "      <td>25</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date  time  test  negative  confirmed  released  deceased\n",
       "0  2020-01-20    16     1         0          1         0         0\n",
       "1  2020-01-21    16     1         0          1         0         0\n",
       "2  2020-01-22    16     4         3          1         0         0\n",
       "3  2020-01-23    16    22        21          1         0         0\n",
       "4  2020-01-24    16    27        25          2         0         0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "time.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {},
   "source": [
    "### 5) TimeAge\n",
    "#### Time series data of COVID-19 status in terms of the age in South Korea\n",
    "- date: YYYY-MM-DD\n",
    "  > - The status in terms of the age has been presented since March 2nd.\n",
    "- time: Time\n",
    "- age: the age of patients\n",
    "- confirmed: the accumulated number of the confirmed\n",
    "- deceased: the accumulated number of the deceased"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>date</th>\n",
       "      <th>time</th>\n",
       "      <th>age</th>\n",
       "      <th>confirmed</th>\n",
       "      <th>deceased</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-03-02</td>\n",
       "      <td>0</td>\n",
       "      <td>0s</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-03-02</td>\n",
       "      <td>0</td>\n",
       "      <td>10s</td>\n",
       "      <td>169</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-03-02</td>\n",
       "      <td>0</td>\n",
       "      <td>20s</td>\n",
       "      <td>1235</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-03-02</td>\n",
       "      <td>0</td>\n",
       "      <td>30s</td>\n",
       "      <td>506</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-03-02</td>\n",
       "      <td>0</td>\n",
       "      <td>40s</td>\n",
       "      <td>633</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date  time  age  confirmed  deceased\n",
       "0  2020-03-02     0   0s         32         0\n",
       "1  2020-03-02     0  10s        169         0\n",
       "2  2020-03-02     0  20s       1235         0\n",
       "3  2020-03-02     0  30s        506         1\n",
       "4  2020-03-02     0  40s        633         1"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t_age.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {},
   "source": [
    "### 6) TimeGender\n",
    "#### Time series data of COVID-19 status in terms of the gender in South Korea\n",
    "- date: YYYY-MM-DD\n",
    "  > - The status in terms of the gender has been presented since March 2nd.\n",
    "- time: Time\n",
    "- sex: the gender of patients\n",
    "- confirmed: the accumulated number of the confirmed\n",
    "- deceased: the accumulated number of the deceased"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>time</th>\n",
       "      <th>sex</th>\n",
       "      <th>confirmed</th>\n",
       "      <th>deceased</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-03-02</td>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "      <td>1591</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-03-02</td>\n",
       "      <td>0</td>\n",
       "      <td>female</td>\n",
       "      <td>2621</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-03-03</td>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "      <td>1810</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-03-03</td>\n",
       "      <td>0</td>\n",
       "      <td>female</td>\n",
       "      <td>3002</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-03-04</td>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "      <td>1996</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date  time     sex  confirmed  deceased\n",
       "0  2020-03-02     0    male       1591        13\n",
       "1  2020-03-02     0  female       2621         9\n",
       "2  2020-03-03     0    male       1810        16\n",
       "3  2020-03-03     0  female       3002        12\n",
       "4  2020-03-04     0    male       1996        20"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t_gender.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {},
   "source": [
    "### 7) TimeProvince\n",
    "#### Time series data of COVID-19 status in terms of the Province in South Korea\n",
    "- date: YYYY-MM-DD\n",
    "- time: Time\n",
    "- province: the province of South Korea\n",
    "- confirmed: the accumulated number of the confirmed in the province\n",
    "  > - The confirmed status in terms of the provinces has been presented since Feburary 21th.\n",
    "  > - The value before Feburary 21th can be different.\n",
    "- released: the accumulated number of the released in the province\n",
    "  > - The confirmed status in terms of the provinces has been presented since March 5th.\n",
    "  > - The value before March 5th can be different.\n",
    "- deceased: the accumulated number of the deceased in the province\n",
    "  > - The confirmed status in terms of the provinces has been presented since March 5th.\n",
    "  > - The value before March 5th can be different."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>date</th>\n",
       "      <th>time</th>\n",
       "      <th>province</th>\n",
       "      <th>confirmed</th>\n",
       "      <th>released</th>\n",
       "      <th>deceased</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-01-20</td>\n",
       "      <td>16</td>\n",
       "      <td>Seoul</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-01-20</td>\n",
       "      <td>16</td>\n",
       "      <td>Busan</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-01-20</td>\n",
       "      <td>16</td>\n",
       "      <td>Daegu</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-01-20</td>\n",
       "      <td>16</td>\n",
       "      <td>Incheon</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-01-20</td>\n",
       "      <td>16</td>\n",
       "      <td>Gwangju</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date  time province  confirmed  released  deceased\n",
       "0  2020-01-20    16    Seoul          0         0         0\n",
       "1  2020-01-20    16    Busan          0         0         0\n",
       "2  2020-01-20    16    Daegu          0         0         0\n",
       "3  2020-01-20    16  Incheon          1         0         0\n",
       "4  2020-01-20    16  Gwangju          0         0         0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t_provin.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {},
   "source": [
    "### 8) Region\n",
    "#### Location and statistical data of the regions in South Korea\n",
    "- code: the code of the region\n",
    "- province: Special City / Metropolitan City / Province(-do)\n",
    "- city: City(-si) / Country (-gun) / District (-gu)\n",
    "- latitude: the latitude of the visit (WGS84)\n",
    "- longitude: the longitude of the visit (WGS84)\n",
    "- elementary_school_count: the number of elementary schools\n",
    "- kindergarten_count: the number of kindergartens\n",
    "- university_count: the number of universities\n",
    "- academy_ratio: the ratio of academies\n",
    "- elderly_population_ratio: the ratio of the elderly population\n",
    "- elderly_alone_ratio: the ratio of elderly households living alone\n",
    "- nursing_home_count: the number of nursing homes\n",
    "\n",
    "Source of the statistic: [KOSTAT (Statistics Korea)](http://kosis.kr/)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>code</th>\n",
       "      <th>province</th>\n",
       "      <th>city</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>elementary_school_count</th>\n",
       "      <th>kindergarten_count</th>\n",
       "      <th>university_count</th>\n",
       "      <th>academy_ratio</th>\n",
       "      <th>elderly_population_ratio</th>\n",
       "      <th>elderly_alone_ratio</th>\n",
       "      <th>nursing_home_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10000</td>\n",
       "      <td>Seoul</td>\n",
       "      <td>Seoul</td>\n",
       "      <td>37.566953</td>\n",
       "      <td>126.977977</td>\n",
       "      <td>607</td>\n",
       "      <td>830</td>\n",
       "      <td>48</td>\n",
       "      <td>1.44</td>\n",
       "      <td>15.38</td>\n",
       "      <td>5.8</td>\n",
       "      <td>22739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10010</td>\n",
       "      <td>Seoul</td>\n",
       "      <td>Gangnam-gu</td>\n",
       "      <td>37.518421</td>\n",
       "      <td>127.047222</td>\n",
       "      <td>33</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "      <td>4.18</td>\n",
       "      <td>13.17</td>\n",
       "      <td>4.3</td>\n",
       "      <td>3088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10020</td>\n",
       "      <td>Seoul</td>\n",
       "      <td>Gangdong-gu</td>\n",
       "      <td>37.530492</td>\n",
       "      <td>127.123837</td>\n",
       "      <td>27</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>1.54</td>\n",
       "      <td>14.55</td>\n",
       "      <td>5.4</td>\n",
       "      <td>1023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10030</td>\n",
       "      <td>Seoul</td>\n",
       "      <td>Gangbuk-gu</td>\n",
       "      <td>37.639938</td>\n",
       "      <td>127.025508</td>\n",
       "      <td>14</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>0.67</td>\n",
       "      <td>19.49</td>\n",
       "      <td>8.5</td>\n",
       "      <td>628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10040</td>\n",
       "      <td>Seoul</td>\n",
       "      <td>Gangseo-gu</td>\n",
       "      <td>37.551166</td>\n",
       "      <td>126.849506</td>\n",
       "      <td>36</td>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>1.17</td>\n",
       "      <td>14.39</td>\n",
       "      <td>5.7</td>\n",
       "      <td>1080</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    code province         city   latitude   longitude  \\\n",
       "0  10000    Seoul        Seoul  37.566953  126.977977   \n",
       "1  10010    Seoul   Gangnam-gu  37.518421  127.047222   \n",
       "2  10020    Seoul  Gangdong-gu  37.530492  127.123837   \n",
       "3  10030    Seoul   Gangbuk-gu  37.639938  127.025508   \n",
       "4  10040    Seoul   Gangseo-gu  37.551166  126.849506   \n",
       "\n",
       "   elementary_school_count  kindergarten_count  university_count  \\\n",
       "0                      607                 830                48   \n",
       "1                       33                  38                 0   \n",
       "2                       27                  32                 0   \n",
       "3                       14                  21                 0   \n",
       "4                       36                  56                 1   \n",
       "\n",
       "   academy_ratio  elderly_population_ratio  elderly_alone_ratio  \\\n",
       "0           1.44                     15.38                  5.8   \n",
       "1           4.18                     13.17                  4.3   \n",
       "2           1.54                     14.55                  5.4   \n",
       "3           0.67                     19.49                  8.5   \n",
       "4           1.17                     14.39                  5.7   \n",
       "\n",
       "   nursing_home_count  \n",
       "0               22739  \n",
       "1                3088  \n",
       "2                1023  \n",
       "3                 628  \n",
       "4                1080  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "region.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {},
   "source": [
    "### 9) Weather\n",
    "#### Data of the weather in the regions of South Korea\n",
    "- code: the code of the region\n",
    "- province: Special City / Metropolitan City / Province(-do)\n",
    "- date: YYYY-MM-DD\n",
    "- avg_temp: the average temperature\n",
    "- min_temp: the lowest temperature\n",
    "- max_temp: the highest temperature\n",
    "- precipitation: the daily precipitation\n",
    "- max_wind_speed: the maximum wind speed\n",
    "- most_wind_direction: the most frequent wind direction\n",
    "- avg_relative_humidity: the average relative humidity\n",
    "\n",
    "Source of the weather data: [KMA (Korea Meteorological Administration)](http://data.kma.go.kr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>code</th>\n",
       "      <th>province</th>\n",
       "      <th>date</th>\n",
       "      <th>avg_temp</th>\n",
       "      <th>min_temp</th>\n",
       "      <th>max_temp</th>\n",
       "      <th>precipitation</th>\n",
       "      <th>max_wind_speed</th>\n",
       "      <th>most_wind_direction</th>\n",
       "      <th>avg_relative_humidity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10000</td>\n",
       "      <td>Seoul</td>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>1.2</td>\n",
       "      <td>-3.3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>90.0</td>\n",
       "      <td>73.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11000</td>\n",
       "      <td>Busan</td>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>5.3</td>\n",
       "      <td>1.1</td>\n",
       "      <td>10.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.4</td>\n",
       "      <td>340.0</td>\n",
       "      <td>52.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12000</td>\n",
       "      <td>Daegu</td>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>1.7</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.7</td>\n",
       "      <td>270.0</td>\n",
       "      <td>70.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13000</td>\n",
       "      <td>Gwangju</td>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>3.2</td>\n",
       "      <td>-1.5</td>\n",
       "      <td>8.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.7</td>\n",
       "      <td>230.0</td>\n",
       "      <td>73.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14000</td>\n",
       "      <td>Incheon</td>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>3.1</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>5.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.3</td>\n",
       "      <td>180.0</td>\n",
       "      <td>83.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    code province        date  avg_temp  min_temp  max_temp  precipitation  \\\n",
       "0  10000    Seoul  2016-01-01       1.2      -3.3       4.0            0.0   \n",
       "1  11000    Busan  2016-01-01       5.3       1.1      10.9            0.0   \n",
       "2  12000    Daegu  2016-01-01       1.7      -4.0       8.0            0.0   \n",
       "3  13000  Gwangju  2016-01-01       3.2      -1.5       8.1            0.0   \n",
       "4  14000  Incheon  2016-01-01       3.1      -0.4       5.7            0.0   \n",
       "\n",
       "   max_wind_speed  most_wind_direction  avg_relative_humidity  \n",
       "0             3.5                 90.0                   73.0  \n",
       "1             7.4                340.0                   52.1  \n",
       "2             3.7                270.0                   70.5  \n",
       "3             2.7                230.0                   73.1  \n",
       "4             5.3                180.0                   83.9  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weather.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {},
   "source": [
    "### 10) SearchTrend\n",
    "#### Trend data of the keywords searched in NAVER which is one of the largest portal in South Korea\n",
    "- date: YYYY-MM-DD\n",
    "- cold: the search volume of 'cold' in Korean language\n",
    "  > - The unit means relative value by setting the highest search volume in the period to 100.\n",
    "- flu: the search volume of 'flu' in Korean language\n",
    "  > - Same as above.\n",
    "- pneumonia: the search volume of 'pneumonia' in Korean language\n",
    "  > - Same as above.\n",
    "- coronavirus: the search volume of 'coronavirus' in Korean language\n",
    "  > - Same as above.\n",
    "\n",
    "\n",
    "Source of the data: [NAVER DataLab](https://datalab.naver.com/)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>cold</th>\n",
       "      <th>flu</th>\n",
       "      <th>pneumonia</th>\n",
       "      <th>coronavirus</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>0.11663</td>\n",
       "      <td>0.05590</td>\n",
       "      <td>0.15726</td>\n",
       "      <td>0.00736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-01-02</td>\n",
       "      <td>0.13372</td>\n",
       "      <td>0.17135</td>\n",
       "      <td>0.20826</td>\n",
       "      <td>0.00890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-01-03</td>\n",
       "      <td>0.14917</td>\n",
       "      <td>0.22317</td>\n",
       "      <td>0.19326</td>\n",
       "      <td>0.00845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-01-04</td>\n",
       "      <td>0.17463</td>\n",
       "      <td>0.18626</td>\n",
       "      <td>0.29008</td>\n",
       "      <td>0.01145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-01-05</td>\n",
       "      <td>0.17226</td>\n",
       "      <td>0.15072</td>\n",
       "      <td>0.24562</td>\n",
       "      <td>0.01381</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date     cold      flu  pneumonia  coronavirus\n",
       "0  2016-01-01  0.11663  0.05590    0.15726      0.00736\n",
       "1  2016-01-02  0.13372  0.17135    0.20826      0.00890\n",
       "2  2016-01-03  0.14917  0.22317    0.19326      0.00845\n",
       "3  2016-01-04  0.17463  0.18626    0.29008      0.01145\n",
       "4  2016-01-05  0.17226  0.15072    0.24562      0.01381"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "search.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {},
   "source": [
    "### 11) SeoulFloating\n",
    "#### Data of floating population in Seoul, South Korea (from SK Telecom Big Data Hub)\n",
    "\n",
    "- date: YYYY-MM-DD\n",
    "- hour: Hour\n",
    "- birth_year: the birth year of the floating population\n",
    "- sext: he sex of the floating population\n",
    "- province: Special City / Metropolitan City / Province(-do)\n",
    "- city: City(-si) / Country (-gun) / District (-gu)\n",
    "- fp_num: the number of floating population\n",
    "\n",
    "Source of the data: [SKT Big Data Hub](https://www.bigdatahub.co.kr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>hour</th>\n",
       "      <th>birth_year</th>\n",
       "      <th>sex</th>\n",
       "      <th>province</th>\n",
       "      <th>city</th>\n",
       "      <th>fp_num</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>female</td>\n",
       "      <td>Seoul</td>\n",
       "      <td>Dobong-gu</td>\n",
       "      <td>19140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>male</td>\n",
       "      <td>Seoul</td>\n",
       "      <td>Dobong-gu</td>\n",
       "      <td>19950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>female</td>\n",
       "      <td>Seoul</td>\n",
       "      <td>Dongdaemun-gu</td>\n",
       "      <td>25450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>male</td>\n",
       "      <td>Seoul</td>\n",
       "      <td>Dongdaemun-gu</td>\n",
       "      <td>27050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>female</td>\n",
       "      <td>Seoul</td>\n",
       "      <td>Dongjag-gu</td>\n",
       "      <td>28880</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date  hour  birth_year     sex province           city  fp_num\n",
       "0  2020-01-01     0          20  female    Seoul      Dobong-gu   19140\n",
       "1  2020-01-01     0          20    male    Seoul      Dobong-gu   19950\n",
       "2  2020-01-01     0          20  female    Seoul  Dongdaemun-gu   25450\n",
       "3  2020-01-01     0          20    male    Seoul  Dongdaemun-gu   27050\n",
       "4  2020-01-01     0          20  female    Seoul     Dongjag-gu   28880"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "floating.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {},
   "source": [
    "### 12) Policy\n",
    "#### Data of the government policy for COVID-19 in South Korea\n",
    "\n",
    "- policy_id: the ID of the policy\n",
    "- country: the country that implemented the policy\n",
    "- type: the type of the policy\n",
    "- gov_policy: the policy of the government\n",
    "- detail: the detail of the policy\n",
    "- start_date: the start date of the policy\n",
    "- end_date: the end date of the policy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>policy_id</th>\n",
       "      <th>country</th>\n",
       "      <th>type</th>\n",
       "      <th>gov_policy</th>\n",
       "      <th>detail</th>\n",
       "      <th>start_date</th>\n",
       "      <th>end_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Korea</td>\n",
       "      <td>Alert</td>\n",
       "      <td>Infectious Disease Alert Level</td>\n",
       "      <td>Level 1 (Blue)</td>\n",
       "      <td>2020-01-03</td>\n",
       "      <td>2020-01-19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Korea</td>\n",
       "      <td>Alert</td>\n",
       "      <td>Infectious Disease Alert Level</td>\n",
       "      <td>Level 2 (Yellow)</td>\n",
       "      <td>2020-01-20</td>\n",
       "      <td>2020-01-27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Korea</td>\n",
       "      <td>Alert</td>\n",
       "      <td>Infectious Disease Alert Level</td>\n",
       "      <td>Level 3 (Orange)</td>\n",
       "      <td>2020-01-28</td>\n",
       "      <td>2020-02-22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Korea</td>\n",
       "      <td>Alert</td>\n",
       "      <td>Infectious Disease Alert Level</td>\n",
       "      <td>Level 4 (Red)</td>\n",
       "      <td>2020-02-23</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Korea</td>\n",
       "      <td>Immigration</td>\n",
       "      <td>Special Immigration Procedure</td>\n",
       "      <td>from China</td>\n",
       "      <td>2020-02-04</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   policy_id country         type                      gov_policy  \\\n",
       "0          1   Korea        Alert  Infectious Disease Alert Level   \n",
       "1          2   Korea        Alert  Infectious Disease Alert Level   \n",
       "2          3   Korea        Alert  Infectious Disease Alert Level   \n",
       "3          4   Korea        Alert  Infectious Disease Alert Level   \n",
       "4          5   Korea  Immigration   Special Immigration Procedure   \n",
       "\n",
       "             detail  start_date    end_date  \n",
       "0    Level 1 (Blue)  2020-01-03  2020-01-19  \n",
       "1  Level 2 (Yellow)  2020-01-20  2020-01-27  \n",
       "2  Level 3 (Orange)  2020-01-28  2020-02-22  \n",
       "3     Level 4 (Red)  2020-02-23         NaN  \n",
       "4        from China  2020-02-04         NaN  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "policy.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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