{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.set_style(\"whitegrid\")\n",
    "# sns.set_context(\"paper\")\n",
    "sns.set(rc={'figure.figsize': (12,8)})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Download the data\n",
    "\n",
    "First, we download the data from the [John Hopkins Github](https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv) and make a inspection of the columns:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "url = \"https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv\"\n",
    "raw_data = pd.read_csv(url)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can inspect the data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "      <th>Province/State</th>\n",
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       "      <td>NaN</td>\n",
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       "      <td>35.233200</td>\n",
       "      <td>0</td>\n",
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       "      <td>390369</td>\n",
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       "      <td>Yemen</td>\n",
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       "      <td>8789</td>\n",
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       "    <tr>\n",
       "      <th>277</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Zambia</td>\n",
       "      <td>-13.133897</td>\n",
       "      <td>27.849332</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>207960</td>\n",
       "      <td>208049</td>\n",
       "      <td>208161</td>\n",
       "      <td>208267</td>\n",
       "      <td>208353</td>\n",
       "      <td>208422</td>\n",
       "      <td>208469</td>\n",
       "      <td>208502</td>\n",
       "      <td>208599</td>\n",
       "      <td>208676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>278</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>-19.015438</td>\n",
       "      <td>29.154857</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>126399</td>\n",
       "      <td>126817</td>\n",
       "      <td>127083</td>\n",
       "      <td>127368</td>\n",
       "      <td>127632</td>\n",
       "      <td>127739</td>\n",
       "      <td>127938</td>\n",
       "      <td>128186</td>\n",
       "      <td>128186</td>\n",
       "      <td>128804</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>279 rows × 614 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Province/State      Country/Region        Lat        Long  1/22/20  \\\n",
       "0              NaN         Afghanistan  33.939110   67.709953        0   \n",
       "1              NaN             Albania  41.153300   20.168300        0   \n",
       "2              NaN             Algeria  28.033900    1.659600        0   \n",
       "3              NaN             Andorra  42.506300    1.521800        0   \n",
       "4              NaN              Angola -11.202700   17.873900        0   \n",
       "..             ...                 ...        ...         ...      ...   \n",
       "274            NaN             Vietnam  14.058324  108.277199        0   \n",
       "275            NaN  West Bank and Gaza  31.952200   35.233200        0   \n",
       "276            NaN               Yemen  15.552727   48.516388        0   \n",
       "277            NaN              Zambia -13.133897   27.849332        0   \n",
       "278            NaN            Zimbabwe -19.015438   29.154857        0   \n",
       "\n",
       "     1/23/20  1/24/20  1/25/20  1/26/20  1/27/20  ...  9/13/21  9/14/21  \\\n",
       "0          0        0        0        0        0  ...   154094   154180   \n",
       "1          0        0        0        0        0  ...   157436   158431   \n",
       "2          0        0        0        0        0  ...   200301   200528   \n",
       "3          0        0        0        0        0  ...    15096    15099   \n",
       "4          0        0        0        0        0  ...    50738    51047   \n",
       "..       ...      ...      ...      ...      ...  ...      ...      ...   \n",
       "274        2        2        2        2        2  ...   624547   635055   \n",
       "275        0        0        0        0        0  ...   372108   374768   \n",
       "276        0        0        0        0        0  ...     8452     8502   \n",
       "277        0        0        0        0        0  ...   207960   208049   \n",
       "278        0        0        0        0        0  ...   126399   126817   \n",
       "\n",
       "     9/15/21  9/16/21  9/17/21  9/18/21  9/19/21  9/20/21  9/21/21  9/22/21  \n",
       "0     154283   154361   154487   154487   154487   154585   154712   154757  \n",
       "1     159423   160365   161324   162173   162953   163404   164276   165096  \n",
       "2     200770   200989   201224   201425   201600   201766   201948   202122  \n",
       "3      15108    15113    15124    15124    15124    15140    15140    15153  \n",
       "4      51407    51827    52208    52307    52307    52644    52968    53387  \n",
       "..       ...      ...      ...      ...      ...      ...      ...      ...  \n",
       "274   645640   656129   667650   677023   687063   695744   707436   718963  \n",
       "275   377134   379635   381854   382584   384390   386493   388470   390369  \n",
       "276     8527     8557     8593     8630     8667     8718     8752     8789  \n",
       "277   208161   208267   208353   208422   208469   208502   208599   208676  \n",
       "278   127083   127368   127632   127739   127938   128186   128186   128804  \n",
       "\n",
       "[279 rows x 614 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating the data for plotting:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will exclude the latitude and longitude columns since they are not useful:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
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       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Andorra</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>15096</td>\n",
       "      <td>15099</td>\n",
       "      <td>15108</td>\n",
       "      <td>15113</td>\n",
       "      <td>15124</td>\n",
       "      <td>15124</td>\n",
       "      <td>15124</td>\n",
       "      <td>15140</td>\n",
       "      <td>15140</td>\n",
       "      <td>15153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Angola</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>50738</td>\n",
       "      <td>51047</td>\n",
       "      <td>51407</td>\n",
       "      <td>51827</td>\n",
       "      <td>52208</td>\n",
       "      <td>52307</td>\n",
       "      <td>52307</td>\n",
       "      <td>52644</td>\n",
       "      <td>52968</td>\n",
       "      <td>53387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>274</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Vietnam</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>624547</td>\n",
       "      <td>635055</td>\n",
       "      <td>645640</td>\n",
       "      <td>656129</td>\n",
       "      <td>667650</td>\n",
       "      <td>677023</td>\n",
       "      <td>687063</td>\n",
       "      <td>695744</td>\n",
       "      <td>707436</td>\n",
       "      <td>718963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275</th>\n",
       "      <td>NaN</td>\n",
       "      <td>West Bank and Gaza</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>372108</td>\n",
       "      <td>374768</td>\n",
       "      <td>377134</td>\n",
       "      <td>379635</td>\n",
       "      <td>381854</td>\n",
       "      <td>382584</td>\n",
       "      <td>384390</td>\n",
       "      <td>386493</td>\n",
       "      <td>388470</td>\n",
       "      <td>390369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>276</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Yemen</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>8452</td>\n",
       "      <td>8502</td>\n",
       "      <td>8527</td>\n",
       "      <td>8557</td>\n",
       "      <td>8593</td>\n",
       "      <td>8630</td>\n",
       "      <td>8667</td>\n",
       "      <td>8718</td>\n",
       "      <td>8752</td>\n",
       "      <td>8789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>277</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Zambia</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>207960</td>\n",
       "      <td>208049</td>\n",
       "      <td>208161</td>\n",
       "      <td>208267</td>\n",
       "      <td>208353</td>\n",
       "      <td>208422</td>\n",
       "      <td>208469</td>\n",
       "      <td>208502</td>\n",
       "      <td>208599</td>\n",
       "      <td>208676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>278</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>126399</td>\n",
       "      <td>126817</td>\n",
       "      <td>127083</td>\n",
       "      <td>127368</td>\n",
       "      <td>127632</td>\n",
       "      <td>127739</td>\n",
       "      <td>127938</td>\n",
       "      <td>128186</td>\n",
       "      <td>128186</td>\n",
       "      <td>128804</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>279 rows × 612 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Province/State      Country/Region  1/22/20  1/23/20  1/24/20  1/25/20  \\\n",
       "0              NaN         Afghanistan        0        0        0        0   \n",
       "1              NaN             Albania        0        0        0        0   \n",
       "2              NaN             Algeria        0        0        0        0   \n",
       "3              NaN             Andorra        0        0        0        0   \n",
       "4              NaN              Angola        0        0        0        0   \n",
       "..             ...                 ...      ...      ...      ...      ...   \n",
       "274            NaN             Vietnam        0        2        2        2   \n",
       "275            NaN  West Bank and Gaza        0        0        0        0   \n",
       "276            NaN               Yemen        0        0        0        0   \n",
       "277            NaN              Zambia        0        0        0        0   \n",
       "278            NaN            Zimbabwe        0        0        0        0   \n",
       "\n",
       "     1/26/20  1/27/20  1/28/20  1/29/20  ...  9/13/21  9/14/21  9/15/21  \\\n",
       "0          0        0        0        0  ...   154094   154180   154283   \n",
       "1          0        0        0        0  ...   157436   158431   159423   \n",
       "2          0        0        0        0  ...   200301   200528   200770   \n",
       "3          0        0        0        0  ...    15096    15099    15108   \n",
       "4          0        0        0        0  ...    50738    51047    51407   \n",
       "..       ...      ...      ...      ...  ...      ...      ...      ...   \n",
       "274        2        2        2        2  ...   624547   635055   645640   \n",
       "275        0        0        0        0  ...   372108   374768   377134   \n",
       "276        0        0        0        0  ...     8452     8502     8527   \n",
       "277        0        0        0        0  ...   207960   208049   208161   \n",
       "278        0        0        0        0  ...   126399   126817   127083   \n",
       "\n",
       "     9/16/21  9/17/21  9/18/21  9/19/21  9/20/21  9/21/21  9/22/21  \n",
       "0     154361   154487   154487   154487   154585   154712   154757  \n",
       "1     160365   161324   162173   162953   163404   164276   165096  \n",
       "2     200989   201224   201425   201600   201766   201948   202122  \n",
       "3      15113    15124    15124    15124    15140    15140    15153  \n",
       "4      51827    52208    52307    52307    52644    52968    53387  \n",
       "..       ...      ...      ...      ...      ...      ...      ...  \n",
       "274   656129   667650   677023   687063   695744   707436   718963  \n",
       "275   379635   381854   382584   384390   386493   388470   390369  \n",
       "276     8557     8593     8630     8667     8718     8752     8789  \n",
       "277   208267   208353   208422   208469   208502   208599   208676  \n",
       "278   127368   127632   127739   127938   128186   128186   128804  \n",
       "\n",
       "[279 rows x 612 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = raw_data.drop(columns=[\"Lat\", \"Long\"])\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since we will exclude Hong-Kong from the China aggregated numbers, we can modify the country in the Hong-Kong province to Hong-Kong,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Province/State</th>\n",
       "      <th>Country/Region</th>\n",
       "      <th>1/22/20</th>\n",
       "      <th>1/23/20</th>\n",
       "      <th>1/24/20</th>\n",
       "      <th>1/25/20</th>\n",
       "      <th>1/26/20</th>\n",
       "      <th>1/27/20</th>\n",
       "      <th>1/28/20</th>\n",
       "      <th>1/29/20</th>\n",
       "      <th>...</th>\n",
       "      <th>9/13/21</th>\n",
       "      <th>9/14/21</th>\n",
       "      <th>9/15/21</th>\n",
       "      <th>9/16/21</th>\n",
       "      <th>9/17/21</th>\n",
       "      <th>9/18/21</th>\n",
       "      <th>9/19/21</th>\n",
       "      <th>9/20/21</th>\n",
       "      <th>9/21/21</th>\n",
       "      <th>9/22/21</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <td>154180</td>\n",
       "      <td>154283</td>\n",
       "      <td>154361</td>\n",
       "      <td>154487</td>\n",
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       "      <td>154487</td>\n",
       "      <td>154585</td>\n",
       "      <td>154712</td>\n",
       "      <td>154757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Albania</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>157436</td>\n",
       "      <td>158431</td>\n",
       "      <td>159423</td>\n",
       "      <td>160365</td>\n",
       "      <td>161324</td>\n",
       "      <td>162173</td>\n",
       "      <td>162953</td>\n",
       "      <td>163404</td>\n",
       "      <td>164276</td>\n",
       "      <td>165096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>200301</td>\n",
       "      <td>200528</td>\n",
       "      <td>200770</td>\n",
       "      <td>200989</td>\n",
       "      <td>201224</td>\n",
       "      <td>201425</td>\n",
       "      <td>201600</td>\n",
       "      <td>201766</td>\n",
       "      <td>201948</td>\n",
       "      <td>202122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Andorra</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>15096</td>\n",
       "      <td>15099</td>\n",
       "      <td>15108</td>\n",
       "      <td>15113</td>\n",
       "      <td>15124</td>\n",
       "      <td>15124</td>\n",
       "      <td>15124</td>\n",
       "      <td>15140</td>\n",
       "      <td>15140</td>\n",
       "      <td>15153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Angola</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>50738</td>\n",
       "      <td>51047</td>\n",
       "      <td>51407</td>\n",
       "      <td>51827</td>\n",
       "      <td>52208</td>\n",
       "      <td>52307</td>\n",
       "      <td>52307</td>\n",
       "      <td>52644</td>\n",
       "      <td>52968</td>\n",
       "      <td>53387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>274</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Vietnam</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>624547</td>\n",
       "      <td>635055</td>\n",
       "      <td>645640</td>\n",
       "      <td>656129</td>\n",
       "      <td>667650</td>\n",
       "      <td>677023</td>\n",
       "      <td>687063</td>\n",
       "      <td>695744</td>\n",
       "      <td>707436</td>\n",
       "      <td>718963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275</th>\n",
       "      <td>NaN</td>\n",
       "      <td>West Bank and Gaza</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>372108</td>\n",
       "      <td>374768</td>\n",
       "      <td>377134</td>\n",
       "      <td>379635</td>\n",
       "      <td>381854</td>\n",
       "      <td>382584</td>\n",
       "      <td>384390</td>\n",
       "      <td>386493</td>\n",
       "      <td>388470</td>\n",
       "      <td>390369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>276</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Yemen</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>8452</td>\n",
       "      <td>8502</td>\n",
       "      <td>8527</td>\n",
       "      <td>8557</td>\n",
       "      <td>8593</td>\n",
       "      <td>8630</td>\n",
       "      <td>8667</td>\n",
       "      <td>8718</td>\n",
       "      <td>8752</td>\n",
       "      <td>8789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>277</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Zambia</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>207960</td>\n",
       "      <td>208049</td>\n",
       "      <td>208161</td>\n",
       "      <td>208267</td>\n",
       "      <td>208353</td>\n",
       "      <td>208422</td>\n",
       "      <td>208469</td>\n",
       "      <td>208502</td>\n",
       "      <td>208599</td>\n",
       "      <td>208676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>278</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>126399</td>\n",
       "      <td>126817</td>\n",
       "      <td>127083</td>\n",
       "      <td>127368</td>\n",
       "      <td>127632</td>\n",
       "      <td>127739</td>\n",
       "      <td>127938</td>\n",
       "      <td>128186</td>\n",
       "      <td>128186</td>\n",
       "      <td>128804</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>279 rows × 612 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Province/State      Country/Region  1/22/20  1/23/20  1/24/20  1/25/20  \\\n",
       "0              NaN         Afghanistan        0        0        0        0   \n",
       "1              NaN             Albania        0        0        0        0   \n",
       "2              NaN             Algeria        0        0        0        0   \n",
       "3              NaN             Andorra        0        0        0        0   \n",
       "4              NaN              Angola        0        0        0        0   \n",
       "..             ...                 ...      ...      ...      ...      ...   \n",
       "274            NaN             Vietnam        0        2        2        2   \n",
       "275            NaN  West Bank and Gaza        0        0        0        0   \n",
       "276            NaN               Yemen        0        0        0        0   \n",
       "277            NaN              Zambia        0        0        0        0   \n",
       "278            NaN            Zimbabwe        0        0        0        0   \n",
       "\n",
       "     1/26/20  1/27/20  1/28/20  1/29/20  ...  9/13/21  9/14/21  9/15/21  \\\n",
       "0          0        0        0        0  ...   154094   154180   154283   \n",
       "1          0        0        0        0  ...   157436   158431   159423   \n",
       "2          0        0        0        0  ...   200301   200528   200770   \n",
       "3          0        0        0        0  ...    15096    15099    15108   \n",
       "4          0        0        0        0  ...    50738    51047    51407   \n",
       "..       ...      ...      ...      ...  ...      ...      ...      ...   \n",
       "274        2        2        2        2  ...   624547   635055   645640   \n",
       "275        0        0        0        0  ...   372108   374768   377134   \n",
       "276        0        0        0        0  ...     8452     8502     8527   \n",
       "277        0        0        0        0  ...   207960   208049   208161   \n",
       "278        0        0        0        0  ...   126399   126817   127083   \n",
       "\n",
       "     9/16/21  9/17/21  9/18/21  9/19/21  9/20/21  9/21/21  9/22/21  \n",
       "0     154361   154487   154487   154487   154585   154712   154757  \n",
       "1     160365   161324   162173   162953   163404   164276   165096  \n",
       "2     200989   201224   201425   201600   201766   201948   202122  \n",
       "3      15113    15124    15124    15124    15140    15140    15153  \n",
       "4      51827    52208    52307    52307    52644    52968    53387  \n",
       "..       ...      ...      ...      ...      ...      ...      ...  \n",
       "274   656129   667650   677023   687063   695744   707436   718963  \n",
       "275   379635   381854   382584   384390   386493   388470   390369  \n",
       "276     8557     8593     8630     8667     8718     8752     8789  \n",
       "277   208267   208353   208422   208469   208502   208599   208676  \n",
       "278   127368   127632   127739   127938   128186   128186   128804  \n",
       "\n",
       "[279 rows x 612 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.loc[data[\"Province/State\"]==\"Hong Kong\", \"Country/Region\"] = \"Hong Kong\"\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will also exclude all the France regions that are not metropolitane France. First, let's take a look at the possible provinces:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['French Guiana', 'French Polynesia', 'Guadeloupe', 'Martinique',\n",
       "       'Mayotte', 'New Caledonia', 'Reunion', 'Saint Barthelemy',\n",
       "       'Saint Pierre and Miquelon', 'St Martin', 'Wallis and Futuna', nan],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data[\"Country/Region\"]==\"France\"][\"Province/State\"].values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we can exclude all the values besides the last one:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "list_of_regions = data[data[\"Country/Region\"]==\"France\"][\"Province/State\"].values[:-1]\n",
    "data = data.drop(data[(data[\"Country/Region\"] == \"France\") & (data[\"Province/State\"].isin(list_of_regions))].index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We do the same thing with Netherlands and UK:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Anguilla' 'Bermuda' 'British Virgin Islands' 'Cayman Islands'\n",
      " 'Channel Islands' 'Falkland Islands (Malvinas)' 'Gibraltar' 'Isle of Man'\n",
      " 'Montserrat' 'Saint Helena, Ascension and Tristan da Cunha'\n",
      " 'Turks and Caicos Islands' nan]\n",
      "['Aruba' 'Bonaire, Sint Eustatius and Saba' 'Curacao' 'Sint Maarten' nan]\n"
     ]
    }
   ],
   "source": [
    "print(data[data[\"Country/Region\"]==\"United Kingdom\"][\"Province/State\"].values)\n",
    "print(data[data[\"Country/Region\"]==\"Netherlands\"][\"Province/State\"].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "regions_netherlands = data[data[\"Country/Region\"]==\"Netherlands\"][\"Province/State\"].values[:-1]\n",
    "data = data.drop(data[(data[\"Country/Region\"] == \"Netherlands\") & (data[\"Province/State\"].isin(regions_netherlands))].index)\n",
    "\n",
    "regions_uk = data[data[\"Country/Region\"]==\"United Kingdom\"][\"Province/State\"].values[:-1]\n",
    "data = data.drop(data[(data[\"Country/Region\"] == \"United Kingdom\") & (data[\"Province/State\"].isin(regions_uk))].index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizations:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can make our plots, but first we need to group the data by country:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>1/22/20</th>\n",
       "      <th>1/23/20</th>\n",
       "      <th>1/24/20</th>\n",
       "      <th>1/25/20</th>\n",
       "      <th>1/26/20</th>\n",
       "      <th>1/27/20</th>\n",
       "      <th>1/28/20</th>\n",
       "      <th>1/29/20</th>\n",
       "      <th>1/30/20</th>\n",
       "      <th>1/31/20</th>\n",
       "      <th>...</th>\n",
       "      <th>9/13/21</th>\n",
       "      <th>9/14/21</th>\n",
       "      <th>9/15/21</th>\n",
       "      <th>9/16/21</th>\n",
       "      <th>9/17/21</th>\n",
       "      <th>9/18/21</th>\n",
       "      <th>9/19/21</th>\n",
       "      <th>9/20/21</th>\n",
       "      <th>9/21/21</th>\n",
       "      <th>9/22/21</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country/Region</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Afghanistan</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>154094</td>\n",
       "      <td>154180</td>\n",
       "      <td>154283</td>\n",
       "      <td>154361</td>\n",
       "      <td>154487</td>\n",
       "      <td>154487</td>\n",
       "      <td>154487</td>\n",
       "      <td>154585</td>\n",
       "      <td>154712</td>\n",
       "      <td>154757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>157436</td>\n",
       "      <td>158431</td>\n",
       "      <td>159423</td>\n",
       "      <td>160365</td>\n",
       "      <td>161324</td>\n",
       "      <td>162173</td>\n",
       "      <td>162953</td>\n",
       "      <td>163404</td>\n",
       "      <td>164276</td>\n",
       "      <td>165096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algeria</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>200301</td>\n",
       "      <td>200528</td>\n",
       "      <td>200770</td>\n",
       "      <td>200989</td>\n",
       "      <td>201224</td>\n",
       "      <td>201425</td>\n",
       "      <td>201600</td>\n",
       "      <td>201766</td>\n",
       "      <td>201948</td>\n",
       "      <td>202122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andorra</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>15096</td>\n",
       "      <td>15099</td>\n",
       "      <td>15108</td>\n",
       "      <td>15113</td>\n",
       "      <td>15124</td>\n",
       "      <td>15124</td>\n",
       "      <td>15124</td>\n",
       "      <td>15140</td>\n",
       "      <td>15140</td>\n",
       "      <td>15153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Angola</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>50738</td>\n",
       "      <td>51047</td>\n",
       "      <td>51407</td>\n",
       "      <td>51827</td>\n",
       "      <td>52208</td>\n",
       "      <td>52307</td>\n",
       "      <td>52307</td>\n",
       "      <td>52644</td>\n",
       "      <td>52968</td>\n",
       "      <td>53387</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 610 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                1/22/20  1/23/20  1/24/20  1/25/20  1/26/20  1/27/20  1/28/20  \\\n",
       "Country/Region                                                                  \n",
       "Afghanistan           0        0        0        0        0        0        0   \n",
       "Albania               0        0        0        0        0        0        0   \n",
       "Algeria               0        0        0        0        0        0        0   \n",
       "Andorra               0        0        0        0        0        0        0   \n",
       "Angola                0        0        0        0        0        0        0   \n",
       "\n",
       "                1/29/20  1/30/20  1/31/20  ...  9/13/21  9/14/21  9/15/21  \\\n",
       "Country/Region                             ...                              \n",
       "Afghanistan           0        0        0  ...   154094   154180   154283   \n",
       "Albania               0        0        0  ...   157436   158431   159423   \n",
       "Algeria               0        0        0  ...   200301   200528   200770   \n",
       "Andorra               0        0        0  ...    15096    15099    15108   \n",
       "Angola                0        0        0  ...    50738    51047    51407   \n",
       "\n",
       "                9/16/21  9/17/21  9/18/21  9/19/21  9/20/21  9/21/21  9/22/21  \n",
       "Country/Region                                                                 \n",
       "Afghanistan      154361   154487   154487   154487   154585   154712   154757  \n",
       "Albania          160365   161324   162173   162953   163404   164276   165096  \n",
       "Algeria          200989   201224   201425   201600   201766   201948   202122  \n",
       "Andorra           15113    15124    15124    15124    15140    15140    15153  \n",
       "Angola            51827    52208    52307    52307    52644    52968    53387  \n",
       "\n",
       "[5 rows x 610 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_grouped = data.groupby('Country/Region').sum()\n",
    "data_grouped.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can plot our selected countries:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Infection Cases of COVID-19 in Linear Scale')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_grouped.loc['Hong Kong'].plot()\n",
    "data_grouped.loc['China'].plot()\n",
    "data_grouped.loc['US'].plot()\n",
    "data_grouped.loc['France'].plot()\n",
    "data_grouped.loc['United Kingdom'].plot()\n",
    "data_grouped.loc['Belgium'].plot()\n",
    "data_grouped.loc['Germany'].plot()\n",
    "data_grouped.loc['Iran'].plot()\n",
    "data_grouped.loc['Italy'].plot()\n",
    "data_grouped.loc['Spain'].plot()\n",
    "data_grouped.loc['Japan'].plot()\n",
    "data_grouped.loc['Korea, South'].plot()\n",
    "data_grouped.loc['Portugal'].plot()\n",
    "plt.legend()\n",
    "plt.title('Infection Cases of COVID-19 in Linear Scale')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Infection Cases of COVID-19 in Log scale')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_grouped.loc['Hong Kong'].plot()\n",
    "data_grouped.loc['China'].plot()\n",
    "data_grouped.loc['US'].plot()\n",
    "data_grouped.loc['France'].plot()\n",
    "data_grouped.loc['United Kingdom'].plot()\n",
    "data_grouped.loc['Belgium'].plot()\n",
    "data_grouped.loc['Germany'].plot()\n",
    "data_grouped.loc['Iran'].plot()\n",
    "data_grouped.loc['Italy'].plot()\n",
    "data_grouped.loc['Spain'].plot()\n",
    "data_grouped.loc['Japan'].plot()\n",
    "data_grouped.loc['Korea, South'].plot()\n",
    "data_grouped.loc['Portugal'].plot()\n",
    "plt.legend()\n",
    "plt.yscale(\"log\")\n",
    "plt.title('Infection Cases of COVID-19 in Log scale')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}