CoronaVirus - 2019.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import Libraries and Read Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Import Libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from pandas.plotting import register_matplotlib_converters\n",
    "register_matplotlib_converters()\n",
    "#%matplotlib inline\n",
    "import plotly.express as px\n",
    "import plotly.graph_objects as go\n",
    "from plotly.subplots import make_subplots\n",
    "plt.rcParams['figure.figsize'] = [15, 5]\n",
    "from IPython import display\n",
    "from ipywidgets import interact, widgets\n",
    "\n",
    "## Read Data for Cases, Deaths and Recoveries\n",
    "ConfirmedCases_raw=pd.read_csv('https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_19-covid-Confirmed.csv')\n",
    "Deaths_raw=pd.read_csv('https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_19-covid-Deaths.csv')\n",
    "Recoveries_raw=pd.read_csv('https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_19-covid-Recovered.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Province/State</th>\n",
       "      <th>Country/Region</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Long</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>...</th>\n",
       "      <th>2/29/20</th>\n",
       "      <th>3/1/20</th>\n",
       "      <th>3/2/20</th>\n",
       "      <th>3/3/20</th>\n",
       "      <th>3/4/20</th>\n",
       "      <th>3/5/20</th>\n",
       "      <th>3/6/20</th>\n",
       "      <th>3/7/20</th>\n",
       "      <th>3/8/20</th>\n",
       "      <th>3/9/20</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Anhui</td>\n",
       "      <td>Mainland China</td>\n",
       "      <td>31.8257</td>\n",
       "      <td>117.2264</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>15</td>\n",
       "      <td>39</td>\n",
       "      <td>60</td>\n",
       "      <td>70</td>\n",
       "      <td>...</td>\n",
       "      <td>990</td>\n",
       "      <td>990</td>\n",
       "      <td>990</td>\n",
       "      <td>990</td>\n",
       "      <td>990</td>\n",
       "      <td>990</td>\n",
       "      <td>990</td>\n",
       "      <td>990</td>\n",
       "      <td>990</td>\n",
       "      <td>990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Beijing</td>\n",
       "      <td>Mainland China</td>\n",
       "      <td>40.1824</td>\n",
       "      <td>116.4142</td>\n",
       "      <td>14</td>\n",
       "      <td>22</td>\n",
       "      <td>36</td>\n",
       "      <td>41</td>\n",
       "      <td>68</td>\n",
       "      <td>80</td>\n",
       "      <td>...</td>\n",
       "      <td>411</td>\n",
       "      <td>413</td>\n",
       "      <td>414</td>\n",
       "      <td>414</td>\n",
       "      <td>418</td>\n",
       "      <td>418</td>\n",
       "      <td>422</td>\n",
       "      <td>426</td>\n",
       "      <td>428</td>\n",
       "      <td>428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Chongqing</td>\n",
       "      <td>Mainland China</td>\n",
       "      <td>30.0572</td>\n",
       "      <td>107.8740</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>27</td>\n",
       "      <td>57</td>\n",
       "      <td>75</td>\n",
       "      <td>110</td>\n",
       "      <td>...</td>\n",
       "      <td>576</td>\n",
       "      <td>576</td>\n",
       "      <td>576</td>\n",
       "      <td>576</td>\n",
       "      <td>576</td>\n",
       "      <td>576</td>\n",
       "      <td>576</td>\n",
       "      <td>576</td>\n",
       "      <td>576</td>\n",
       "      <td>576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Fujian</td>\n",
       "      <td>Mainland China</td>\n",
       "      <td>26.0789</td>\n",
       "      <td>117.9874</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>18</td>\n",
       "      <td>35</td>\n",
       "      <td>59</td>\n",
       "      <td>...</td>\n",
       "      <td>296</td>\n",
       "      <td>296</td>\n",
       "      <td>296</td>\n",
       "      <td>296</td>\n",
       "      <td>296</td>\n",
       "      <td>296</td>\n",
       "      <td>296</td>\n",
       "      <td>296</td>\n",
       "      <td>296</td>\n",
       "      <td>296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Gansu</td>\n",
       "      <td>Mainland China</td>\n",
       "      <td>36.0611</td>\n",
       "      <td>103.8343</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>14</td>\n",
       "      <td>...</td>\n",
       "      <td>91</td>\n",
       "      <td>91</td>\n",
       "      <td>91</td>\n",
       "      <td>91</td>\n",
       "      <td>91</td>\n",
       "      <td>102</td>\n",
       "      <td>119</td>\n",
       "      <td>120</td>\n",
       "      <td>124</td>\n",
       "      <td>124</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 52 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  Province/State  Country/Region      Lat      Long  1/22/20  1/23/20  \\\n",
       "0          Anhui  Mainland China  31.8257  117.2264        1        9   \n",
       "1        Beijing  Mainland China  40.1824  116.4142       14       22   \n",
       "2      Chongqing  Mainland China  30.0572  107.8740        6        9   \n",
       "3         Fujian  Mainland China  26.0789  117.9874        1        5   \n",
       "4          Gansu  Mainland China  36.0611  103.8343        0        2   \n",
       "\n",
       "   1/24/20  1/25/20  1/26/20  1/27/20  ...  2/29/20  3/1/20  3/2/20  3/3/20  \\\n",
       "0       15       39       60       70  ...      990     990     990     990   \n",
       "1       36       41       68       80  ...      411     413     414     414   \n",
       "2       27       57       75      110  ...      576     576     576     576   \n",
       "3       10       18       35       59  ...      296     296     296     296   \n",
       "4        2        4        7       14  ...       91      91      91      91   \n",
       "\n",
       "   3/4/20  3/5/20  3/6/20  3/7/20  3/8/20  3/9/20  \n",
       "0     990     990     990     990     990     990  \n",
       "1     418     418     422     426     428     428  \n",
       "2     576     576     576     576     576     576  \n",
       "3     296     296     296     296     296     296  \n",
       "4      91     102     119     120     124     124  \n",
       "\n",
       "[5 rows x 52 columns]"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ConfirmedCases_raw.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Melt the dateframe into the right shape and set index\n",
    "def cleandata(df_raw):\n",
    "    df_cleaned=df_raw.melt(id_vars=['Province/State','Country/Region','Lat','Long'],value_name='Cases',var_name='Date')\n",
    "    df_cleaned=df_cleaned.set_index(['Country/Region','Province/State','Date'])\n",
    "    return df_cleaned \n",
    "\n",
    "### Get Countrywise Data\n",
    "def countrydata(df_cleaned,oldname,newname):\n",
    "    df_country=df_cleaned.groupby(['Country/Region','Date'])['Cases'].sum().reset_index()\n",
    "    df_country=df_country.set_index(['Country/Region','Date'])\n",
    "    df_country.index=df_country.index.set_levels([df_country.index.levels[0], pd.to_datetime(df_country.index.levels[1])])\n",
    "    df_country=df_country.sort_values(['Country/Region','Date'],ascending=True)\n",
    "    df_country=df_country.rename(columns={oldname:newname})\n",
    "    return df_country\n",
    "\n",
    "### Get DailyData from Cumulative sum\n",
    "def dailydata(dfcountry,oldname,newname):\n",
    "    dfcountrydaily=dfcountry.groupby(level=0).diff().fillna(0)\n",
    "    dfcountrydaily=dfcountrydaily.rename(columns={oldname:newname})\n",
    "    return dfcountrydaily"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Clean all datasets\n",
    "ConfirmedCases=cleandata(ConfirmedCases_raw)\n",
    "Deaths=cleandata(Deaths_raw)\n",
    "Recoveries=cleandata(Recoveries_raw)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Lat</th>\n",
       "      <th>Long</th>\n",
       "      <th>Cases</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country/Region</th>\n",
       "      <th>Province/State</th>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td rowspan=\"5\" valign=\"top\">Mainland China</td>\n",
       "      <td>Anhui</td>\n",
       "      <td>1/22/20</td>\n",
       "      <td>31.8257</td>\n",
       "      <td>117.2264</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Beijing</td>\n",
       "      <td>1/22/20</td>\n",
       "      <td>40.1824</td>\n",
       "      <td>116.4142</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Chongqing</td>\n",
       "      <td>1/22/20</td>\n",
       "      <td>30.0572</td>\n",
       "      <td>107.8740</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Fujian</td>\n",
       "      <td>1/22/20</td>\n",
       "      <td>26.0789</td>\n",
       "      <td>117.9874</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Gansu</td>\n",
       "      <td>1/22/20</td>\n",
       "      <td>36.0611</td>\n",
       "      <td>103.8343</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "                                           Lat      Long  Cases\n",
       "Country/Region Province/State Date                             \n",
       "Mainland China Anhui          1/22/20  31.8257  117.2264      1\n",
       "               Beijing        1/22/20  40.1824  116.4142     14\n",
       "               Chongqing      1/22/20  30.0572  107.8740      6\n",
       "               Fujian         1/22/20  26.0789  117.9874      1\n",
       "               Gansu          1/22/20  36.0611  103.8343      0"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ConfirmedCases.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get countrywise data\n",
    "ConfirmedCasesCountry=countrydata(ConfirmedCases,'Cases','Total Confirmed Cases')\n",
    "DeathsCountry=countrydata(Deaths,'Cases','Total Deaths')\n",
    "RecoveriesCountry=countrydata(Recoveries,'Cases','Total Recoveries')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get Daily Data\n",
    "NewCasesCountry=dailydata(ConfirmedCasesCountry,'Total Confirmed Cases','Daily New Cases')\n",
    "NewDeathsCountry=dailydata(DeathsCountry,'Total Deaths','Daily New Deaths')\n",
    "NewRecoveriesCountry=dailydata(RecoveriesCountry,'Total Recoveries','Daily New Recoveries')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "CountryConsolidated=pd.merge(ConfirmedCasesCountry,NewCasesCountry,how='left',left_index=True,right_index=True)\n",
    "CountryConsolidated=pd.merge(CountryConsolidated,NewDeathsCountry,how='left',left_index=True,right_index=True)\n",
    "CountryConsolidated=pd.merge(CountryConsolidated,DeathsCountry,how='left',left_index=True,right_index=True)\n",
    "CountryConsolidated=pd.merge(CountryConsolidated,RecoveriesCountry,how='left',left_index=True,right_index=True)\n",
    "CountryConsolidated=pd.merge(CountryConsolidated,NewRecoveriesCountry,how='left',left_index=True,right_index=True)\n",
    "CountryConsolidated['Active Cases']=CountryConsolidated['Total Confirmed Cases']-CountryConsolidated['Total Deaths']-CountryConsolidated['Total Recoveries']\n",
    "CountryConsolidated['Share of Recoveries - Closed Cases']=np.round(CountryConsolidated['Total Recoveries']/(CountryConsolidated['Total Recoveries']+CountryConsolidated['Total Deaths']),2)\n",
    "CountryConsolidated['Death to Cases Ratio']=np.round(CountryConsolidated['Total Deaths']/CountryConsolidated['Total Confirmed Cases'],3)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "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>Total Confirmed Cases</th>\n",
       "      <th>Daily New Cases</th>\n",
       "      <th>Daily New Deaths</th>\n",
       "      <th>Total Deaths</th>\n",
       "      <th>Total Recoveries</th>\n",
       "      <th>Daily New Recoveries</th>\n",
       "      <th>Active Cases</th>\n",
       "      <th>Share of Recoveries - Closed Cases</th>\n",
       "      <th>Death to Cases Ratio</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</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",
       "      <td>2020-03-08</td>\n",
       "      <td>109814</td>\n",
       "      <td>3979.0</td>\n",
       "      <td>245.0</td>\n",
       "      <td>3803</td>\n",
       "      <td>60695</td>\n",
       "      <td>2336.0</td>\n",
       "      <td>45316</td>\n",
       "      <td>0.94</td>\n",
       "      <td>0.035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-03-09</td>\n",
       "      <td>113583</td>\n",
       "      <td>3769.0</td>\n",
       "      <td>193.0</td>\n",
       "      <td>3996</td>\n",
       "      <td>62512</td>\n",
       "      <td>1817.0</td>\n",
       "      <td>47075</td>\n",
       "      <td>0.94</td>\n",
       "      <td>0.035</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Total Confirmed Cases  Daily New Cases  Daily New Deaths  \\\n",
       "Date                                                                   \n",
       "2020-03-08                 109814           3979.0             245.0   \n",
       "2020-03-09                 113583           3769.0             193.0   \n",
       "\n",
       "            Total Deaths  Total Recoveries  Daily New Recoveries  \\\n",
       "Date                                                               \n",
       "2020-03-08          3803             60695                2336.0   \n",
       "2020-03-09          3996             62512                1817.0   \n",
       "\n",
       "            Active Cases  Share of Recoveries - Closed Cases  \\\n",
       "Date                                                           \n",
       "2020-03-08         45316                                0.94   \n",
       "2020-03-09         47075                                0.94   \n",
       "\n",
       "            Death to Cases Ratio  \n",
       "Date                              \n",
       "2020-03-08                 0.035  \n",
       "2020-03-09                 0.035  "
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GlobalTotals=CountryConsolidated.reset_index().groupby('Date').sum()\n",
    "GlobalTotals['Share of Recoveries - Closed Cases']=np.round(GlobalTotals['Total Recoveries']/(GlobalTotals['Total Recoveries']+GlobalTotals['Total Deaths']),2)\n",
    "GlobalTotals['Death to Cases Ratio']=np.round(GlobalTotals['Total Deaths']/GlobalTotals['Total Confirmed Cases'],3)\n",
    "GlobalTotals.tail(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Cases Worldwide by Status: Total Confirmed, Active, Deaths, Recoveries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Total Confirmed Cases                 113583.000\n",
       "Daily New Cases                         3769.000\n",
       "Daily New Deaths                         193.000\n",
       "Total Deaths                            3996.000\n",
       "Total Recoveries                       62512.000\n",
       "Daily New Recoveries                    1817.000\n",
       "Active Cases                           47075.000\n",
       "Share of Recoveries - Closed Cases         0.940\n",
       "Death to Cases Ratio                       0.035\n",
       "Name: 2020-03-09 00:00:00, dtype: float64"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GlobalTotals.iloc[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.style.use('seaborn-dark')"
   ]
  },
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     "metadata": {},
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   ],
   "source": [
    "chartcol='red'\n",
    "fig = make_subplots(rows=3, cols=2,shared_xaxes=True,\n",
    "                    specs=[[{}, {}],[{},{}],\n",
    "                       [{\"colspan\": 2}, None]],\n",
    "                    subplot_titles=('Total Confirmed Cases','Active Cases','Deaths','Recoveries','Death to Cases Ratio'))\n",
    "fig.add_trace(go.Scatter(x=GlobalTotals.index,y=GlobalTotals['Total Confirmed Cases'],\n",
    "                         mode='lines+markers',\n",
    "                         name='Confirmed Cases',\n",
    "                         line=dict(color=chartcol,width=2)),\n",
    "                         row=1,col=1)\n",
    "fig.add_trace(go.Scatter(x=GlobalTotals.index,y=GlobalTotals['Active Cases'],\n",
    "                         mode='lines+markers',\n",
    "                         name='Active Cases',\n",
    "                         line=dict(color=chartcol,width=2)),\n",
    "                         row=1,col=2)\n",
    "fig.add_trace(go.Scatter(x=GlobalTotals.index,y=GlobalTotals['Total Deaths'],\n",
    "                         mode='lines+markers',\n",
    "                         name='Deaths',\n",
    "                         line=dict(color=chartcol,width=2)),\n",
    "                         row=2,col=1)\n",
    "fig.add_trace(go.Scatter(x=GlobalTotals.index,y=GlobalTotals['Total Recoveries'],\n",
    "                         mode='lines+markers',\n",
    "                         name='Recoveries',\n",
    "                         line=dict(color=chartcol,width=2)),\n",
    "                         row=2,col=2)\n",
    "fig.add_trace(go.Scatter(x=GlobalTotals.index,y=GlobalTotals['Death to Cases Ratio'],\n",
    "                         mode='lines+markers',\n",
    "                         line=dict(color=chartcol,width=2)),\n",
    "                         row=3,col=1)\n",
    "fig.update_layout(showlegend=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cases by Region (Insite or Outside China)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Date</th>\n",
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       "      <td>Afghanistan</td>\n",
       "      <td>2020-01-22</td>\n",
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       "      <td>Outside China</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-01-23</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Outside China</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-01-24</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Outside China</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-01-25</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Outside China</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-01-26</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Outside China</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Country/Region       Date  Total Confirmed Cases  Daily New Cases  \\\n",
       "0    Afghanistan 2020-01-22                      0              0.0   \n",
       "1    Afghanistan 2020-01-23                      0              0.0   \n",
       "2    Afghanistan 2020-01-24                      0              0.0   \n",
       "3    Afghanistan 2020-01-25                      0              0.0   \n",
       "4    Afghanistan 2020-01-26                      0              0.0   \n",
       "\n",
       "   Daily New Deaths  Total Deaths  Total Recoveries  Daily New Recoveries  \\\n",
       "0               0.0             0                 0                   0.0   \n",
       "1               0.0             0                 0                   0.0   \n",
       "2               0.0             0                 0                   0.0   \n",
       "3               0.0             0                 0                   0.0   \n",
       "4               0.0             0                 0                   0.0   \n",
       "\n",
       "   Active Cases  Share of Recoveries - Closed Cases  Death to Cases Ratio  \\\n",
       "0             0                                 NaN                   NaN   \n",
       "1             0                                 NaN                   NaN   \n",
       "2             0                                 NaN                   NaN   \n",
       "3             0                                 NaN                   NaN   \n",
       "4             0                                 NaN                   NaN   \n",
       "\n",
       "        location  \n",
       "0  Outside China  \n",
       "1  Outside China  \n",
       "2  Outside China  \n",
       "3  Outside China  \n",
       "4  Outside China  "
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#CountryConsolidated['location']=np.where(CountryConsolidated['Country/Region']=='Mainland China','Mainland China','Outside China')\n",
    "locdata=CountryConsolidated.reset_index()\n",
    "locdata['location']=np.where(locdata['Country/Region']=='Mainland China','Mainland China','Outside China')\n",
    "locdata.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "scrolled": true
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   "outputs": [
    {
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       "      <td>25</td>\n",
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       "      <td>51</td>\n",
       "      <td>Outside China</td>\n",
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       "      <td>Outside China</td>\n",
       "      <td>2020-01-26</td>\n",
       "      <td>53</td>\n",
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       "         Location       Date  Active Cases\n",
       "48  Outside China 2020-01-22             8\n",
       "49  Outside China 2020-01-23            14\n",
       "50  Outside China 2020-01-24            25\n",
       "51  Outside China 2020-01-25            35\n",
       "52  Outside China 2020-01-26            53"
      ]
     },
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   "source": [
    "# Cases by Region (inside or Outside China)\n",
    "TotalCasesLoc=CountryConsolidated.reset_index()\n",
    "TotalCasesLoc.head()\n",
    "TotalCasesLoc['Location']=np.where(TotalCasesLoc['Country/Region']=='Mainland China','Mainland China','Outside China')\n",
    "TotalCasesLocaggregated=TotalCasesLoc.groupby(['Location','Date'])['Active Cases'].sum().reset_index()\n",
    "ActiveCasesChina=TotalCasesLocaggregated[TotalCasesLocaggregated.loc[:,'Location']=='Mainland China']\n",
    "ActiveCasesExcludingChina=TotalCasesLocaggregated[TotalCasesLocaggregated.loc[:,'Location']=='Outside China']\n",
    "ActiveCasesExcludingChina.head()"
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       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
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       "\n",
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       "var notebookContainer = gd.closest('#notebook-container');\n",
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       "    x.observe(notebookContainer, {childList: true});\n",
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       "    x.observe(outputEl, {childList: true});\n",
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       "\n",
       "                        })\n",
       "                };\n",
       "                });\n",
       "            </script>\n",
       "        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = make_subplots(rows=1, cols=2,shared_xaxes=True,\n",
    "                   subplot_titles=('Active Cases in Mainland China','Active Cases Excluding Mainland China'))\n",
    "fig.add_trace(go.Scatter(x=ActiveCasesChina['Date'],y=ActiveCasesChina['Active Cases'],\n",
    "                         mode='lines+markers',\n",
    "                         name='New Cases',\n",
    "                         line=dict(color=chartcol,width=2)),\n",
    "                         row=1,col=1)\n",
    "fig.add_trace(go.Scatter(x=ActiveCasesExcludingChina['Date'],y=ActiveCasesExcludingChina['Active Cases'],\n",
    "                         mode='lines+markers',\n",
    "                         name='Active Cases',\n",
    "                         line=dict(color=chartcol,width=2)),\n",
    "                         row=1,col=2)\n",
    "fig.update_layout(showlegend=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cases by Country"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "394448d2fe304ab08f54145d83e65d6e",
       "version_major": 2,
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      },
      "text/plain": [
       "interactive(children=(Dropdown(description='Country', options=('Germany', 'Italy', 'US', 'Australia', 'Pakista…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<function __main__.plotcountry(Country)>"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def plotcountry(Country):\n",
    "    fig = make_subplots(rows=3, cols=2,shared_xaxes=True,\n",
    "                    specs=[[{}, {}],[{},{}],\n",
    "                       [{\"colspan\": 2}, None]],\n",
    "                    subplot_titles=('Total Confirmed Cases','Active Cases','Deaths','Recoveries','Death to Cases Ratio'))\n",
    "    fig.add_trace(go.Scatter(x=CountryConsolidated.loc[Country].index,y=CountryConsolidated.loc[Country,'Total Confirmed Cases'],\n",
    "                         mode='lines+markers',\n",
    "                         name='Confirmed Cases',\n",
    "                         line=dict(color=chartcol,width=2)),\n",
    "                         row=1,col=1)\n",
    "    fig.add_trace(go.Scatter(x=CountryConsolidated.loc[Country].index,y=CountryConsolidated.loc[Country,'Active Cases'],\n",
    "                         mode='lines+markers',\n",
    "                         name='Active Cases',\n",
    "                         line=dict(color=chartcol,width=2)),\n",
    "                         row=1,col=2)\n",
    "    fig.add_trace(go.Scatter(x=CountryConsolidated.loc[Country].index,y=CountryConsolidated.loc[Country,'Total Deaths'],\n",
    "                         mode='lines+markers',\n",
    "                         name='Total Deaths',\n",
    "                         line=dict(color=chartcol,width=2)),\n",
    "                         row=2,col=1)\n",
    "    fig.add_trace(go.Scatter(x=CountryConsolidated.loc[Country].index,y=CountryConsolidated.loc[Country,'Total Recoveries'],\n",
    "                         mode='lines+markers',\n",
    "                         name='Total Recoveries',\n",
    "                         line=dict(color=chartcol,width=2)),\n",
    "                         row=2,col=2)\n",
    "    fig.add_trace(go.Scatter(x=CountryConsolidated.loc[Country].index,y=CountryConsolidated.loc[Country,'Death to Cases Ratio'],\n",
    "                         mode='lines+markers',\n",
    "                         name='Death to Cases Ratio',\n",
    "                         line=dict(color=chartcol,width=2)),\n",
    "                         row=3,col=1)\n",
    "    fig.update_layout(showlegend=False)\n",
    "    \n",
    "    return fig\n",
    "CountriesList=['Germany','Italy','US','Australia','Pakistan','Iran','South Korea','Japan','Netherlands']\n",
    "interact(plotcountry, Country=widgets.Dropdown(options=CountriesList))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cumulative Data by Country"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "max_index=CountryConsolidated.reset_index()['Date'].max()\n",
    "CumulativeCountryData=CountryConsolidated.reset_index().set_index('Date').loc[max_index].set_index('Country/Region')\n",
    "top10countriesbycases=CumulativeCountryData['Total Confirmed Cases'].sort_values(ascending=False).head(10).index.tolist()\n",
    "top10countriesbycasesexclChina=CumulativeCountryData[~CumulativeCountryData.index.isin(['Mainland China','Others'])]['Total Confirmed Cases'].sort_values(ascending=False).head(10).index.tolist()\n",
    "top10countriescumulative=CumulativeCountryData.loc[top10countriesbycases]\n",
    "top10countriescumulativexclChina=CumulativeCountryData.loc[top10countriesbycasesexclChina]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>Total Confirmed Cases</th>\n",
       "      <th>Daily New Cases</th>\n",
       "      <th>Daily New Deaths</th>\n",
       "      <th>Total Deaths</th>\n",
       "      <th>Total Recoveries</th>\n",
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       "      <th>Death to Cases Ratio</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country/Region</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <td>Afghanistan</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>Albania</td>\n",
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       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>Algeria</td>\n",
       "      <td>20</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>Andorra</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>12</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>11</td>\n",
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       "      <td>US</td>\n",
       "      <td>605</td>\n",
       "      <td>68.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>22</td>\n",
       "      <td>8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>575</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.036</td>\n",
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       "    <tr>\n",
       "      <td>Ukraine</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>38</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.000</td>\n",
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       "    <tr>\n",
       "      <td>Vatican City</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
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       "<p>111 rows × 9 columns</p>\n",
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       "                      Total Confirmed Cases  Daily New Cases  \\\n",
       "Country/Region                                                 \n",
       "Afghanistan                               4              0.0   \n",
       "Albania                                   2              2.0   \n",
       "Algeria                                  20              1.0   \n",
       "Andorra                                   1              0.0   \n",
       "Argentina                                12              0.0   \n",
       "...                                     ...              ...   \n",
       "US                                      605             68.0   \n",
       "Ukraine                                   1              0.0   \n",
       "United Arab Emirates                     45              0.0   \n",
       "Vatican City                              1              0.0   \n",
       "Vietnam                                  30              0.0   \n",
       "\n",
       "                      Daily New Deaths  Total Deaths  Total Recoveries  \\\n",
       "Country/Region                                                           \n",
       "Afghanistan                        0.0             0                 0   \n",
       "Albania                            0.0             0                 0   \n",
       "Algeria                            0.0             0                 0   \n",
       "Andorra                            0.0             0                 0   \n",
       "Argentina                          0.0             1                 0   \n",
       "...                                ...           ...               ...   \n",
       "US                                 1.0            22                 8   \n",
       "Ukraine                            0.0             0                 0   \n",
       "United Arab Emirates               0.0             0                 7   \n",
       "Vatican City                       0.0             0                 0   \n",
       "Vietnam                            0.0             0                16   \n",
       "\n",
       "                      Daily New Recoveries  Active Cases  \\\n",
       "Country/Region                                             \n",
       "Afghanistan                            0.0             4   \n",
       "Albania                                0.0             2   \n",
       "Algeria                                0.0            20   \n",
       "Andorra                                0.0             1   \n",
       "Argentina                              0.0            11   \n",
       "...                                    ...           ...   \n",
       "US                                     0.0           575   \n",
       "Ukraine                                0.0             1   \n",
       "United Arab Emirates                   0.0            38   \n",
       "Vatican City                           0.0             1   \n",
       "Vietnam                                0.0            14   \n",
       "\n",
       "                      Share of Recoveries - Closed Cases  Death to Cases Ratio  \n",
       "Country/Region                                                                  \n",
       "Afghanistan                                          NaN                 0.000  \n",
       "Albania                                              NaN                 0.000  \n",
       "Algeria                                              NaN                 0.000  \n",
       "Andorra                                              NaN                 0.000  \n",
       "Argentina                                           0.00                 0.083  \n",
       "...                                                  ...                   ...  \n",
       "US                                                  0.27                 0.036  \n",
       "Ukraine                                              NaN                 0.000  \n",
       "United Arab Emirates                                1.00                 0.000  \n",
       "Vatican City                                         NaN                 0.000  \n",
       "Vietnam                                             1.00                 0.000  \n",
       "\n",
       "[111 rows x 9 columns]"
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       "                        {\"responsive\": true}\n",
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       "\n",
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       "var notebookContainer = gd.closest('#notebook-container');\n",
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       "            </script>\n",
       "        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = go.Figure(go.Bar(x=top10countriescumulativexclChina.index, y=top10countriescumulativexclChina['Total Confirmed Cases'],\n",
    "                      text=top10countriescumulativexclChina['Death to Cases Ratio'],\n",
    "            textposition='outside'))\n",
    "fig.update_layout(title_text='Top countries by Cases and their Death Rates - Excluding China',yaxis_title='Total Cases')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Total Cases by number of days since 'Outbreak'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "ItalyFirstCase=CountryConsolidated.loc['Italy']['Total Confirmed Cases'].reset_index().set_index('Date')\n",
    "SKFirstCase=CountryConsolidated.loc['South Korea']['Total Confirmed Cases'].reset_index().set_index('Date')\n",
    "IranFirstCase=CountryConsolidated.loc['Iran']['Total Confirmed Cases'].reset_index().set_index('Date')\n",
    "GermanyFirstCase=CountryConsolidated.loc['Germany']['Total Confirmed Cases'].reset_index().set_index('Date')\n",
    "SingaporeFirstCase=CountryConsolidated.loc['Singapore']['Total Confirmed Cases'].reset_index().set_index('Date')\n",
    "\n",
    "ItalyGrowth=ItalyFirstCase[ItalyFirstCase.ne(0)].dropna().reset_index()\n",
    "SKGrowth=SKFirstCase[SKFirstCase.ne(0)].dropna().reset_index()\n",
    "IranGrowth=IranFirstCase[IranFirstCase.ne(0)].dropna().reset_index()\n",
    "GermanyGrowth=GermanyFirstCase[GermanyFirstCase.ne(0)].dropna().reset_index()\n",
    "SingaporeGrowth=SingaporeFirstCase[SingaporeFirstCase.ne(0)].dropna().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Date</th>\n",
       "      <th>Total Confirmed Cases</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>2020-01-31</td>\n",
       "      <td>2.0</td>\n",
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       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2020-02-01</td>\n",
       "      <td>2.0</td>\n",
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       "      <td>2020-02-02</td>\n",
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       "      <td>3</td>\n",
       "      <td>2020-02-03</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>2020-02-04</td>\n",
       "      <td>2.0</td>\n",
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       "        Date  Total Confirmed Cases\n",
       "0 2020-01-31                    2.0\n",
       "1 2020-02-01                    2.0\n",
       "2 2020-02-02                    2.0\n",
       "3 2020-02-03                    2.0\n",
       "4 2020-02-04                    2.0"
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\"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"yaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"zaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}}, \"shapedefaults\": {\"line\": {\"color\": \"#2a3f5f\"}}, \"ternary\": {\"aaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"baxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"caxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"title\": {\"text\": \"Active Cases\"}, \"xaxis\": {\"anchor\": \"y\", \"domain\": [0.0, 0.45], \"matches\": \"x3\", \"showticklabels\": false}, \"xaxis2\": {\"anchor\": \"y2\", \"domain\": [0.55, 1.0], \"matches\": \"x4\", \"showticklabels\": false}, \"xaxis3\": {\"anchor\": \"y3\", \"domain\": [0.0, 0.45], \"title\": {\"text\": \"Number of Days since Outbreak\"}}, \"xaxis4\": {\"anchor\": \"y4\", \"domain\": [0.55, 1.0], \"title\": {\"text\": \"Number of Days since Outbreak\"}}, \"yaxis\": {\"anchor\": \"x\", \"domain\": [0.625, 1.0]}, \"yaxis2\": {\"anchor\": \"x2\", \"domain\": [0.625, 1.0]}, \"yaxis3\": {\"anchor\": \"x3\", \"domain\": [0.0, 0.375]}, \"yaxis4\": {\"anchor\": \"x4\", \"domain\": [0.0, 0.375]}},\n",
       "                        {\"responsive\": true}\n",
       "                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('6311b84b-09cb-4d9e-a4cc-ccca61fd5568');\n",
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       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
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       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
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       "    x.observe(notebookContainer, {childList: true});\n",
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       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
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       "\n",
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       "                });\n",
       "            </script>\n",
       "        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = make_subplots(rows=2, cols=2,shared_xaxes=True,\n",
    "                   subplot_titles=('Italy','Iran','South Korea','Germany'))\n",
    "\n",
    "fig.update_xaxes(title_text=\"Number of Days since Outbreak\", row=2, col=1)\n",
    "\n",
    "fig.update_xaxes(title_text=\"Number of Days since Outbreak\", row=2, col=2)\n",
    "\n",
    "fig.add_trace(go.Scatter(x=ItalyGrowth.index,y=ItalyGrowth['Total Confirmed Cases'],\n",
    "                         mode='lines+markers',\n",
    "                         name='Active Cases',\n",
    "                         line=dict(color=chartcol,width=2)),\n",
    "                          row=1,col=1)\n",
    "fig.add_trace(go.Scatter(x=IranGrowth.index,y=IranGrowth['Total Confirmed Cases'],\n",
    "                         mode='lines+markers',\n",
    "                         name='Active Cases',\n",
    "                         line=dict(color=chartcol,width=2)),\n",
    "                          row=1,col=2)\n",
    "                           \n",
    "fig.add_trace(go.Scatter(x=SKGrowth.index,y=SKGrowth['Total Confirmed Cases'],\n",
    "                         mode='lines+markers',\n",
    "                         name='Active Cases',\n",
    "                         line=dict(color=chartcol,width=2)),\n",
    "                          row=2,col=1)    \n",
    "                           \n",
    "fig.add_trace(go.Scatter(x=GermanyGrowth.index,y=GermanyGrowth['Total Confirmed Cases'],\n",
    "                         mode='lines+markers',\n",
    "                         name='Active Cases',\n",
    "                         line=dict(color=chartcol,width=2)),\n",
    "                          row=2,col=2)  \n",
    "\n",
    "fig.update_layout(showlegend=False,title = 'Active Cases')"
   ]
  }
 ],
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