COVID_19-DATA-ANALYSIS/Covid_19_Data_Analysis(SUYASH).ipynb

{
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    "id": "kXQR7ZpCsYBP",
    "outputId": "02f33c4e-76d4-4d28-9821-1982c17f26a2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: plotly in c:\\users\\admin\\appdata\\local\\programs\\python\\python38\\lib\\site-packages (4.12.0)\n",
      "Requirement already satisfied: six in c:\\users\\admin\\appdata\\local\\programs\\python\\python38\\lib\\site-packages (from plotly) (1.14.0)\n",
      "Requirement already satisfied: retrying>=1.3.3 in c:\\users\\admin\\appdata\\local\\programs\\python\\python38\\lib\\site-packages (from plotly) (1.3.3)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: You are using pip version 20.2.1; however, version 20.2.4 is available.\n",
      "You should consider upgrading via the 'c:\\users\\admin\\appdata\\local\\programs\\python\\python38\\python.exe -m pip install --upgrade pip' command.\n"
     ]
    }
   ],
   "source": [
    "!pip install plotly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-latest.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import plotly.offline as py\n",
    "py.init_notebook_mode(connected = True)\n",
    "import plotly.graph_objs as go\n",
    "import plotly.express as px\n",
    "from plotly.subplots import make_subplots\n",
    "import plotly.io as pio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "2mD0OCHksYBY"
   },
   "outputs": [],
   "source": [
    "covid_data = pd.read_csv(r'C:\\Users\\Admin\\Desktop\\covid.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "gygDJvVfsYBd",
    "outputId": "d426bf76-a5fe-4038-8e34-8b5479280ce7"
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   "outputs": [
    {
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       "      <th>NewCases</th>\n",
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       "      <th>1</th>\n",
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       "      <td>98644.0</td>\n",
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       "      <td>8318.0</td>\n",
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       "      <td>464.0</td>\n",
       "      <td>13206188.0</td>\n",
       "      <td>62085.0</td>\n",
       "      <td>Americas</td>\n",
       "      <td>BRA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>India</td>\n",
       "      <td>Asia</td>\n",
       "      <td>1.381345e+09</td>\n",
       "      <td>2025409</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41638.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1377384.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>606387.0</td>\n",
       "      <td>8944.0</td>\n",
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       "      <td>30.0</td>\n",
       "      <td>22149351.0</td>\n",
       "      <td>16035.0</td>\n",
       "      <td>South-EastAsia</td>\n",
       "      <td>IND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Russia</td>\n",
       "      <td>Europe</td>\n",
       "      <td>1.459409e+08</td>\n",
       "      <td>871894</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14606.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>676357.0</td>\n",
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       "      <td>180931.0</td>\n",
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       "      <td>100.0</td>\n",
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       "      <td>Europe</td>\n",
       "      <td>RUS</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>South Africa</td>\n",
       "      <td>Africa</td>\n",
       "      <td>5.938157e+07</td>\n",
       "      <td>538184</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9604.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>387316.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>141264.0</td>\n",
       "      <td>539.0</td>\n",
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       "      <td>53044.0</td>\n",
       "      <td>Africa</td>\n",
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       "  Country/Region      Continent    Population  TotalCases  NewCases  \\\n",
       "0            USA  North America  3.311981e+08     5032179       NaN   \n",
       "1         Brazil  South America  2.127107e+08     2917562       NaN   \n",
       "2          India           Asia  1.381345e+09     2025409       NaN   \n",
       "3         Russia         Europe  1.459409e+08      871894       NaN   \n",
       "4   South Africa         Africa  5.938157e+07      538184       NaN   \n",
       "\n",
       "   TotalDeaths  NewDeaths  TotalRecovered  NewRecovered  ActiveCases  \\\n",
       "0     162804.0        NaN       2576668.0           NaN    2292707.0   \n",
       "1      98644.0        NaN       2047660.0           NaN     771258.0   \n",
       "2      41638.0        NaN       1377384.0           NaN     606387.0   \n",
       "3      14606.0        NaN        676357.0           NaN     180931.0   \n",
       "4       9604.0        NaN        387316.0           NaN     141264.0   \n",
       "\n",
       "   Serious,Critical  Tot Cases/1M pop  Deaths/1M pop  TotalTests  \\\n",
       "0           18296.0           15194.0          492.0  63139605.0   \n",
       "1            8318.0           13716.0          464.0  13206188.0   \n",
       "2            8944.0            1466.0           30.0  22149351.0   \n",
       "3            2300.0            5974.0          100.0  29716907.0   \n",
       "4             539.0            9063.0          162.0   3149807.0   \n",
       "\n",
       "   Tests/1M pop      WHO Region iso_alpha  \n",
       "0      190640.0        Americas       USA  \n",
       "1       62085.0        Americas       BRA  \n",
       "2       16035.0  South-EastAsia       IND  \n",
       "3      203623.0          Europe       RUS  \n",
       "4       53044.0          Africa       ZAF  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "eRF_RT3UsYBh"
   },
   "outputs": [],
   "source": [
    "covid_data = covid_data.drop(['NewCases','NewDeaths','NewRecovered'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
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    "id": "6HAqD5gDsYBl",
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       "      <td>Americas</td>\n",
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       "    <tr>\n",
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       "      <td>1.381345e+09</td>\n",
       "      <td>2025409</td>\n",
       "      <td>41638.0</td>\n",
       "      <td>1377384.0</td>\n",
       "      <td>606387.0</td>\n",
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       "      <td>1466.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>22149351.0</td>\n",
       "      <td>16035.0</td>\n",
       "      <td>South-EastAsia</td>\n",
       "      <td>IND</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Russia</td>\n",
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       "      <td>1.459409e+08</td>\n",
       "      <td>871894</td>\n",
       "      <td>14606.0</td>\n",
       "      <td>676357.0</td>\n",
       "      <td>180931.0</td>\n",
       "      <td>2300.0</td>\n",
       "      <td>5974.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>29716907.0</td>\n",
       "      <td>203623.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>RUS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>South Africa</td>\n",
       "      <td>Africa</td>\n",
       "      <td>5.938157e+07</td>\n",
       "      <td>538184</td>\n",
       "      <td>9604.0</td>\n",
       "      <td>387316.0</td>\n",
       "      <td>141264.0</td>\n",
       "      <td>539.0</td>\n",
       "      <td>9063.0</td>\n",
       "      <td>162.0</td>\n",
       "      <td>3149807.0</td>\n",
       "      <td>53044.0</td>\n",
       "      <td>Africa</td>\n",
       "      <td>ZAF</td>\n",
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      "text/plain": [
       "  Country/Region      Continent    Population  TotalCases  TotalDeaths  \\\n",
       "0            USA  North America  3.311981e+08     5032179     162804.0   \n",
       "1         Brazil  South America  2.127107e+08     2917562      98644.0   \n",
       "2          India           Asia  1.381345e+09     2025409      41638.0   \n",
       "3         Russia         Europe  1.459409e+08      871894      14606.0   \n",
       "4   South Africa         Africa  5.938157e+07      538184       9604.0   \n",
       "\n",
       "   TotalRecovered  ActiveCases  Serious,Critical  Tot Cases/1M pop  \\\n",
       "0       2576668.0    2292707.0           18296.0           15194.0   \n",
       "1       2047660.0     771258.0            8318.0           13716.0   \n",
       "2       1377384.0     606387.0            8944.0            1466.0   \n",
       "3        676357.0     180931.0            2300.0            5974.0   \n",
       "4        387316.0     141264.0             539.0            9063.0   \n",
       "\n",
       "   Deaths/1M pop  TotalTests  Tests/1M pop      WHO Region iso_alpha  \n",
       "0          492.0  63139605.0      190640.0        Americas       USA  \n",
       "1          464.0  13206188.0       62085.0        Americas       BRA  \n",
       "2           30.0  22149351.0       16035.0  South-EastAsia       IND  \n",
       "3          100.0  29716907.0      203623.0          Europe       RUS  \n",
       "4          162.0   3149807.0       53044.0          Africa       ZAF  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "FIKd213_sYBp"
   },
   "outputs": [],
   "source": [
    "def trunc(x):\n",
    "    r = round(x,2)\n",
    "    return r\n",
    "\n",
    "def trunc1(x):\n",
    "    r = round(x,0)\n",
    "    return r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "DTqVNLk5sYBs"
   },
   "outputs": [],
   "source": [
    "covid_data['Percentage_affected'] = ((covid_data['TotalCases']/covid_data['Population'])*100).apply(trunc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "BAXQ_TuYsYBw"
   },
   "outputs": [],
   "source": [
    "covid_data['Recovery_percentage'] = ((covid_data['TotalRecovered']/covid_data['TotalCases'])*100).apply(trunc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "f-QWnQYjsYBz"
   },
   "outputs": [],
   "source": [
    "covid_data['Death_percentage'] = ((covid_data['TotalDeaths']/covid_data['TotalCases'])*100).apply(trunc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "zjbpYT7fsYB2"
   },
   "outputs": [],
   "source": [
    "covid_data['Serious_Percentage'] = ((covid_data['Serious,Critical']/covid_data['TotalCases'])*100).apply(trunc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "Bwu8AsuDsYB5",
    "outputId": "e90cbc2f-91ce-4dbc-fef4-6ca692b10840"
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   "outputs": [
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       "      <td>63139605.0</td>\n",
       "      <td>190640.0</td>\n",
       "      <td>Americas</td>\n",
       "      <td>USA</td>\n",
       "      <td>1.52</td>\n",
       "      <td>51.20</td>\n",
       "      <td>3.24</td>\n",
       "      <td>0.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Brazil</td>\n",
       "      <td>South America</td>\n",
       "      <td>2.127107e+08</td>\n",
       "      <td>2917562</td>\n",
       "      <td>98644.0</td>\n",
       "      <td>2047660.0</td>\n",
       "      <td>771258.0</td>\n",
       "      <td>8318.0</td>\n",
       "      <td>13716.0</td>\n",
       "      <td>464.0</td>\n",
       "      <td>13206188.0</td>\n",
       "      <td>62085.0</td>\n",
       "      <td>Americas</td>\n",
       "      <td>BRA</td>\n",
       "      <td>1.37</td>\n",
       "      <td>70.18</td>\n",
       "      <td>3.38</td>\n",
       "      <td>0.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>India</td>\n",
       "      <td>Asia</td>\n",
       "      <td>1.381345e+09</td>\n",
       "      <td>2025409</td>\n",
       "      <td>41638.0</td>\n",
       "      <td>1377384.0</td>\n",
       "      <td>606387.0</td>\n",
       "      <td>8944.0</td>\n",
       "      <td>1466.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>22149351.0</td>\n",
       "      <td>16035.0</td>\n",
       "      <td>South-EastAsia</td>\n",
       "      <td>IND</td>\n",
       "      <td>0.15</td>\n",
       "      <td>68.01</td>\n",
       "      <td>2.06</td>\n",
       "      <td>0.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Russia</td>\n",
       "      <td>Europe</td>\n",
       "      <td>1.459409e+08</td>\n",
       "      <td>871894</td>\n",
       "      <td>14606.0</td>\n",
       "      <td>676357.0</td>\n",
       "      <td>180931.0</td>\n",
       "      <td>2300.0</td>\n",
       "      <td>5974.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>29716907.0</td>\n",
       "      <td>203623.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>RUS</td>\n",
       "      <td>0.60</td>\n",
       "      <td>77.57</td>\n",
       "      <td>1.68</td>\n",
       "      <td>0.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>South Africa</td>\n",
       "      <td>Africa</td>\n",
       "      <td>5.938157e+07</td>\n",
       "      <td>538184</td>\n",
       "      <td>9604.0</td>\n",
       "      <td>387316.0</td>\n",
       "      <td>141264.0</td>\n",
       "      <td>539.0</td>\n",
       "      <td>9063.0</td>\n",
       "      <td>162.0</td>\n",
       "      <td>3149807.0</td>\n",
       "      <td>53044.0</td>\n",
       "      <td>Africa</td>\n",
       "      <td>ZAF</td>\n",
       "      <td>0.91</td>\n",
       "      <td>71.97</td>\n",
       "      <td>1.78</td>\n",
       "      <td>0.10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Country/Region      Continent    Population  TotalCases  TotalDeaths  \\\n",
       "0            USA  North America  3.311981e+08     5032179     162804.0   \n",
       "1         Brazil  South America  2.127107e+08     2917562      98644.0   \n",
       "2          India           Asia  1.381345e+09     2025409      41638.0   \n",
       "3         Russia         Europe  1.459409e+08      871894      14606.0   \n",
       "4   South Africa         Africa  5.938157e+07      538184       9604.0   \n",
       "\n",
       "   TotalRecovered  ActiveCases  Serious,Critical  Tot Cases/1M pop  \\\n",
       "0       2576668.0    2292707.0           18296.0           15194.0   \n",
       "1       2047660.0     771258.0            8318.0           13716.0   \n",
       "2       1377384.0     606387.0            8944.0            1466.0   \n",
       "3        676357.0     180931.0            2300.0            5974.0   \n",
       "4        387316.0     141264.0             539.0            9063.0   \n",
       "\n",
       "   Deaths/1M pop  TotalTests  Tests/1M pop      WHO Region iso_alpha  \\\n",
       "0          492.0  63139605.0      190640.0        Americas       USA   \n",
       "1          464.0  13206188.0       62085.0        Americas       BRA   \n",
       "2           30.0  22149351.0       16035.0  South-EastAsia       IND   \n",
       "3          100.0  29716907.0      203623.0          Europe       RUS   \n",
       "4          162.0   3149807.0       53044.0          Africa       ZAF   \n",
       "\n",
       "   Percentage_affected  Recovery_percentage  Death_percentage  \\\n",
       "0                 1.52                51.20              3.24   \n",
       "1                 1.37                70.18              3.38   \n",
       "2                 0.15                68.01              2.06   \n",
       "3                 0.60                77.57              1.68   \n",
       "4                 0.91                71.97              1.78   \n",
       "\n",
       "   Serious_Percentage  \n",
       "0                0.36  \n",
       "1                0.29  \n",
       "2                0.44  \n",
       "3                0.26  \n",
       "4                0.10  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid_data.head()"
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  },
  {
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    "id": "RVicYQvWsYB8",
    "outputId": "3ecf04fe-856e-4a05-d538-8e5aaca4a903"
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from plotly.figure_factory import create_table\n",
    "table = create_table(covid_data.head(10),colorscale = [[0, '#4d004c'],[.5, '#f2e5ff'],[1, '#ffffff']])\n",
    "table.layout.width=2800\n",
    "py.iplot(table)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "T7vW2EWJsYCB"
   },
   "source": [
    "### Population of top 15 countries with most covid-cases "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "4bN_hj3ssYCC",
    "outputId": "f3236332-75d6-4bb9-cbac-0bd5112bdfed"
   },
   "outputs": [
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           "Russia",
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           "South Africa",
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           "Spain",
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           "UK",
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           "Saudi Arabia",
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           "Pakistan",
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     },
     "metadata": {},
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   ],
   "source": [
    "px.bar(covid_data.head(15),x='Country/Region',y='Population',color = 'Population',\n",
    "       hover_data = ['Country/Region','Continent'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "cGM554YmsYCG"
   },
   "source": [
    "### TotalCases per Population percentage of top 10 countries with most covid_cases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "Dm4GerOGsYCH"
   },
   "outputs": [],
   "source": [
    "fig = go.Figure([go.Pie(labels=covid_data.head(10)['Country/Region'], values=covid_data.head(10)['Percentage_affected'])])"
   ]
  },
  {
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    "outputId": "5e6ea892-b6ab-4d44-f207-d4dc287c05da"
   },
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       "            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",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
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       "                        })                };                });            </script>        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = go.Figure([go.Pie(labels=covid_data.head(10)['Country/Region'], values=covid_data.head(10)['Serious_Percentage'])])\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "id": "elmJTf_ksYCe"
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   "outputs": [],
   "source": [
    "continent_group = covid_data.groupby('Continent')['TotalCases'].sum().reset_index()"
   ]
  },
  {
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   "execution_count": 20,
   "metadata": {
    "id": "Y5DJsePtsYCi",
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   },
   "outputs": [
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       "           Continent  TotalCases\n",
       "0             Africa     1011867\n",
       "1               Asia     4689794\n",
       "2  Australia/Oceania       21735\n",
       "3             Europe     2982576\n",
       "4      North America     5919209\n",
       "5      South America     4543273"
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   "source": [
    "### Total Number of Cases in each continent"
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "px.bar(covid_data.head(15),x='Country/Region',y='TotalCases',color = 'Country/Region',\n",
    "       hover_data = ['Country/Region','Continent'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "id": "tm0LrPfVsYDC",
    "outputId": "8b84b643-96f6-4fad-af89-e3cbac92e072"
   },
   "outputs": [
    {
     "data": {
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   ],
   "source": [
    "px.bar(covid_data.head(15),x='Country/Region',y='TotalDeaths',color = 'Country/Region',\n",
    "       hover_data = ['Country/Region','Continent'])"
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    "outputId": "7696e087-29d6-457b-8b1d-bb39e903b477"
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     "metadata": {},
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   ],
   "source": [
    "px.bar(covid_data.head(15),x='Country/Region',y='TotalRecovered',color = 'Country/Region',\n",
    "       hover_data = ['Country/Region','Continent'])"
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  },
  {
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   "execution_count": 25,
   "metadata": {
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    "outputId": "bc0758ef-41d8-4684-f038-58f65bcbc46a"
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   "outputs": [
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     "metadata": {},
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   ],
   "source": [
    "fig = make_subplots(rows=1, cols=3,subplot_titles=(\"Total Cases\", \"Total Deaths\", \"Total Recovered\"),column_widths=[1, 1,1])\n",
    "fig.add_trace(go.Box(y=covid_data['TotalCases'],name=\"Total Cases\"),row=1, col=1)\n",
    "fig.add_trace(go.Box(y=covid_data['TotalDeaths'],name=\"Total Deaths\"),row=1, col=2)\n",
    "fig.add_trace(go.Box(y=covid_data['TotalRecovered'],name=\"Total Recovered\"),row=1, col=3)\n",
    "fig.show()"
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  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SSUBky1hsYDc"
   },
   "source": [
    "# THANK YOU"
   ]
  }
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