Data analysis on Coronavirus outbreak.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Analysis on Coronavirus Outbreak\n",
    "\n",
    "## __Wuhan Coronavirus__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What is coronavirus?\n",
    "\n",
    "The coronavirus is a family of viruses that can cause a range of illnesses in humans including common cold and more severe forms like SARS and MERS which are life-threatening. The virus is named after its shape which takes the form of a crown with protrusions around it and hence is known as coronavirus."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": 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      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"600\"\n",
       "            height=\"400\"\n",
       "            src=\"https://www.youtube.com/embed/mOV1aBVYKGA\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.YouTubeVideo at 0x7f31147c6410>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import YouTubeVideo\n",
    "YouTubeVideo('mOV1aBVYKGA', width=600, height=400)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### How did the recent outbreak occur?\n",
    "The recent outbreak of coronavirus is believed to have occurred in a market for illegal wildlife in the central Chinese city of Wuhan. Chinese health authorities and the WHO are investigating the outbreak of the recent coronavirus which has claimed thousands of lives."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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",
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       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# import the necessary libraries\n",
    "import numpy as np \n",
    "import pandas as pd \n",
    "\n",
    "# Visualisation libraries\n",
    "import matplotlib.pyplot as plt\n",
    "from pprint import pprint\n",
    "import seaborn as sns\n",
    "sns.set_style(\"whitegrid\")\n",
    "%matplotlib inline\n",
    "\n",
    "'''Plotly visualization .'''\n",
    "import plotly.offline as py\n",
    "from plotly.offline import iplot, init_notebook_mode\n",
    "import plotly.graph_objs as go\n",
    "py.init_notebook_mode(connected = True) # Required to use plotly offline in jupyter notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 73 entries, 0 to 72\n",
      "Data columns (total 6 columns):\n",
      "Province/State    52 non-null object\n",
      "Country/Region    73 non-null object\n",
      "Last Update       73 non-null object\n",
      "Confirmed         73 non-null int64\n",
      "Deaths            73 non-null int64\n",
      "Recovered         73 non-null int64\n",
      "dtypes: int64(3), object(3)\n",
      "memory usage: 3.5+ KB\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv(\"corona_geral.csv\")\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### GLOBALY"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>Confirmed</th>\n",
       "      <th>Deaths</th>\n",
       "      <th>Recovered</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Australia</th>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belgium</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cambodia</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Canada</th>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>China</th>\n",
       "      <td>44687</td>\n",
       "      <td>1115</td>\n",
       "      <td>5062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Finland</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hong Kong</th>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>India</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Italy</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Japan</th>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Macau</th>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Malaysia</th>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nepal</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Others</th>\n",
       "      <td>175</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Philippines</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Russia</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Singapore</th>\n",
       "      <td>47</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South Korea</th>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spain</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sri Lanka</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sweden</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Taiwan</th>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thailand</th>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UK</th>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>US</th>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Arab Emirates</th>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vietnam</th>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Confirmed  Deaths  Recovered\n",
       "Country                                           \n",
       "Australia                    15       0          2\n",
       "Belgium                       1       0          0\n",
       "Cambodia                      1       0          1\n",
       "Canada                        7       0          0\n",
       "China                     44687    1115       5062\n",
       "Finland                       1       0          1\n",
       "France                       11       0          0\n",
       "Germany                      16       0          0\n",
       "Hong Kong                    50       1          1\n",
       "India                         3       0          0\n",
       "Italy                         3       0          0\n",
       "Japan                        28       0          9\n",
       "Macau                        10       0          2\n",
       "Malaysia                     18       0          3\n",
       "Nepal                         1       0          1\n",
       "Others                      175       0          0\n",
       "Philippines                   3       1          1\n",
       "Russia                        2       0          2\n",
       "Singapore                    47       0          9\n",
       "South Korea                  28       0          7\n",
       "Spain                         2       0          0\n",
       "Sri Lanka                     1       0          1\n",
       "Sweden                        1       0          0\n",
       "Taiwan                       18       0          1\n",
       "Thailand                     33       0         10\n",
       "UK                            8       0          0\n",
       "US                           13       0          3\n",
       "United Arab Emirates          8       0          1\n",
       "Vietnam                      15       0          6"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.rename({'Country/Region': 'Country'}, axis='columns', inplace=True)\n",
    "df['Country'].replace({'Mainland China':'China'},inplace=True)\n",
    "\n",
    "countries = pd.DataFrame(df.groupby('Country')['Country','Confirmed','Deaths', 'Recovered'].sum())\n",
    "countries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "coordinates = pd.read_csv('coordinates.csv')\n",
    "\n",
    "countries['Country'] = countries.index\n",
    "countries.index=np.arange(1,len(countries)+1)\n",
    "countries = pd.merge(coordinates,countries,on='Country')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
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\"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\": \"<b>Countries with Confirmed, Deaths and Recovered cases of 2019-nCoV\"}},\n",
       "                        {\"responsive\": true}\n",
       "                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('6148ce08-d738-408a-8b1b-ecd3fb5d809d');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
       "        var display = window.getComputedStyle(gd).display;\n",
       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\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",
       "}}\n",
       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
       "}}\n",
       "\n",
       "                        })\n",
       "                };\n",
       "                });\n",
       "            </script>\n",
       "        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "fig = go.Figure()\n",
    "fig.add_trace(go.Scattergeo(\n",
    "        lat=countries['latitude'],\n",
    "        lon=countries['longitude'],\n",
    "        mode=\"markers\",\n",
    "        marker=dict(\n",
    "            size=17,\n",
    "            color='rgb(255, 0, 0)',\n",
    "            opacity=0.7\n",
    "        ),\n",
    "        text=countries['Country'],\n",
    "        hoverinfo=\"text\",\n",
    "    ))\n",
    "\n",
    "fig.add_trace(go.Scattergeo(\n",
    "        lat=countries['latitude'],\n",
    "        lon=countries['longitude'],\n",
    "        mode='markers',\n",
    "        marker=dict(\n",
    "            size=8,\n",
    "            color='rgb(242, 177, 172)',\n",
    "            opacity=0.7\n",
    "        ),\n",
    "        hoverinfo='none'\n",
    "    ))\n",
    "\n",
    "fig.update_layout(\n",
    "        autosize=True,\n",
    "        hovermode='closest',\n",
    "        showlegend=False,\n",
    "        title_text = '<b>Countries with Confirmed, Deaths and Recovered cases of 2019-nCoV',\n",
    "        font=dict(family=\"Courier New\", color='darkred'),\n",
    "    geo = go.layout.Geo(\n",
    "        showframe = False,\n",
    "        showcoastlines = False,\n",
    "        showcountries = True,\n",
    "        landcolor = \"rgb(229, 229, 229)\",\n",
    "        countrycolor = \"black\",\n",
    "        coastlinecolor = \"black\",\n",
    "        projection_type=\"natural earth\"\n",
    "    ))\n",
    "\n",
    " \n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/lib/python3.7/site-packages/ipykernel_launcher.py:2: UserWarning:\n",
      "\n",
      "Boolean Series key will be reindexed to match DataFrame index.\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "others = countries[countries.Country != 'China']\n",
    "others = others[countries.Country != 'Others']\n",
    "\n",
    "f, ax = plt.subplots(figsize=(15, 10))\n",
    "\n",
    "\n",
    "sns.barplot(x=\"Confirmed\", y=\"Country\", data=others,\n",
    "            label=\"Infected\", color=\"orange\",alpha=0.7)\n",
    "\n",
    "\n",
    "sns.barplot(x=\"Recovered\", y=\"Country\", data=others,\n",
    "            label=\"Recovered\", color=\"g\",alpha=0.7)\n",
    "\n",
    "\n",
    "ax.set_title('Confirmed vs Recovered comparison of Countries other than China', fontsize=20, fontweight='bold', position=(0.40, 1.05))\n",
    "ax.legend(ncol=2, loc=\"lower right\", frameon=True)\n",
    "ax.set(xlim=(0, 55), ylabel=\"\",\n",
    "       xlabel=\"People\")\n",
    "sns.despine(left=True, bottom=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Overall, there are 45206 people affected, 5123 recovered and 1117 deaths.\n"
     ]
    }
   ],
   "source": [
    "print(\"\\nOverall, there are\",df['Confirmed'].sum(),\"people affected,\", \n",
    "      df['Recovered'].sum(),\"recovered and\", \n",
    "      df['Deaths'].sum(),\"deaths.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CHINA SCENARIO"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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