covid19.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import sklearn\n",
    "import math\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "\n",
    "url = 'https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_19-covid-Confirmed.csv'\n",
    "dataset = pd.read_csv(url)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>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>2/21/20</th>\n",
       "      <th>2/22/20</th>\n",
       "      <th>2/23/20</th>\n",
       "      <th>2/24/20</th>\n",
       "      <th>2/25/20</th>\n",
       "      <th>2/26/20</th>\n",
       "      <th>2/27/20</th>\n",
       "      <th>2/28/20</th>\n",
       "      <th>2/29/20</th>\n",
       "      <th>3/1/20</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>988</td>\n",
       "      <td>989</td>\n",
       "      <td>989</td>\n",
       "      <td>989</td>\n",
       "      <td>989</td>\n",
       "      <td>989</td>\n",
       "      <td>989</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>396</td>\n",
       "      <td>399</td>\n",
       "      <td>399</td>\n",
       "      <td>399</td>\n",
       "      <td>400</td>\n",
       "      <td>400</td>\n",
       "      <td>410</td>\n",
       "      <td>410</td>\n",
       "      <td>411</td>\n",
       "      <td>413</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>572</td>\n",
       "      <td>573</td>\n",
       "      <td>575</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>293</td>\n",
       "      <td>293</td>\n",
       "      <td>293</td>\n",
       "      <td>293</td>\n",
       "      <td>294</td>\n",
       "      <td>294</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>91</td>\n",
       "      <td>91</td>\n",
       "      <td>91</td>\n",
       "      <td>91</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 44 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  ...  2/21/20  2/22/20  2/23/20  \\\n",
       "0       15       39       60       70  ...      988      989      989   \n",
       "1       36       41       68       80  ...      396      399      399   \n",
       "2       27       57       75      110  ...      572      573      575   \n",
       "3       10       18       35       59  ...      293      293      293   \n",
       "4        2        4        7       14  ...       91       91       91   \n",
       "\n",
       "   2/24/20  2/25/20  2/26/20  2/27/20  2/28/20  2/29/20  3/1/20  \n",
       "0      989      989      989      989      990      990     990  \n",
       "1      399      400      400      410      410      411     413  \n",
       "2      576      576      576      576      576      576     576  \n",
       "3      293      294      294      296      296      296     296  \n",
       "4       91       91       91       91       91       91      91  \n",
       "\n",
       "[5 rows x 44 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1ac8cfd0>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x = 'Country/Region', y='3/1/20', hue='1/22/20', sizes=(20, 200),\n",
    "                     legend=\"full\", data=dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}