coronavirus-united-states-visualizations-ver1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<H3>Learning acknowledgement </H3> <p>https://www.kaggle.com/imdevskp/covid-19-analysis-viz-prediction-comparisons <br></p>\n",
    "Its a very basic data visualization done during the process of learning Pandas and Data Science. I hope to improve over time! "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/kaggle/input/corona-virus-report/covid_19_clean_complete.csv\n"
     ]
    }
   ],
   "source": [
    "# This Python 3 environment comes with many helpful analytics libraries installed\n",
    "# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n",
    "# For example, here's several helpful packages to load in \n",
    "\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "import matplotlib.pyplot as plt\n",
    "import plotly.express as px\n",
    "import plotly.graph_objs as go\n",
    "import plotly.figure_factory as ff\n",
    "\n",
    "# Input data files are available in the \"../input/\" directory.\n",
    "# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
    "\n",
    "import os\n",
    "for dirname, _, filenames in os.walk('/kaggle/input'):\n",
    "    for filename in filenames:\n",
    "        print(os.path.join(dirname, filename))\n",
    "\n",
    "# Any results you write to the current directory are saved as output."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<H2> Reading the dataset </H2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
    "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a"
   },
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\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>Date</th>\n",
       "      <th>Confirmed</th>\n",
       "      <th>Deaths</th>\n",
       "      <th>Recovered</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NA</td>\n",
       "      <td>Thailand</td>\n",
       "      <td>15.0000</td>\n",
       "      <td>101.0000</td>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NA</td>\n",
       "      <td>Japan</td>\n",
       "      <td>36.0000</td>\n",
       "      <td>138.0000</td>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NA</td>\n",
       "      <td>Singapore</td>\n",
       "      <td>1.2833</td>\n",
       "      <td>103.8333</td>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NA</td>\n",
       "      <td>Nepal</td>\n",
       "      <td>28.1667</td>\n",
       "      <td>84.2500</td>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NA</td>\n",
       "      <td>Malaysia</td>\n",
       "      <td>2.5000</td>\n",
       "      <td>112.5000</td>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>British Columbia</td>\n",
       "      <td>Canada</td>\n",
       "      <td>49.2827</td>\n",
       "      <td>-123.1207</td>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>New South Wales</td>\n",
       "      <td>Australia</td>\n",
       "      <td>-33.8688</td>\n",
       "      <td>151.2093</td>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Victoria</td>\n",
       "      <td>Australia</td>\n",
       "      <td>-37.8136</td>\n",
       "      <td>144.9631</td>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Queensland</td>\n",
       "      <td>Australia</td>\n",
       "      <td>-28.0167</td>\n",
       "      <td>153.4000</td>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>NA</td>\n",
       "      <td>Cambodia</td>\n",
       "      <td>11.5500</td>\n",
       "      <td>104.9167</td>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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      ],
      "text/plain": [
       "     Province/State Country/Region      Lat      Long       Date  Confirmed  \\\n",
       "0                NA       Thailand  15.0000  101.0000 2020-01-22          2   \n",
       "1                NA          Japan  36.0000  138.0000 2020-01-22          2   \n",
       "2                NA      Singapore   1.2833  103.8333 2020-01-22          0   \n",
       "3                NA          Nepal  28.1667   84.2500 2020-01-22          0   \n",
       "4                NA       Malaysia   2.5000  112.5000 2020-01-22          0   \n",
       "5  British Columbia         Canada  49.2827 -123.1207 2020-01-22          0   \n",
       "6   New South Wales      Australia -33.8688  151.2093 2020-01-22          0   \n",
       "7          Victoria      Australia -37.8136  144.9631 2020-01-22          0   \n",
       "8        Queensland      Australia -28.0167  153.4000 2020-01-22          0   \n",
       "9                NA       Cambodia  11.5500  104.9167 2020-01-22          0   \n",
       "\n",
       "   Deaths  Recovered  \n",
       "0       0          0  \n",
       "1       0          0  \n",
       "2       0          0  \n",
       "3       0          0  \n",
       "4       0          0  \n",
       "5       0          0  \n",
       "6       0          0  \n",
       "7       0          0  \n",
       "8       0          0  \n",
       "9       0          0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = pd.read_csv(\"/kaggle/input/corona-virus-report/covid_19_clean_complete.csv\", parse_dates=['Date'])\n",
    "dataset.replace(np.nan, \"NA\", inplace=True)\n",
    "dataset.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<H2> Adding additional coloumn for active state</H2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>Province/State</th>\n",
       "      <th>Country/Region</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Long</th>\n",
       "      <th>Date</th>\n",
       "      <th>Confirmed</th>\n",
       "      <th>Deaths</th>\n",
       "      <th>Recovered</th>\n",
       "      <th>Active</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NA</td>\n",
       "      <td>Thailand</td>\n",
       "      <td>15.0000</td>\n",
       "      <td>101.0000</td>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NA</td>\n",
       "      <td>Japan</td>\n",
       "      <td>36.0000</td>\n",
       "      <td>138.0000</td>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NA</td>\n",
       "      <td>Singapore</td>\n",
       "      <td>1.2833</td>\n",
       "      <td>103.8333</td>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NA</td>\n",
       "      <td>Nepal</td>\n",
       "      <td>28.1667</td>\n",
       "      <td>84.2500</td>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NA</td>\n",
       "      <td>Malaysia</td>\n",
       "      <td>2.5000</td>\n",
       "      <td>112.5000</td>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>British Columbia</td>\n",
       "      <td>Canada</td>\n",
       "      <td>49.2827</td>\n",
       "      <td>-123.1207</td>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>New South Wales</td>\n",
       "      <td>Australia</td>\n",
       "      <td>-33.8688</td>\n",
       "      <td>151.2093</td>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Victoria</td>\n",
       "      <td>Australia</td>\n",
       "      <td>-37.8136</td>\n",
       "      <td>144.9631</td>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Queensland</td>\n",
       "      <td>Australia</td>\n",
       "      <td>-28.0167</td>\n",
       "      <td>153.4000</td>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>NA</td>\n",
       "      <td>Cambodia</td>\n",
       "      <td>11.5500</td>\n",
       "      <td>104.9167</td>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Province/State Country/Region      Lat      Long       Date  Confirmed  \\\n",
       "0                NA       Thailand  15.0000  101.0000 2020-01-22          2   \n",
       "1                NA          Japan  36.0000  138.0000 2020-01-22          2   \n",
       "2                NA      Singapore   1.2833  103.8333 2020-01-22          0   \n",
       "3                NA          Nepal  28.1667   84.2500 2020-01-22          0   \n",
       "4                NA       Malaysia   2.5000  112.5000 2020-01-22          0   \n",
       "5  British Columbia         Canada  49.2827 -123.1207 2020-01-22          0   \n",
       "6   New South Wales      Australia -33.8688  151.2093 2020-01-22          0   \n",
       "7          Victoria      Australia -37.8136  144.9631 2020-01-22          0   \n",
       "8        Queensland      Australia -28.0167  153.4000 2020-01-22          0   \n",
       "9                NA       Cambodia  11.5500  104.9167 2020-01-22          0   \n",
       "\n",
       "   Deaths  Recovered  Active  \n",
       "0       0          0       2  \n",
       "1       0          0       2  \n",
       "2       0          0       0  \n",
       "3       0          0       0  \n",
       "4       0          0       0  \n",
       "5       0          0       0  \n",
       "6       0          0       0  \n",
       "7       0          0       0  \n",
       "8       0          0       0  \n",
       "9       0          0       0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset['Active'] = dataset['Confirmed'] - dataset['Deaths'] - dataset['Recovered']\n",
    "dataset.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<H2>Visualizing the crisis Country wise </H2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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oQx1rE890jljihEQ5U1ibOFK1eltVf6i+U6DWtg6yqrRG1WW18lY0yuz2z0BLbHRHqNaY6FBjkkP2MfFKnJSk0Re4NT4jWRmj4jtWQY5NdCqKNZAAAAAAAGCEIDjrhuBsCAuFpIqK0wO1k68HD0qBTn9BbxjS+PFnDtbS0yX7yJ2eME1Tc56fo+bWZu26cZdsUUwLIDICvkDHpNtZA7juU2+dvjdDZ//fIJvDdsZz3c4WutldDjXbo3QsGNThet+ABWuNvsCps8ROhmLdzhI70eiTAiHFNrYqvqFVsY1+JTYFlNoaUrI3oLjG9imx6haZPaxNjEmKOeeUWMLYBNYm4qx6XAdZVqvj+2v+f3t3HmfJXdf7//WpOlv3dE8nM5mESULCosaLEKIsyvWKQZGruLCqiD+Eq4L4+7ngvW6P60PFDRUXlE2F/JBFQWSV7RqUPYoQCJAECLIkgawzmZl09/Ry+pyqz/3j+60+dU7XOb1Mz/Sc7vczOXPqfOtbVd+uvepT329x/NZ7WbxtnnyxvznIbi3pNQM502B5pknjwmn2XzLDoQecw8Vfd5BLz59abQpyRjUVRURERERERGSMKHA2QIGzMZZlcPvtw9+v9rWvheBbIUng4ouHB9YuughqZ0+TbVvxls+9hae+6am8/smv58ce8mM7XRyRLXF3Vk6uVAfdqt7zVhGY6wzc+K/SmG6sDbrtb+L76rSbKYu1hLkETphzNHfu7na5rZOxkBorrZSsloAZrXrC8kpGazEEv4qaYgeWMw4sZ0wtdmnNtand28Znq5tNLJpGHFZLbPrCaRr7Gqdjdov06WsO8qvh3WrHb76Xu798nOO33MvibXNkJ/qbWc4NFqdCUG1hf4POgRbNw9PMXDLDoQeey30vO8glF81wyYEQbFYzkCIiIiIiIiJyNlHgbIACZ7tYpwO33TY8sHb77aG5yEKtFmqlDQbUiu/DhyE9u2/25Z5z+V9dDsD1P3s9ianmiexNWSejPTc6uLZes5P5QM2zNWqG7WvgtQRmQ22yPgZTF0yNfo/YRdNMHJhQs4kyVgabgzzylePc9cVeYK17dBEbaEa03BxkfmiC1oXT7L/vDOc/8Fwuvuw87v+AA1xycJILptUMpIiIiIiIiMhWuDvz7S4n5ldYmFvmQQ88uNNFGhsKnA1Q4GwPW1kJtdIGm4Asuu+8sz9/vQ6XXjo8sHbBBaFW2w57ww1v4OlvfTpv/uE385QHPWWniyMyltydzmKnMshW9U63fRfsC8GwUmBs3wX7SFWzRvagcnOQ994yyx1fPMadXzrGiVvuZeH2ebK7FrCBdyEWzUEuzjSwQ5M0Lpxm5tIZzn/AuVz8DedxznmTNFo1WhM1Gs0azXpKPU2op0YjTUJ3LfyuJ4mCbyIiIiIiIjLW8tyZX+5ybHaJu++Y55475zh+1wJzd59k/ugCi8eWaB9bojO7TD7bxuZXSE92aC51aSx3WTpvghce+dWd/jPGhgJnAxQ4k6GWl8N71AYDasX3kSP9+ZvNXlCtqjnIQ4fCe9hOsyzPeNDLH8RkfZLrnnOdarKcAe7Ocnd5zWepu7Q2rVORVsrX7rZp1VrMtGaYac4w05phf3P/avdMM/5uzTDVmFKtQhEZO0VzkLNfneXoV47ztZuOcdeXjoUaa7fP073rJOncyshxZKmRp0aeWOhOkvA7Db89TfDUoJaEWqE1g1oKtQSrJ1gtfuoJ1khJ6glJPSVtpCSNlLSekjYS0kZKrZGSNmvUGym1Zvhda9ZoNFPqrRDIq7dqNFs16o3w3Zio0WrVaUzUaDZCkK9RS0KQTwE+ERERERGRPSXPndmlDvccX+TuO+Y4dvs8x+8+ydyRBU4eXWTp2CLt48t0710mnwtBsNpCh8ZSl2Y7GzrerJaQT9Vhf5NkpknjnBbNAxNMnjfJwUtneNpvXnnm/sgxp8DZAAXOZMsWF+HWW6sDa7fcAvfc059/cnJ0YO3AgW0LrL32M6/lmW9/Ju942jv4wct+cFvGebbLPd9wcGrT+dYZtp2tfW/VZhjGRH2CidoEjbTBUneJufYcuY9uKtAw9jf3rwbS1gTa1gm8Fd31tH5K5RcR2W5Fc5B3fPEYt950lMXZNp12l+5KRred0V3JyFaK7y5ZOyPrZOSdnHwlJ+9kePztnRzvZNDN8W4emlXt5tB1rJuHT+ZYfnrOfXNjNciXpyHAlyUhyOdpQl4L39QM0hjoq/cCe9RSklqCNRKSeoLVU5L4SZtJDPKlq0G+ENyrrQb56s2UerMWPq0ajVZKo1Wn0arRbNZoTtRotuo0W2n4bvRq89XTEOxTgE9ERERERCTIcufexRWOHFngyB1zHLtjnhN3nWTu6AILRRDsxDLZ7DI+28ZOdqgthJpg9c7we31ZMyWbqmP7m6QzTerntmgdmGDfeZPsPzTFzH32cfDwNIcunOa8w9PsO7SP+qTu6W0XBc4GKHAmp838/OjA2okT/fmnpqqbgCy6zzlnw5Pu5l0ue+llHJw4yMd++mNnrNZZN+9uPTg1mC9bmzZquE7eOaWy15IarVpr9TNRm+j73aq1mKiX0tKB3xsdriJfLamtWUbuzkJngdnlWWbbs8wuzzLXnlvtnm3H30X/wd8x/0aCehO1iS0H3or+EzW9p0tExpvnTtYJAbnBT7fdZWU5o73cob3cpb3cZSV+OstdVtpdOu0sBPfid6ddBPm6q0G+vBhnJydfycg7WQj0dbMY4AtBPu/m0CkF+bJykC8n6Z6+8/ReLb5erb68FmrweZrg5QBfLdTqo572avDF76RZI2nGoF6rRtqsUWsVnxC8q0/UqE/UQiBvskZzok5rX4Nmq05rX52JfeF3q1mjWQ9BvEatV3tPxx0RERERETkV3SznxMIKR+6c58gd89xzxzyzd5eCYMeXWDm+RDbbxufbJPMr1Be7NJe6pNnw67LuZI18qrEaBGuc26J1cJKp8yaYPrSPcw9PcfA+0xy6aD8HD08xeXCSWrN2Bv9yGaTA2QAFzmTHzM5WNwFZfM/P9+efmakOqBXf09N92a+67iqe/c5n87onvY7LL7h8a0GsbHhgq2q4bt7/zprNqif1zQWj0tEBqo0GsYrg1W7U7rY3FnhbnmVuZW3gbbY9y8mVk+tOp5bURgfa1gm8zTRnmG5Oq+lJEZENcHc8815wr1MO8IXAXnspfFba8RODfp3lGNhb7tKJAb1uOwxbdPcFDjsxwBeDfavBvU5OXgT2ykG+IsAXP+k2BPlyC02QZKmFpkiK71qC10MQzxsp1GMQr5FAbHbTmrXQ/GYzNLmZtkJTm2kzBO3qrRr1iXqojTcZat81Juu0Jmo099VpTjZoTdZptWo0agnNWkqzFLwLaQm1VMcvEZGzkbvTzZ1u5nTzPH73d2d5TidzstzpZDlZHrqTxEjMSAzS1W4jSSA1w8xiOjHdSGP+YtjUDIv5i2FX0w09BCIiso06Wc6xuWXuvn2eo3fMcfzOk9wb3we2cM8iy8eWWDmxTDbXhrk2ycleECwZctmSJ5BN1smnGyT7m9TOadE4t8XEgQmmzt/H/kP7OOeCKc67cIrzL9rPufeZYvLAJElN1wfjRoGzAQqcyVnJPdRIq6qpdvPN4bO42D/MgQN9wbSVSy/m60++gK+uHN3UpJtpMwaTmjHwNEGrvk4wagu1rAbzNNMmaZJuz/yTbZXlGXPtufUDb0X/IYG6jTQ9Od2c3nLgrUhT05MiImcPz0OAr7vcpdvu0lnqsLTQYWlhheXFDssLHZYXO7SXOrQXO6zE785SDO4th+4wfEa23CVrx+Y5VzK83Q0BvXYI5rGSQSeDlRi86+Qk3dHHn40YFrwrp3k9fKiHIJ410r5P0gyfNAbu0mZKrRWDd60ikBdq4jUmajQnQ+CuOVmnNdEL3hXBunLwri+op+Y1RWSD3EOAqFt8snxDQaaVbk7WzVlZyeh2MlZWwu/uSpdOJ6fbzeh28tVP1g35uh0n72Z04/BZJyPL8lATuxu+syx05zFPnjl5Kc2znLzr4TtziN+e5Xj8JnM8D/3IHcvBcifJHXMwj9156C73C98OxS0xA4/BLbeYbIbH3aybxTwhs1schv7hMIvDrv1tIYIGSdEdv5OqfiFgZwmxGyyJ6Un/7yQBLMESYr9efzPD0v7fSWK9fknxm9Lv3rjKeYvuJKnoNiNJY5AxNUiMNE1IjNVxJolhKaSxuxgurcVgYy2JwcjYP01IE7Ak6eVJjHojDe+bnQifRqNGa6JOo5XSbIZjqAKVIuOj3c04dmKJu26bi0GwBWaPnGT+yAKLx5ZYPr5E58QS+Wwb5ldIiyDYyPeBGdm+Oj7djEGwJs0DE0wcnGDqvH3MXDDFORfs49CF05x/0X4OHJ6mub8Z9quy6+2JwJmZfS/wl0AKXOXufzQsrwJnMpbcwzvUhgXWbrkFlpe56Tz45GGY6EJr4DPRWZvWzBj6hAW1GqRp+FR170T/s7FMaUq4QpEyd2exs3hKgbe59hzL3eV1p9WqtYYH2jYQeNvf3M9kfVIXVSIiu8Rq8K4dA3Dxk7Uz2osdlhdWWF7osLS0wspiN6QtdVhZ7ISmOJdCwK/TN2yX7nJGHt+xl7czPDbH6StZCOCthHfrsXLmgndZqTYe9VDzjkYM4tVLAbxGqH232pRmveLhJffe63cHzg8t9u/lLdWacNYMZOXhvRjBwDiKxDXjHZy+rw7OwHhD3sGT2YHxFvnWDD/8762aVtXfsOFyDTvf3kTevrMUs97veGO8r/fqqelAP+tPszg8q797PVaHix1WKoRhpTy9CVjSPw4GhulraKD0N5gNDAOrN6wGilH6e0t/R3/h+uaBrZkHA9ONQY81ZRksg/X+7sH5UB7OinxAN8tWg0O9wFForjfLcrKurwaLvAgadXPyLMe7TpaFoJF3nTwPeYpAUQgg9YJHIXAUAkiUAkirv/P4bs/cSUrBo9XAUQweJTHgZN7fb+g14w5zA9IQuCFNIAnBIZIkBInSkB66kxDQSUMzw0kaAjSWhsCMJSGw5XE+ee64E79DYK3o9rz/d9GNh2Eo5/G4DIpxF3kJ+Sjni8OG76J78Hf/d9/+Vvr03jEb3jvrsduLJqjT2AR1auEhmFqC1QyL3Ulskjqpp1g9vGc2qSehZnstWX3n7Op7Z0vvnq3H5qsbzfAe2kYzpDXie2ibrdDdmAjvm23GgF+rVadRT0l1017G3NJKl6NHF7j79nnuuXOe43fMM3fkJPNHF1k8tkj7+DKd2WXye9vYyRAEayyOfh9YtxHeB8Z0g2Qm1ARrnTvB5KFJps6bZOb8fRw4HN4Fdv5F05x7eJr6ZF33emSoXR84M7MU+E/ge4DbgGuBH3P3z1XlV+BMdiV3OHIkBNFOnIAsC59ud213Vdq49D9bmZ1a4K0Ivlm4yOv7nEra6RjnGU5bIWPWl5jNFpnzZWbzRWbz+DtbZDZbYDZ+z2WLzHYXmO2eZLZ7krnuArOdk8x3F9ZdhDWrsb8xzUxjOnyvvvdtf3+grXUOU42p8MSoO25O7o4TL6IBL/2Xex6vaUv9vNSvb9id6+delLWi31lW1q30A0iTlNRSEktGdieWkFq6se4NjO90dG+qjNvUrYsNkc0ZFbwrujtLHVaWuqEm3uIK7cVOaG4zBvFWYi28Im+3Xa6FF77zIngXA3m+koWmNDsZNuLGg4ic3dzAE1v9UPomCTf/Q3uCMWiU9AeISCwEAJKk7ztJe4GipAggxfSkFj5pKaiU1hLSWkpasxA0qCXU6glpmlCLwYNaGgIItdivVk/jJ6FeT0nryer4LY636ndVv/LHE49/i0EaA5sJI89vN3IOXD5fhFjbClv9TixZk3aq38U4t2198f7g25oAX0Ww71T75XkI4GZ5rCGYEwLCMeiYZTn56jerv0M/J89DQDjPPdQyzL2X3wkB5PjbnZgvD9deWRxHlodmqGOt9PCe2Tw0Pd3NydrdEIxeiU1QF01Rd2IgunjPbDe8d5as/12zdB3LcixzktXvUFvxdMqT3rtnQ9AvBvtK755dfe9sGh6cseK7CPjF99EmjbDdJo0Q9Ks1UpJaStoM22oR+Ks3a6T1hHoM9NWbIa0c7Gu0ajSa4b21jUZKazJ8NyfrNJs1mo0Q8NN1w+6z2O5y113zHPnaHPfcNc+Ju04yd/cC80cXWD4eaoJl9y6Tz7Wx+RXShQ6NxS61Ee8D60zUVoNg6UyL+jktWgdaTJ43yfT5+5g5f4oDh6c4/8L9HLpomnMumNL7wGTbbTRwNs5r3iOBL7n7VwDM7B+AJwCVgTORXckMLrggfHazPD/7gnnb0T/Pe59wVdD7WwfThuU93Wk7pAEcip+tygzmmzDXhNkmzLbC91ype7bVZa55gtnmidW0rzXhxlZvmOwsrFgYmp4JDzsnpe7V79idrEm3/uGoGtb6x1kxfDJknL3xWsXwts5w4TntZHUYW9PP1u1nfeOGsPxynMwgMw81OSz8zs3pGLTpT1vNOyzd2FCefBdcOxbrUepG6pAQvlO3yvTES/0p9d9geuLrzLSB3muetl7ngn0ri8Q2OdTw3NV9NjT2ck2IquvQESMxZ935MnpUo4ddbcIKpyia96WDx0J76UNMHzoMPiR97fjW5ls73t5v3/B0yvn7xlcMM5g+7O9cM80R6XXwuuMzA/lW59mQ9NV+4cZmkqUk3Rq1bo20m5JmNdIsJc3S1WEMX7N+W/w7eum9Wl+9GkXeyxt/lisOOaW1pjSIFVNezVvk7kvu67ZSXu8rS6FU1nLxrP9vs8G8a9J7E1tNK4pb3v5ieVfL5QPlsuF/V5Fenjer/bw3fDGM0zuu9f/NoWdvX1BaiVanM/i3x2HWplaml/cztjpHin8G52Ky+tdW1U5ytzVDhB7l+R2nUV5FvDQziEtvoFyr63LFsaM8XetLXzuP+gfsTWFwfqw55HjFSaKDJ06swgVJhluOJzluOWY5nmTkieOWkadObjl5kod9ovX2jb2P923jeZFmA/0ZMmwp3+hhB35XDZuDLw/PM6wc+Wq+dYbdBedOG9V3Trz6e+C8djDPQHoyathNjbN3fn1q4xzoF8/vRg5bmd4rz/B8pZUlAVrxczo5WJ7geQJ5imcp5Ame1cATKH7nKZYXv8PHclv9bXmCZWEcliXxd0yP/YrvpJSWxPQkT0g6CbZgoTv2C91GGr+LtFqWsHIaZ0tuTp7kZKmTpzl56mRJHrtz8lOuurqZ4XfxTuRUZ+MmJHlCc6nOxFKdpOpYR9zPT6zQmWjTnlhhZbpN5/xlOq023Ykl8tYy2cQSeWsJby3hrUXy1jIkvuZPKc6hWQRuCZ+qP3cjaWvHPmS4qlOTDQxbXYaK4SrHv/npbXSaF6fn8uY//HJFTjkV4xw4uwj4Wun3bcC3ljOY2XOA5wBccsklZ65kIrK9kgQajZ0uxd5UDqqdyYDdemkbyJvmOefkOeecwnQ8z1nK28zmi5zMQ/ORIXBUukAc+E7KF33FxZ2Hm3VDg1Wr4/SBC8O1/XpNUA35HtVvo987PY7tnv522sQ4i5tMIXDnMdDmZHh/0A0nJ4/dFfkH0vv7UxpfRTp5CPQN5sGr09cb52p5R4+nL2/8zpLq9JVS/qqLh+Hzd3M2M+7+aWxguLheDL3hN2QUbmxunaoa/4jBNz3+Decs8vf2WdDbZ4XuUrrTnwcr5WMgn/Wn+9rhk3z0uCnto8vlKgcwKodfJ09/mUvlLKcP5Bn+d5amM3T48jC9MEFxM3HY9IeOGwcPNfkHg5YhbeA3ve2mql+R7gNbylbHA72b56PzVpR9i+PplW/4eOL//ePxIdP0wbTB79586OtnA9Okf5qDhm2ro7bhYfvA7ZrG6k2vUx3P0PEPy79N0930fBg+P4c9JLR6rlgOCMT9nuUD54YMBA6c1QdMBse3NnCxdtgw3WT9YYvyrBl2+HiH/Z1F+qgHp4aPk17QZYPT6Z9Xm5lObz/QF9wzQrCwHAS0ct4h6fhAvqpx9qcNy7vutOI51NC8ff1Gl6ucnlfmzTdVrnIZ8qHj3Mw86B/vjjDCCj1uPG5PWYp5LQbmajEol8agXfgkWS0E57IU8zQG61KSmC/J0tXhkjyNQbtSWpaSDqQn+WmcaVUrw5D9+a6wmb/tFDYUT5yV1jIrrSVWWkt0JhbpNhfpthbpthbImot0m8uVxal6WGY1JY8f1v4pVacR1eOvSBs4iG98uM1PsziebHa4YdPc6t84mG86bVcMJadqnANnVetR3+rn7q8AXgGhqcYzUSgRkV2laI4yrXgnyh5gwGT8iGyWEV7CmgL1HS6LiIiIiIiIiIhszDg+K1G4Dbhv6ffFwB07VBYREREREREREREREREZc+McOLsW+Hozu7+ZNYCnAe/Y4TKJiIiIiIiIiIiIiIjImBrbphrdvWtmPwdcTWgF6VXu/tkdLpaIiIiIiIiIiIiIiIiMqbENnAG4+3uA9+x0OURERERERERERERERGT8jXNTjSIiIiIiIiIiIiIiIiLbRoEzERERERERERERERERERQ4ExEREREREREREREREQEUOBMREREREREREREREREBFDgTERERERERERERERERARQ4ExEREREREREREREREQEUOBMREREREREREREREREBFDgTERERERERERERERERARQ4ExEREREREREREREREQEUOBMREREREREREREREREBFDgTERERERERERERERERARQ4ExEREREREREREREREQEUOBMREREREREREREREREBFDgTERERERERERERERERARQ4ExEREREREREREREREQEUOBMREREREREREREREREBFDgTERERERERERERERERARQ4ExEREREREREREREREQEUOBMREREREREREREREREBwNx9p8twRph/2CVwAAAVL0lEQVTZUeDWnS7HGDkPuGenCyGbomU2frTMxouW1/jRMhs/WmbjRctr/GiZjR8ts/GjZTZetLzGj5bZ+NEyGy9aXuNHy2xzLnX3Q+tl2jOBM9kcM/uEuz98p8shG6dlNn60zMaLltf40TIbP1pm40XLa/xomY0fLbPxo2U2XrS8xo+W2fjRMhsvWl7jR8vs9FBTjSIiIiIiIiIiIiIiIiIocCYiIiIiIiIiIiIiIiICKHAmw71ipwsgm6ZlNn60zMaLltf40TIbP1pm40XLa/xomY0fLbPxo2U2XrS8xo+W2fjRMhsvWl7jR8vsNNA7zkRERERERERERERERERQjTMRERERERERERERERERQIEzEREREREREREREREREUCBsz3BzO5jZv9gZl82s8+Z2XvM7Dlm9q4h+a8yswed6XJKNTM7Gb/vZ2ZP30D++5nZjae/ZDJKsdzk7GVmv2FmnzWz683s02b2rVsYxw+Z2a+fjvLtFuVtwcweb2ZfNLNLdqgsP2lmN8RlfqOZPWGL47nCzB5f+v18M/vl7Svp+DKzLG5Pxed+O10m2R5V5xfFum9m32ZmH4vL/PNm9vwdKuaeo/ONUzdq3V5nuIeb2Ytj95Vm9l+3MO1bzOy8Uelm9jAzu9nMvnk7zztimSuvB3cLM3Mz+7PS719eb/80uCzN7NVm9tRTLEflct7iuPbENj/kHsY3bHIcv2tmjz3Fckya2d/H88cbzewaM5sadc29HdMdR1u5tirPKzN7nplNblNZtu3cfDv2AWez0rn7jWb2prjOb2j9NrMPmtnDY/d7zOycLZbhuWb2E1v/K2Sz9sqx5GxlZheY2evN7Ctm9kkz+6iZPWmnyyVr1Xa6AHJ6mZkBbwNe4+5Pi2lXAD84bBh3/+kzVDzZnPsBTwdev8PlkC0ys9Tds50uh4CZPQr4AeBb3L0db2Y0Njsed38H8I7tLt9uZGbfDbwEeJy7f3WDw9TcvbtN078Y+A3CMp81syng0BZHdwXwcOA921G2XWbJ3a8Y1nM7l6mcVV4D/Ii7f8bMUuCynS6QyOnm7p8APhF/XgmcBP59O6dhZpcDbwZ+1N0/BXwKnXdsRht4spn9obvfs8FhrmSblmW8FrdTHc9eM+IexgXAf5byjby2cvff2obi/CJwt7s/JE7zMqAzaoBtmu5Y2eq11cC8eh7wd8DiKZZF9zk3Z/Xc3cz+Hngu8NZhmYet3+7++Kr0jXD3v97qsLJ9dL/qzIjHuLcTjnFPj2mXAj+0weG1nM4g1Tjb/R4DdMoHInf/NPARYMrM3mxmN8WnqAzWPDVy0sz+wMw+Y2b/YWYXxPQftPBk8afM7F+LdDmt/gj4jvg00C/Fp4A+YmbXxc+ap1xj/ytKv/8tXoDLGRKfWv2Amb0euCGmvT0+VfJZM3tOKW/l9ianxWHgHndvA7j7Pe5+R3wi+I/N7OPx83UwfJ9nZs8ys5fG7leb2YvN7N/jk0O79snEzTKz7wBeCXy/u385pl1qZu+LT6W+z2IttDgf/9zMPgD8sZntM7NXmdm1cf4/IeZbdx844HxgnnAzDHc/6e43x3FdEbe5683sbWZ2bkwvHw/Pi+tHA/hd4Efj/vhH4/gfFPN/xcx+YRtn39iL28mbzOydwHstPKn9vrjcbhhYpp83s1fG/eN7zWwi9vu6uO19Jg73wJj+K3HduN7MfmcH/8y97nzgTgB3z9z9cztcnj1lnW3qJjN7TdxG3mzxaX4z+6247dxoZq8YuA4ojoP/Gfffe9qweRLP8d5loWbtc4FfiseF7zCzQ2b2ljiPrzWzb4/DHIz7tk+Z2d8wOqjyXwg3Vp7h7h+Pw6973mFmiZm9PO5H32WhFkDR73vjOnEN8OTS33jAwvnp9fF4eHlMf35cf94bj4FPNrMXxvXsn82svq0ze/t1gVcAvzTYo2oZVS3LmP3Rg/M5jmPNMah0LHs5cB1w34Hpbuo6wMzub+FJ9GvN7PdK+Q+b2YetV1NkN22rlfcw3P0jNnBtZQM1Y6xUq9BKNYXM7I8s1Fy73sz+NKZVbqcDDgO3l8rxheL6AUit+pylPN3Ka4tdaM21FXCxmb0VwMyeYGZLZtYws5aZfSWmv9rMnmrh3PlC4ANx+f6Q9Vov+IKZFefsDzOzD8Vt6GozOxzTP2hmLzCzDxGCnavM7Nlx+X4mLu/J0rSr9qFmZi+N68u7Cec4e8VHgGIdXXf9Lovr+nk2+txj2LX2ag1BG37MTc3sT0r73J+J6bt5X3jaDe5TY9qw49T/iMvkQ3HdeOmOFXy8fRewMnCMu9XdXzJiPa869t1kocW4Gy3c03+shXu+XzSzR8bhHhn3cZ+K35fF9GeZ2VstnMt90cxeGNN/ysxeVJQr7j///EzOnLONAme734OBTw7p982Ep3oeBDwAqDpR3Af8h7s/FPgw8OyYfg3wbe7+zcA/AL+6nYWWSr8OfMTdr3D3FwFHgO9x928BfhR4ccUwVwHPArDQtEXT3a8/Q+WVnkcCv+HuRROoP+nuDyPUWPkFMzsY04dtb7L93gvcN574vdzMvrPUb87dHwm8FPiLmLbRfd5h4L8Rnrj8o9NT9LHTBP4JeKK731RKfynwWne/HPh7+vdh3wA81t3/F6GW2Pvd/RGEGyl/Ymb72Ng+sOwzwN3AzWb2t2ZWrnn9WuDXYlluAH572EjcfQX4LeCNcX/8xtjrG4H/Ttjef9vO/puJp8tE6UbH20rpjwKe6e7fBSwDT4rL7jHAn5lZcfP464GXufs3AfcCT4npfx/THwr8V+BOM3tczP9IQi3Ah5nZo0/3HyiVXgR8wULg+WfMrLXTBdpjRm1TlwGviPu3OeD/jekvdfdHuPuDgQnCcatQi8fB5zFif7jHDJ0n7n4L8NfAi+Jx4SPAX8bfjyDsx66K2X8buCaeT7wDGNV08T8BP+fu14zIU3Xe8WRCSxUPAX6asP8lbpevJLQ88h3AfUrj+R3gU3E9+d+E42LhgcD3A08g1Ab5QKx9sxTTz3YvA37czGYG0tcsoyHLEirm8zrHoMsI5zjf7O63Dkx3s9cBfwn8VSznXaXxPB24OtYUeSjw6c3OmLPYqHsYsPbaaiQzOwA8CfimuI7/fuw1bDstexXwaxaCl79vZl9f6jfsnGVQ1bXFblN1bXUd4Z4ThH3OjcAjgG8FPlYe2N1fDNwBPMbdH+Pu74jb4BWEc/g/jefWLwGeGrehVwF/UBrNOe7+ne7+Z/R7azzePRT4PPBTpX5V+9AnEbbhhxC2w003wzuOLNTU+z5i8ISNr99Vhp17wMa2h6pj7k8Bs3F7fQTwbDO7P7t7X3imrHu/ykKQ+ncI942/h3AfWbbmmwj7xyrD1nNYu5y+jnAcu5xwL+LphP3ZLxPO5QBuAh4dzzt/C3hBaVpXEO6jPITwUPB9Cfe6fqh0L+N/AH97Cn/r2FMV5r3t4+5+G4CZfZpwgTV4YbYCFG3ff5KwgwS4GHhj3Hk2gJtPe2llUB14qYUaZRnhRvOgNwG/aWa/Avwk8OozVzwp+XhRsyX6Beu1X3xfwknpMYZvb7LN3P2kmT2McBH3GML+rHhnyBtK38XTNhvd573d3XPgc6Yag4UOobmjn6L/CdBH0Xva/XXAC0v93lRqfuBxhJO34j0FLcKNxjtYfx+4yt0zM/tewgnodwMviuvAiwgX2h+KWV9D2Hdu1rvjU7ZtMztCaE7oti2MZ9wNa6rxX9z9eOw24AXxBmMOXESYXwA3x5rxEPaD9zOzaeAid38bgLsvw+pNy8cRmi4DmCLsTz+8zX+TBD4s3d1/10LzPo8jXLT9GKG5MzkzRm1TX3P3f4vdfwf8AvCnwGPM7FeBSeAA8FngnTFf0UTTJwnXB7vd0HW71L3ZefJYQk3k4vf+uC97NPHY5+7vNrMTI8bxr8BPm9nVI5rkqTrv+G+E42gO3GWhBjeEmyo3u/sXAczs74DnlIZ5SizX++NNsiLQ9H/cvWNmNwAp8M8x/QbGYP1w9zkzey1h3V8q9Rq2jKpUzedhx6CvAre6+38MGddmrwO+nd4N69cBfxy7rwVeFW9uvb107NwLBq+t1jNHeMDgKgs1iIr5XLkOuPt8keDunzazBxCW9WOBay00S7hExTnLkOlXXVvsKlXXVoQHf79kZv+FcMP3zwn7wJRQs2ld8Ti15O4vM7MHE4Kq/xKXWUqs7R69sWIUAA82s98HziFsp1eX+lVt248G3hD3u3eY2fs3UtYxNhHvB0JYLv8/ofbfRtfvKsPOPWBj20PVMfdxwOXWq+02Q9h/7uV94XbZyP2q+wAfdPejAGb2Rta5/paNMbOXEc7DVoBbqV7PV1i7nG5296KW4GeB97m7x/O1+5WGf0186MMJ95EL73P32Tj854BL3f1rcZ/3A2b2eaBeTGOvUuBs9/ssMKy5sHapO6N6fei4u1fkeQnw5+7+DjO7Enj+qRdVNumXCLUnHkqoPbo8mMHdF83sXwhPiP4I4YkROfMWio64vTwWeFRcPh8kBAJg+PYmp0G8GPog8MF4cvHMolc5W/ze6D6vvF/VOy2CnLD/+Vcz+9/u/oIh+crzfaHUbcBT3P0L5cwWmuEZuQ9cM4GwfX0c+HjcN/4to29gdOnVzl+vBs1Gjql7WXmZ/jjh/XIPizdjb6E3fwfn4wTDtyUD/tDd/2abyyrVjgHnDqQdID5I4KEZ1r8ys1cCR83soLsfO8Nl3KtGbVODQSGPNY9eDjw8XiA/n/59XLEd7pV92ch1O9rsPEkI53rlQA3xZu+wQN2gnyPUfno58DND8lSdd4w6/xg27aphirxF02u5mZXPVXPGZ/34C8LT3eWnpkcto0HD5vOaY5CF5h7Lx7xyvyvZ2nXAmuXm7h+OwfLvB15nZn/i7q8dzDemRt3DgP75Wz5Xg4rzNXfvWmi26ruBpxG2re9iyDpQMfxJwo38t5pZDjweeAvV5yyVoxjSvasMubb6CKEWU4fwMMCrCQGvX64eS4+F9yP/MCGQBWGb+6y7P2rIIJXbXZzmEz28h/VZ9D/YM+zabdcupwprHnqL+8GNrt9V1px7bKC7rOqYa8DPu/vVg5l38b7wTNno/aq9tF2cTp+lVIPT3f8/C++F/ATh4Zs163lcLoP7uPI2mpd+l8/Pfo/QUsCT4vnJB4cMX97WriLUWLuJPV7bDNRU417wfqBpZqtNvpnZI4DvHD7IhszQa+v7maMyyraZB8pPQc4Ad8YnpJ5BOAGtchWhCbNrS0/7y86ZAU7Ek5BvBL5tpwu0F5nZZQNNrVxBeLoHQnX14vujsVv7vFPg7ouEJlB+3MyK5lH+nXDzAsJN32FNUV0N/HzR7JiZFU2+DN0HmtlNgyMxswvN7FtKSVcQngifBU5Yrz38ZwBF7bNbgIfF7vINnMH9sWzODHAk3uB/DHDpqMzuPgfcZmZPBDCzpoV3JVwN/KSZTcX0i8xsL72H4oyKNw7vjDeyiqavvhe4xsy+v9hGCU9FZoRmfeTMGLVNXRJrR0CoCXgNvRsg98TtZ0+/k3PUur2J0QweF95LuDlPHGdxU/LDhGMeZvZ9rA3YleWEZXaZmf3uJspyDfAUC+86u4DeTeKbgPtbfEdkHHehXK4rCe8qmtvENM9q8RroH+lvom3YMtroMX4rx6CtXAf8G/3nS0V5LyVs968k1BD5lophx1XlPQzrb1q9cDdwfqwl2aS/2dli2Clgxt3fQ2j6rVjWw9aB8rDfbr133zYIzZMNNr+5nqpri11lxLXVhwnz/KOxpspBQu3Xz1aMZnXbi+v3y4EfKQU2vwAcKo5pZlY3s2/aQPGmCfv4OqVtaIQPA0+z8K6hw4QadLI5Veceha1uD1cDPxuXI2b2DRbehb2b94U7Ydhx6mPAlXFfWycEtWVr3g+0zOxnS2mT8btyPT+FaZXvYz1rIwO4+8cINQ2fTq+G6J41Lk+IyRbFappPAv7CQjNky4QbgW8/xVE/H3iTmd0O/Adw/9HZZRtcD3TN7DOEp6ZeDrzFzH4Y+ABDnrBy90+a2Rx6UuBs8c/Ac83sesLJ/7BmXOT0mgJeYmbnEJ5U/RKhuaIfIFyof4zwcElxU+n5aJ93Stz9uIWmEj9sZvcQmux4lYWmZI8S2s+u8nuEJ8WvjzfmbyEsp8p9YHxaq+px8Trh/QgXEo6FR4Hnxn7PBP46BmO+UirLnwL/aGbPIJzgFj4A/LqFZk3+cFMzQiC8r+ydZvYJwnsI1gQ6KzwD+Jt487gD/LC7v9dC8z8fjTGbk8D/Q3j/nZwePwG8zMyK94f8jrt/2cz+gND86SJhn/rjI5qWk21i4X0kbUZvU58HnmlmfwN8kfCupEULNQNvIOxTrz2jBT87Va7bmxj+ncCbzewJwM8TjnEvi+d7NcKN2OcS3g/yBjO7jvCQxldHjdTd23GcHzKzuxleo6LsLYSaNTcC/0m42TXr7stm9hzg3fE4fA2h2TMI5zl/G8u7yO58SOjPKAVJGL6MBpdlpRHHoFH7vq1cB/wi8Hoz+0XCsi1cCfyKmXXitH9iA+MaCyPuYTyP0BRtOW8nnht8jFBLtOqcYhr4Jwu1bY3QcgsMXwfKHkioTW2Ea4N3E5bDyId+BlRdW+w2w66tFghNBxfNaF9PCHJU1Vx5BfB/zOxOQq2Ig8Db4vZ1h7s/3kLzZS+20JRsjXCNUBWEK/tNwvpxK+G4t15g/G2EGok3EPahHxqdXSqsOfco9dvq9nAVofm56+L2eBR4Irt4X7hDKo9T7n6nhRYKPkpoIvU6hj+8LyPEY9wTCddOv0pYlxeAXyO8MuJ+rF3Pt+qFhKYa/yf99zPW84/AFe4+qknxPcGqj1cislvEm8QfBL4x1swQkSEsNG/1cHe/Z6fLIltjZj8APMDDS8ZFRHYtM3so8Ep3f+SQ/vcD3uXuD67qL7uXmU3Fdw4dJDRR/O3uftdOl0tkr9G1hew1o849tD3sHhaaPX24u//cenll/JjZu4AXufv7drosO001zkR2MTP7CeAPgP+poJmI7AXu/q71c4mIjDczey6htsTzdrosclZ6V6z50QB+T0EzERERERklnjt+HPiMgmaBapyJiIiIiIiIiIiIiIiIENqUFREREREREREREREREdnzFDgTERERERERERERERERQYEzEREREREREREREREREUCBMxERERERERERERERERFAgTMRERERERERERERERERAP4vAYr5DU7oLcwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 2160x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "grouped = dataset.groupby([\"Country/Region\"])[\"Deaths\", \"Confirmed\", \"Recovered\", \"Active\"].sum().reset_index()\n",
    "grouped = grouped.sort_values(by='Deaths', ascending=False)\n",
    "grouped = grouped[grouped['Deaths']>100]\n",
    "plt.figure(figsize=(30, 10))\n",
    "plt.plot(grouped['Country/Region'], grouped['Deaths'],color='red')\n",
    "plt.plot(grouped['Country/Region'], grouped['Confirmed'])\n",
    "plt.plot(grouped['Country/Region'], grouped['Recovered'], color='green')\n",
    "plt.plot(grouped['Country/Region'], grouped['Active'], color='purple')\n",
    "\n",
    "plt.title('Total Deaths by Country')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<H2>Vizualizing the crisis Country wise by different plotting tool</H2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        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"
    },
    {
     "data": {
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       "                require([\"plotly\"], function(Plotly) {\n",
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       "                    Plotly.newPlot(\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country/Region</th>\n",
       "      <th>Case</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>China</td>\n",
       "      <td>Confirmed</td>\n",
       "      <td>3124622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>304</th>\n",
       "      <td>China</td>\n",
       "      <td>Recovered</td>\n",
       "      <td>1428378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Italy</td>\n",
       "      <td>Confirmed</td>\n",
       "      <td>238049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Iran</td>\n",
       "      <td>Confirmed</td>\n",
       "      <td>150833</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Korea, South</td>\n",
       "      <td>Confirmed</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>238</th>\n",
       "      <td>Moldova</td>\n",
       "      <td>Deaths</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>411</th>\n",
       "      <td>Nigeria</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>262</th>\n",
       "      <td>Cuba</td>\n",
       "      <td>Deaths</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>253</th>\n",
       "      <td>Bangladesh</td>\n",
       "      <td>Deaths</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>250</th>\n",
       "      <td>Burkina Faso</td>\n",
       "      <td>Deaths</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>293 rows × 3 columns</p>\n",
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      ],
      "text/plain": [
       "    Country/Region       Case    Count\n",
       "0            China  Confirmed  3124622\n",
       "304          China  Recovered  1428378\n",
       "1            Italy  Confirmed   238049\n",
       "2             Iran  Confirmed   150833\n",
       "3     Korea, South  Confirmed   137889\n",
       "..             ...        ...      ...\n",
       "238        Moldova     Deaths        1\n",
       "411        Nigeria  Recovered        1\n",
       "262           Cuba     Deaths        1\n",
       "253     Bangladesh     Deaths        1\n",
       "250   Burkina Faso     Deaths        1\n",
       "\n",
       "[293 rows x 3 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
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   ],
   "source": [
    "grouped = dataset.groupby([\"Country/Region\"])[\"Deaths\", \"Confirmed\", \"Recovered\", \"Active\"].sum().reset_index()\n",
    "grouped = grouped.sort_values(by='Confirmed', ascending=False)\n",
    "grouped.head()\n",
    "\n",
    "temp = grouped.melt(id_vars=['Country/Region'], value_vars=['Confirmed', 'Deaths', 'Recovered'], var_name='Case', value_name='Count').sort_values('Count', ascending=False)\n",
    "temp = temp[temp['Count'] > 0]\n",
    "temp.head()\n",
    "\n",
    "fig = px.bar(temp, x='Country/Region', y='Count', barmode='group', color='Case')\n",
    "fig.show()\n",
    "temp\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<H2> Grouping data on basis of Country, State and Date </H2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Deaths        103112\n",
      "Confirmed    3124622\n",
      "Recovered    1428378\n",
      "Active       1593132\n",
      "dtype: object\n"
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       "    <tr>\n",
       "      <th>3419</th>\n",
       "      <td>China</td>\n",
       "      <td>Hubei</td>\n",
       "      <td>2020-03-18</td>\n",
       "      <td>3122</td>\n",
       "      <td>67800</td>\n",
       "      <td>56927</td>\n",
       "      <td>7751</td>\n",
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       "    <tr>\n",
       "      <th>3418</th>\n",
       "      <td>China</td>\n",
       "      <td>Hubei</td>\n",
       "      <td>2020-03-17</td>\n",
       "      <td>3111</td>\n",
       "      <td>67799</td>\n",
       "      <td>56003</td>\n",
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       "      <th>3417</th>\n",
       "      <td>China</td>\n",
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       "      <td>55142</td>\n",
       "      <td>9557</td>\n",
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       "    <tr>\n",
       "      <th>3416</th>\n",
       "      <td>China</td>\n",
       "      <td>Hubei</td>\n",
       "      <td>2020-03-15</td>\n",
       "      <td>3085</td>\n",
       "      <td>67794</td>\n",
       "      <td>54288</td>\n",
       "      <td>10421</td>\n",
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       "    <tr>\n",
       "      <th>3415</th>\n",
       "      <td>China</td>\n",
       "      <td>Hubei</td>\n",
       "      <td>2020-03-14</td>\n",
       "      <td>3075</td>\n",
       "      <td>67790</td>\n",
       "      <td>52960</td>\n",
       "      <td>11755</td>\n",
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       "    <tr>\n",
       "      <th>3534</th>\n",
       "      <td>China</td>\n",
       "      <td>Jiangsu</td>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>4331</th>\n",
       "      <td>China</td>\n",
       "      <td>Tibet</td>\n",
       "      <td>2020-03-18</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3136</th>\n",
       "      <td>China</td>\n",
       "      <td>Hebei</td>\n",
       "      <td>2020-01-23</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3135</th>\n",
       "      <td>China</td>\n",
       "      <td>Hebei</td>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3021</th>\n",
       "      <td>China</td>\n",
       "      <td>Guizhou</td>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1862 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Country/Region Province/State       Date  Deaths  Confirmed  Recovered  \\\n",
       "3419          China          Hubei 2020-03-18    3122      67800      56927   \n",
       "3418          China          Hubei 2020-03-17    3111      67799      56003   \n",
       "3417          China          Hubei 2020-03-16    3099      67798      55142   \n",
       "3416          China          Hubei 2020-03-15    3085      67794      54288   \n",
       "3415          China          Hubei 2020-03-14    3075      67790      52960   \n",
       "...             ...            ...        ...     ...        ...        ...   \n",
       "3534          China        Jiangsu 2020-01-22       0          1          0   \n",
       "4331          China          Tibet 2020-03-18       0          1          1   \n",
       "3136          China          Hebei 2020-01-23       1          1          0   \n",
       "3135          China          Hebei 2020-01-22       0          1          0   \n",
       "3021          China        Guizhou 2020-01-22       0          1          0   \n",
       "\n",
       "      Active  \n",
       "3419    7751  \n",
       "3418    8685  \n",
       "3417    9557  \n",
       "3416   10421  \n",
       "3415   11755  \n",
       "...      ...  \n",
       "3534       1  \n",
       "4331       0  \n",
       "3136       0  \n",
       "3135       1  \n",
       "3021       1  \n",
       "\n",
       "[1862 rows x 7 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped = dataset.groupby([\"Country/Region\", \"Province/State\", \"Date\"])[\"Deaths\", \"Confirmed\", \"Recovered\", \"Active\"].sum().reset_index()\n",
    "grouped = grouped.sort_values(by='Confirmed', ascending=False)\n",
    "grouped = grouped.loc[grouped.sum(axis=1)>0]\n",
    "grouped\n",
    "\n",
    "# lets see for china how many cases are reported till now\n",
    "china = grouped.loc[grouped['Country/Region']=='China']\n",
    "print(china.sum(axis=0).drop(['Country/Region','Province/State']))\n",
    "china"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<H2> Get the data for cases reported and their status - Active, Death or Recovered on last date in dataset</H2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Cases reported in World on  2020-03-18 00:00:00\n",
      "Deaths         8733\n",
      "Confirmed    214910\n",
      "Recovered     83207\n",
      "Active       122970\n",
      "dtype: int64\n",
      "********************************\n",
      "Total Cases reported in China on 2020-03-18 00:00:00\n",
      "Deaths        3241\n",
      "Confirmed    81102\n",
      "Recovered    69755\n",
      "Active        8106\n",
      "dtype: int64\n",
      "********************************\n",
      "Total Cases reported in US on 2020-03-18 00:00:00\n",
      "Deaths        118\n",
      "Confirmed    7783\n",
      "Recovered       0\n",
      "Active       7665\n",
      "dtype: int64\n",
      "********************************\n",
      "Total Cases reported in Itlay on 2020-03-18 00:00:00\n",
      "Deaths       0\n",
      "Confirmed    0\n",
      "Recovered    0\n",
      "Active       0\n",
      "dtype: int64\n",
      "********************************\n",
      "Total Cases reported in India on 2020-03-18 00:00:00\n",
      "Deaths         3\n",
      "Confirmed    156\n",
      "Recovered     14\n",
      "Active       139\n",
      "dtype: int64\n",
      "********************************\n",
      "Total Cases reported in Iran on 2020-03-18 00:00:00\n",
      "Deaths        1135\n",
      "Confirmed    17361\n",
      "Recovered     5389\n",
      "Active       10837\n",
      "dtype: int64\n",
      "********************************\n"
     ]
    }
   ],
   "source": [
    "latestCase = dataset.groupby([\"Country/Region\", \"Province/State\", \"Date\"])[\"Deaths\", \"Confirmed\", \"Recovered\", \"Active\"].sum().sort_values(by='Date', ascending=False).reset_index()\n",
    "latestCase = latestCase[latestCase['Date'] == max(latestCase['Date'])]\n",
    "nonZeroLatestCase = latestCase[latestCase.sum(axis = 1) > 0]\n",
    "zeroLatestCase = latestCase.merge(nonZeroLatestCase, how = 'outer' ,indicator=True).loc[lambda x:x['_merge'] == 'left_only']\n",
    "\n",
    "print(\"Total Cases reported in World on \", max(latestCase['Date']))\n",
    "print(nonZeroLatestCase.drop(['Country/Region', 'Province/State', 'Date'], axis=1).sum(axis=0)) # gives total no.\n",
    "print(\"********************************\")\n",
    "\n",
    "countries = ['China', 'US', 'Itlay', 'India', 'Iran']\n",
    "for _country in countries:\n",
    "    print(\"Total Cases reported in {} on {}\".format(_country, max(latestCase['Date'])))\n",
    "    country = nonZeroLatestCase.loc[nonZeroLatestCase['Country/Region']== _country].sort_values(\"Confirmed\", ascending=False)\n",
    "    print(country.drop(['Country/Region', 'Province/State', 'Date'], axis = 1).sum(axis=0))\n",
    "    print(\"********************************\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<H2> Finding correlation between Confirmed, Deaths, Recovered</H2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>Date</th>\n",
       "      <th>Confirmed</th>\n",
       "      <th>Deaths</th>\n",
       "      <th>Recovered</th>\n",
       "      <th>Active</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>554</td>\n",
       "      <td>17</td>\n",
       "      <td>28</td>\n",
       "      <td>509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-01-23</td>\n",
       "      <td>652</td>\n",
       "      <td>18</td>\n",
       "      <td>30</td>\n",
       "      <td>604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-01-24</td>\n",
       "      <td>939</td>\n",
       "      <td>26</td>\n",
       "      <td>36</td>\n",
       "      <td>877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-01-25</td>\n",
       "      <td>1432</td>\n",
       "      <td>42</td>\n",
       "      <td>39</td>\n",
       "      <td>1351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-01-26</td>\n",
       "      <td>2113</td>\n",
       "      <td>56</td>\n",
       "      <td>52</td>\n",
       "      <td>2005</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date  Confirmed  Deaths  Recovered  Active\n",
       "0 2020-01-22        554      17         28     509\n",
       "1 2020-01-23        652      18         30     604\n",
       "2 2020-01-24        939      26         36     877\n",
       "3 2020-01-25       1432      42         39    1351\n",
       "4 2020-01-26       2113      56         52    2005"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datasetByDate = dataset.drop(['Lat', 'Long'], axis=1).groupby('Date').sum().reset_index()\n",
    "datasetByDate.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9885433992064101"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datasetByDate['Confirmed'].corr(datasetByDate['Deaths'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9359240365976017"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datasetByDate['Confirmed'].corr(datasetByDate['Recovered'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.952295860725684"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datasetByDate['Recovered'].corr(datasetByDate['Deaths'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "why recovered and death is coming out +ve? shouldn't it be -ve as no. of deaths is inversely proportional to recovered? Is there as lurking variable? May be its increase in no. of confirmed cases? Lets plot the data against each other."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<H2>Visualizing the current state in United States</H2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "        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>Date</th>\n",
       "      <th>Confirmed</th>\n",
       "      <th>Deaths</th>\n",
       "      <th>Recovered</th>\n",
       "      <th>Active</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9045</th>\n",
       "      <td>Diamond Princess</td>\n",
       "      <td>2020-02-24</td>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9316</th>\n",
       "      <td>Diamond Princess</td>\n",
       "      <td>2020-02-25</td>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9587</th>\n",
       "      <td>Diamond Princess</td>\n",
       "      <td>2020-02-26</td>\n",
       "      <td>42</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9858</th>\n",
       "      <td>Diamond Princess</td>\n",
       "      <td>2020-02-27</td>\n",
       "      <td>42</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10129</th>\n",
       "      <td>Diamond Princess</td>\n",
       "      <td>2020-02-28</td>\n",
       "      <td>44</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15329</th>\n",
       "      <td>Wyoming</td>\n",
       "      <td>2020-03-18</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15399</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>2020-03-18</td>\n",
       "      <td>46</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15402</th>\n",
       "      <td>Puerto Rico</td>\n",
       "      <td>2020-03-18</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15416</th>\n",
       "      <td>Guam</td>\n",
       "      <td>2020-03-18</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15435</th>\n",
       "      <td>Virgin Islands</td>\n",
       "      <td>2020-03-18</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>478 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Province/State       Date  Confirmed  Deaths  Recovered  Active\n",
       "9045   Diamond Princess 2020-02-24         36       0          0      36\n",
       "9316   Diamond Princess 2020-02-25         36       0          0      36\n",
       "9587   Diamond Princess 2020-02-26         42       0          0      42\n",
       "9858   Diamond Princess 2020-02-27         42       0          0      42\n",
       "10129  Diamond Princess 2020-02-28         44       0          0      44\n",
       "...                 ...        ...        ...     ...        ...     ...\n",
       "15329           Wyoming 2020-03-18         15       0          0      15\n",
       "15399           Alabama 2020-03-18         46       0          0      46\n",
       "15402       Puerto Rico 2020-03-18          5       0          0       5\n",
       "15416              Guam 2020-03-18          5       0          0       5\n",
       "15435    Virgin Islands 2020-03-18          2       0          0       2\n",
       "\n",
       "[478 rows x 6 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "us = dataset[dataset['Country/Region']=='US'].drop(['Country/Region','Lat', 'Long'], axis=1)\n",
    "us = us[us.sum(axis = 1) > 0]\n",
    "us"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<H2> States with Covid-19 case confirmed</H2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
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     "metadata": {},
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   ],
   "source": [
    "cases = us\n",
    "cases = cases.groupby(['Province/State'])['Confirmed'].sum().reset_index()\n",
    "cases = cases[cases['Confirmed'] > 0]\n",
    "fig = px.bar(cases, x='Province/State', y='Confirmed', title='State wise confirmed cases of COVID-19', text='Confirmed')\n",
    "fig.show()"
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  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<H2> States with Deaths reported till now and the number of it reported</H2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
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   "source": [
    "cases = us\n",
    "cases = cases.groupby(['Province/State'])['Deaths'].sum().reset_index()\n",
    "cases = cases[cases['Deaths'] > 0]\n",
    "fig = px.bar(cases, x='Province/State', y='Deaths', title='State wise deaths reported of COVID-19', text='Deaths')\n",
    "fig.show()"
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  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<H2> States which have seen recovery </H2> "
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  },
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   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
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   "source": [
    "cases = us\n",
    "cases = cases.groupby(['Province/State'])['Recovered'].sum().reset_index()\n",
    "cases = cases[cases['Recovered'] > 0]\n",
    "fig = px.bar(cases, x='Province/State', y='Recovered', title='State wise recovered cases of COVID-19', text='Recovered')\n",
    "fig.show()"
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  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<H2> State that have cases reported, but no deaths and recovery reported yet</H2>"
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   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
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     "metadata": {},
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   ],
   "source": [
    "cases = us[us['Confirmed']==us['Active']]\n",
    "cases = cases.groupby(['Province/State'])['Confirmed', 'Deaths', 'Recovered', 'Active'].sum().reset_index()\n",
    "cases\n",
    "fig = px.bar(cases, x='Province/State', y='Confirmed', text='Active', title='Total confirmed cases with no deaths or recovery reported till now in US')\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<H2>States where all the confirmed cases died </H2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
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    "cases = us[us['Confirmed']==us['Deaths']]\n",
    "cases = cases.groupby(['Province/State'])['Confirmed', 'Deaths', 'Recovered', 'Active'].sum().reset_index()\n",
    "cases\n",
    "fig = px.bar(cases, x='Province/State', y='Deaths', title='Total confirmed cases with all deaths and no recovery in US')\n",
    "fig.show()"
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   "cell_type": "markdown",
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   "source": [
    "<H2>States where all the confirmed cases recovered </H2>"
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  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
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   "source": [
    "cases = us[us['Confirmed']==us['Recovered']]\n",
    "cases = cases.groupby(['Province/State'])['Confirmed', 'Deaths', 'Recovered', 'Active'].sum().reset_index()\n",
    "cases\n",
    "fig = px.bar(cases, x='Province/State', y='Confirmed', title='Total confirmed cases with no deaths and all recovered in US')\n",
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  {
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   "source": [
    "<H2> Combining the above graph in one </H2> "
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   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
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     "metadata": {},
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   ],
   "source": [
    "cases = us\n",
    "cases = cases.groupby(['Province/State'])['Confirmed', 'Deaths', 'Recovered', 'Active'].sum().reset_index()\n",
    "cases = pd.melt(cases, id_vars='Province/State', value_vars=['Confirmed', 'Deaths', 'Recovered', 'Active'], value_name='Count', var_name='Case')\n",
    "fig = px.bar(cases, x='Province/State', y='Count', text='Count', barmode='group', color='Case', title='State wise status of COVID-19')\n",
    "fig.show()"
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  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<H2> Visualizing the cases trend over time </H2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "cases = us\n",
    "cases = cases.groupby(['Date'])['Confirmed', 'Deaths', 'Recovered', 'Active'].sum().reset_index()\n",
    "cases = cases[cases.sum(axis=1)>0]"
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<H2> Confirmed cases trend</H2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
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   ],
   "source": [
    "fig = px.line(cases, x='Date', y='Confirmed', text='Confirmed', title='Over time confirmed status of COVID-19')\n",
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<H2> Deaths trend</H2>"
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  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
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    "<H2> Recovery trend </H2>"
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   "execution_count": 23,
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   "source": [
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       "                        {\"responsive\": true}\n",
       "                    ).then(function(){\n",
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       "\n",
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       "            </script>\n",
       "        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = px.line(cases, x='Date', y='Active', text='Active', title='Over time active status of COVID-19')\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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