novel-corona-data-analysis-latest-data-update (1).ipynb

{
 "cells": [
  {
   "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/novel-corona-virus-2019-dataset/2019_nCoV_data.csv\n",
      "/kaggle/input/novel-corona-virus-2019-dataset/time_series_2019_ncov_deaths.csv\n",
      "/kaggle/input/novel-corona-virus-2019-dataset/time_series_2019_ncov_confirmed.csv\n",
      "/kaggle/input/novel-corona-virus-2019-dataset/time_series_2019_ncov_recovered.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",
    "\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": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "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 seaborn as sns; sns.set(color_codes=True)  # visualization tool\n",
    "\n",
    "\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
    "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"800\"\n",
       "            height=\"600\"\n",
       "            src=\"https://www.myheatmap.com/maps/PPk1_rfT1jQ%3D\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
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       "<IPython.lib.display.IFrame at 0x7fabb456d5f8>"
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     "execution_count": 3,
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   "source": [
    "from IPython.display import IFrame\n",
    "IFrame('https://www.myheatmap.com/maps/PPk1_rfT1jQ%3D', width=800, height=600)"
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  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><h2>**What is Coronavirus</h2>**</br>\n",
    "2019 Novel Coronavirus (2019-nCoV) is a virus (more specifically, a coronavirus) identified as the cause of an outbreak of respiratory illness first detected in Wuhan, China. Early on, many of the patients in the outbreak in Wuhan, China reportedly had some link to a large seafood and animal market, suggesting animal-to-person spread. However, a growing number of patients reportedly have not had exposure to animal markets, indicating person-to-person spread is occurring. At this time, it’s unclear how easily or sustainably this virus is spreading between people - CDC\n",
    "\n",
    "This dataset has daily level information on the number of affected cases, deaths and recovery from 2019 novel coronavirus.\n",
    "\n",
    "The data is available from 22 Jan 2020."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><h2>**Define the Problem**</h2></br>\n",
    "Coronaviruses are a large family of viruses that are common in many different species of animals, including camels, cattle, cats, and bats. Rarely, animal coronaviruses can infect people and then spread between people such as with MERS, SARS, and now with 2019-nCoV.\n",
    "\n",
    "Outbreaks of novel virus infections among people are always of public health concern. The risk from these outbreaks depends on characteristics of the virus, including whether and how well it spreads between people, the severity of resulting illness, and the medical or other measures available to control the impact of the virus (for example, vaccine or treatment medications).\n",
    "\n",
    "This is a very serious public health threat. The fact that this virus has caused severe illness and sustained person-to-person spread in China is concerning, but it’s unclear how the situation in the United States will unfold at this time.\n",
    "\n",
    "The risk to individuals is dependent on exposure. At this time, some people will have an increased risk of infection, for example healthcare workers caring for 2019-nCoV patients and other close contacts. For the general American public, who are unlikely to be exposed to this virus, the immediate health risk from 2019-nCoV is considered low. The goal of the ongoing U.S. public health response is to prevent sustained spread of 2019-nCov in this country.\n",
    "\n",
    "<br><h2>**Precautions**</h2></br>\n",
    "Health authorities and scientists say the same precautions against other viral illnesses can be used: wash your hands frequently, cover up your coughs, try not to touch your face.\n",
    "And anyone who does come down with the virus should be placed in isolation.\n",
    "\"Considering that substantial numbers of patients with SARS and MERS were infected in health-care settings\", precautions need to be taken to prevent that happening again, the Chinese team warned in The Lancet."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>If you like my work Kindly **upvote** the kernel!!</h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "data= pd.read_csv(\"../input/novel-corona-virus-2019-dataset/2019_nCoV_data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sno</th>\n",
       "      <th>Date</th>\n",
       "      <th>Province/State</th>\n",
       "      <th>Country</th>\n",
       "      <th>Last Update</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>1</td>\n",
       "      <td>01/22/2020 12:00:00</td>\n",
       "      <td>Anhui</td>\n",
       "      <td>China</td>\n",
       "      <td>01/22/2020 12:00:00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>01/22/2020 12:00:00</td>\n",
       "      <td>Beijing</td>\n",
       "      <td>China</td>\n",
       "      <td>01/22/2020 12:00:00</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>01/22/2020 12:00:00</td>\n",
       "      <td>Chongqing</td>\n",
       "      <td>China</td>\n",
       "      <td>01/22/2020 12:00:00</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>01/22/2020 12:00:00</td>\n",
       "      <td>Fujian</td>\n",
       "      <td>China</td>\n",
       "      <td>01/22/2020 12:00:00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>01/22/2020 12:00:00</td>\n",
       "      <td>Gansu</td>\n",
       "      <td>China</td>\n",
       "      <td>01/22/2020 12:00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "  </tbody>\n",
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      ],
      "text/plain": [
       "   Sno                 Date Province/State Country          Last Update  \\\n",
       "0    1  01/22/2020 12:00:00          Anhui   China  01/22/2020 12:00:00   \n",
       "1    2  01/22/2020 12:00:00        Beijing   China  01/22/2020 12:00:00   \n",
       "2    3  01/22/2020 12:00:00      Chongqing   China  01/22/2020 12:00:00   \n",
       "3    4  01/22/2020 12:00:00         Fujian   China  01/22/2020 12:00:00   \n",
       "4    5  01/22/2020 12:00:00          Gansu   China  01/22/2020 12:00:00   \n",
       "\n",
       "   Confirmed  Deaths  Recovered  \n",
       "0        1.0     0.0        0.0  \n",
       "1       14.0     0.0        0.0  \n",
       "2        6.0     0.0        0.0  \n",
       "3        1.0     0.0        0.0  \n",
       "4        0.0     0.0        0.0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1199 entries, 0 to 1198\n",
      "Data columns (total 8 columns):\n",
      "Sno               1199 non-null int64\n",
      "Date              1199 non-null object\n",
      "Province/State    888 non-null object\n",
      "Country           1199 non-null object\n",
      "Last Update       1199 non-null object\n",
      "Confirmed         1199 non-null float64\n",
      "Deaths            1199 non-null float64\n",
      "Recovered         1199 non-null float64\n",
      "dtypes: float64(3), int64(1), object(4)\n",
      "memory usage: 75.1+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1199, 8)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sno</th>\n",
       "      <th>Confirmed</th>\n",
       "      <th>Deaths</th>\n",
       "      <th>Recovered</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1199.000000</td>\n",
       "      <td>1199.000000</td>\n",
       "      <td>1199.000000</td>\n",
       "      <td>1199.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>600.000000</td>\n",
       "      <td>276.213511</td>\n",
       "      <td>5.961635</td>\n",
       "      <td>14.617181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>346.265794</td>\n",
       "      <td>1966.264622</td>\n",
       "      <td>58.082724</td>\n",
       "      <td>103.959136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>300.500000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>600.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>899.500000</td>\n",
       "      <td>82.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1199.000000</td>\n",
       "      <td>31728.000000</td>\n",
       "      <td>974.000000</td>\n",
       "      <td>2222.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Sno     Confirmed       Deaths    Recovered\n",
       "count  1199.000000   1199.000000  1199.000000  1199.000000\n",
       "mean    600.000000    276.213511     5.961635    14.617181\n",
       "std     346.265794   1966.264622    58.082724   103.959136\n",
       "min       1.000000      0.000000     0.000000     0.000000\n",
       "25%     300.500000      2.000000     0.000000     0.000000\n",
       "50%     600.000000     10.000000     0.000000     0.000000\n",
       "75%     899.500000     82.000000     0.000000     2.000000\n",
       "max    1199.000000  31728.000000   974.000000  2222.000000"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sno                 0\n",
       "Date                0\n",
       "Province/State    311\n",
       "Country             0\n",
       "Last Update         0\n",
       "Confirmed           0\n",
       "Deaths              0\n",
       "Recovered           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#this shows we have empty values in province/state field"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#data[['Confirmed', 'Deaths', 'Recovered']].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's get rid of the Sno column as it's redundant\n",
    "data.drop(['Sno'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Date'] = data['Date'].apply(pd.to_datetime)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Last Update'] =data['Last Update'].apply(pd.to_datetime)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Date</th>\n",
       "      <th>Province/State</th>\n",
       "      <th>Country</th>\n",
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       "    <tr>\n",
       "      <th>1194</th>\n",
       "      <td>2020-02-10 19:30:00</td>\n",
       "      <td>Los Angeles, CA</td>\n",
       "      <td>US</td>\n",
       "      <td>2020-02-01 19:53:03</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1195</th>\n",
       "      <td>2020-02-10 19:30:00</td>\n",
       "      <td>Madison, WI</td>\n",
       "      <td>US</td>\n",
       "      <td>2020-02-05 21:53:02</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1196</th>\n",
       "      <td>2020-02-10 19:30:00</td>\n",
       "      <td>Orange, CA</td>\n",
       "      <td>US</td>\n",
       "      <td>2020-02-01 19:53:03</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1197</th>\n",
       "      <td>2020-02-10 19:30:00</td>\n",
       "      <td>Seattle, WA</td>\n",
       "      <td>US</td>\n",
       "      <td>2020-02-09 07:03:04</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1198</th>\n",
       "      <td>2020-02-10 19:30:00</td>\n",
       "      <td>Tempe, AZ</td>\n",
       "      <td>US</td>\n",
       "      <td>2020-02-01 19:43:03</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Date   Province/State Country         Last Update  \\\n",
       "1194 2020-02-10 19:30:00  Los Angeles, CA      US 2020-02-01 19:53:03   \n",
       "1195 2020-02-10 19:30:00      Madison, WI      US 2020-02-05 21:53:02   \n",
       "1196 2020-02-10 19:30:00       Orange, CA      US 2020-02-01 19:53:03   \n",
       "1197 2020-02-10 19:30:00      Seattle, WA      US 2020-02-09 07:03:04   \n",
       "1198 2020-02-10 19:30:00        Tempe, AZ      US 2020-02-01 19:43:03   \n",
       "\n",
       "      Confirmed  Deaths  Recovered  \n",
       "1194        1.0     0.0        0.0  \n",
       "1195        1.0     0.0        0.0  \n",
       "1196        1.0     0.0        0.0  \n",
       "1197        1.0     0.0        1.0  \n",
       "1198        1.0     0.0        0.0  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Macau                           20\n",
       "Guizhou                         20\n",
       "Shandong                        20\n",
       "Hainan                          20\n",
       "Guangdong                       20\n",
       "Anhui                           20\n",
       "Fujian                          20\n",
       "Yunnan                          20\n",
       "Inner Mongolia                  20\n",
       "Heilongjiang                    20\n",
       "Liaoning                        20\n",
       "Hebei                           20\n",
       "Hunan                           20\n",
       "Ningxia                         20\n",
       "Shanghai                        20\n",
       "Tianjin                         20\n",
       "Zhejiang                        20\n",
       "Guangxi                         20\n",
       "Gansu                           20\n",
       "Taiwan                          20\n",
       "Beijing                         20\n",
       "Chongqing                       20\n",
       "Jiangsu                         20\n",
       "Qinghai                         20\n",
       "Sichuan                         20\n",
       "Shaanxi                         20\n",
       "Xinjiang                        20\n",
       "Hubei                           20\n",
       "Jiangxi                         20\n",
       "Hong Kong                       20\n",
       "Henan                           20\n",
       "Jilin                           20\n",
       "Shanxi                          20\n",
       "Victoria                        15\n",
       "New South Wales                 15\n",
       "Tibet                           15\n",
       "British Columbia                14\n",
       "Queensland                      13\n",
       "Orange, CA                      10\n",
       "Tempe, AZ                       10\n",
       "Chicago, IL                     10\n",
       "Seattle, WA                     10\n",
       "South Australia                 10\n",
       "Washington                      10\n",
       "Los Angeles, CA                 10\n",
       "Boston, MA                      10\n",
       "Santa Clara, CA                 10\n",
       "San Benito, CA                   8\n",
       "London, ON                       8\n",
       "Toronto, ON                      8\n",
       "Ontario                          8\n",
       "Illinois                         7\n",
       "California                       6\n",
       "Arizona                          6\n",
       "Madison, WI                      5\n",
       "Bavaria                          5\n",
       "Diamond Princess cruise ship     2\n",
       "Cruise Ship                      2\n",
       "Chicago                          1\n",
       "Name: Province/State, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Province/State'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>**Implementing Data Exploration**</h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['China', 'US', 'Japan', 'Thailand', 'South Korea', 'Mainland China', 'Hong Kong', 'Macau', 'Taiwan', 'Singapore', 'Philippines', 'Malaysia', 'Vietnam', 'Australia', 'Mexico', 'Brazil', 'France', 'Nepal', 'Canada', 'Cambodia', 'Sri Lanka', 'Ivory Coast', 'Germany', 'Finland', 'United Arab Emirates', 'India', 'Italy', 'Sweden', 'Russia', 'Spain', 'UK', 'Belgium', 'Others']\n",
      "\n",
      "Total countries affected by virus:  33\n"
     ]
    }
   ],
   "source": [
    "# Countries affected\n",
    "\n",
    "countries = data['Country'].unique().tolist()\n",
    "print(countries)\n",
    "\n",
    "print(\"\\nTotal countries affected by virus: \",len(countries))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['China', 'US', 'Japan', 'Thailand', 'South Korea', 'Hong Kong', 'Macau', 'Taiwan', 'Singapore', 'Philippines', 'Malaysia', 'Vietnam', 'Australia', 'Mexico', 'Brazil', 'France', 'Nepal', 'Canada', 'Cambodia', 'Sri Lanka', 'Ivory Coast', 'Germany', 'Finland', 'United Arab Emirates', 'India', 'Italy', 'Sweden', 'Russia', 'Spain', 'UK', 'Belgium', 'Others']\n",
      "\n",
      "Total countries affected by virus:  32\n"
     ]
    }
   ],
   "source": [
    "#Combining China and Mainland China cases\n",
    "\n",
    "data['Country'].replace({'Mainland China':'China'},inplace=True)\n",
    "countries = data['Country'].unique().tolist()\n",
    "print(countries)\n",
    "print(\"\\nTotal countries affected by virus: \",len(countries))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>**Latest_Data_On_nCoV Virus**</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>Province/State</th>\n",
       "      <th>Country</th>\n",
       "      <th>Last Update</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>2020-01-22 12:00:00</td>\n",
       "      <td>Anhui</td>\n",
       "      <td>China</td>\n",
       "      <td>2020-01-22 12:00:00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-01-22 12:00:00</td>\n",
       "      <td>Beijing</td>\n",
       "      <td>China</td>\n",
       "      <td>2020-01-22 12:00:00</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-01-22 12:00:00</td>\n",
       "      <td>Chongqing</td>\n",
       "      <td>China</td>\n",
       "      <td>2020-01-22 12:00:00</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-01-22 12:00:00</td>\n",
       "      <td>Fujian</td>\n",
       "      <td>China</td>\n",
       "      <td>2020-01-22 12:00:00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-01-22 12:00:00</td>\n",
       "      <td>Gansu</td>\n",
       "      <td>China</td>\n",
       "      <td>2020-01-22 12:00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</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",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1194</th>\n",
       "      <td>2020-02-10 19:30:00</td>\n",
       "      <td>Los Angeles, CA</td>\n",
       "      <td>US</td>\n",
       "      <td>2020-02-01 19:53:03</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1195</th>\n",
       "      <td>2020-02-10 19:30:00</td>\n",
       "      <td>Madison, WI</td>\n",
       "      <td>US</td>\n",
       "      <td>2020-02-05 21:53:02</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1196</th>\n",
       "      <td>2020-02-10 19:30:00</td>\n",
       "      <td>Orange, CA</td>\n",
       "      <td>US</td>\n",
       "      <td>2020-02-01 19:53:03</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1197</th>\n",
       "      <td>2020-02-10 19:30:00</td>\n",
       "      <td>Seattle, WA</td>\n",
       "      <td>US</td>\n",
       "      <td>2020-02-09 07:03:04</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1198</th>\n",
       "      <td>2020-02-10 19:30:00</td>\n",
       "      <td>Tempe, AZ</td>\n",
       "      <td>US</td>\n",
       "      <td>2020-02-01 19:43:03</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1199 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Date   Province/State Country         Last Update  \\\n",
       "0    2020-01-22 12:00:00            Anhui   China 2020-01-22 12:00:00   \n",
       "1    2020-01-22 12:00:00          Beijing   China 2020-01-22 12:00:00   \n",
       "2    2020-01-22 12:00:00        Chongqing   China 2020-01-22 12:00:00   \n",
       "3    2020-01-22 12:00:00           Fujian   China 2020-01-22 12:00:00   \n",
       "4    2020-01-22 12:00:00            Gansu   China 2020-01-22 12:00:00   \n",
       "...                  ...              ...     ...                 ...   \n",
       "1194 2020-02-10 19:30:00  Los Angeles, CA      US 2020-02-01 19:53:03   \n",
       "1195 2020-02-10 19:30:00      Madison, WI      US 2020-02-05 21:53:02   \n",
       "1196 2020-02-10 19:30:00       Orange, CA      US 2020-02-01 19:53:03   \n",
       "1197 2020-02-10 19:30:00      Seattle, WA      US 2020-02-09 07:03:04   \n",
       "1198 2020-02-10 19:30:00        Tempe, AZ      US 2020-02-01 19:43:03   \n",
       "\n",
       "      Confirmed  Deaths  Recovered  \n",
       "0           1.0     0.0        0.0  \n",
       "1          14.0     0.0        0.0  \n",
       "2           6.0     0.0        0.0  \n",
       "3           1.0     0.0        0.0  \n",
       "4           0.0     0.0        0.0  \n",
       "...         ...     ...        ...  \n",
       "1194        1.0     0.0        0.0  \n",
       "1195        1.0     0.0        0.0  \n",
       "1196        1.0     0.0        0.0  \n",
       "1197        1.0     0.0        1.0  \n",
       "1198        1.0     0.0        0.0  \n",
       "\n",
       "[1199 rows x 7 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import date\n",
    "d = data['Date'][-1:].astype('str')\n",
    "year = int(d.values[0].split('-')[0])\n",
    "month = int(d.values[0].split('-')[1])\n",
    "day = int(d.values[0].split('-')[2].split()[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "latest_nCoV_data = data[data['Date'] > pd.Timestamp(date(year,month,day))]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>Province/State</th>\n",
       "      <th>Country</th>\n",
       "      <th>Last Update</th>\n",
       "      <th>Confirmed</th>\n",
       "      <th>Deaths</th>\n",
       "      <th>Recovered</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1194</th>\n",
       "      <td>2020-02-10 19:30:00</td>\n",
       "      <td>Los Angeles, CA</td>\n",
       "      <td>US</td>\n",
       "      <td>2020-02-01 19:53:03</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1195</th>\n",
       "      <td>2020-02-10 19:30:00</td>\n",
       "      <td>Madison, WI</td>\n",
       "      <td>US</td>\n",
       "      <td>2020-02-05 21:53:02</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1196</th>\n",
       "      <td>2020-02-10 19:30:00</td>\n",
       "      <td>Orange, CA</td>\n",
       "      <td>US</td>\n",
       "      <td>2020-02-01 19:53:03</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1197</th>\n",
       "      <td>2020-02-10 19:30:00</td>\n",
       "      <td>Seattle, WA</td>\n",
       "      <td>US</td>\n",
       "      <td>2020-02-09 07:03:04</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1198</th>\n",
       "      <td>2020-02-10 19:30:00</td>\n",
       "      <td>Tempe, AZ</td>\n",
       "      <td>US</td>\n",
       "      <td>2020-02-01 19:43:03</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Date   Province/State Country         Last Update  \\\n",
       "1194 2020-02-10 19:30:00  Los Angeles, CA      US 2020-02-01 19:53:03   \n",
       "1195 2020-02-10 19:30:00      Madison, WI      US 2020-02-05 21:53:02   \n",
       "1196 2020-02-10 19:30:00       Orange, CA      US 2020-02-01 19:53:03   \n",
       "1197 2020-02-10 19:30:00      Seattle, WA      US 2020-02-09 07:03:04   \n",
       "1198 2020-02-10 19:30:00        Tempe, AZ      US 2020-02-01 19:43:03   \n",
       "\n",
       "      Confirmed  Deaths  Recovered  \n",
       "1194        1.0     0.0        0.0  \n",
       "1195        1.0     0.0        0.0  \n",
       "1196        1.0     0.0        0.0  \n",
       "1197        1.0     0.0        1.0  \n",
       "1198        1.0     0.0        0.0  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Data Glimpse\n",
    "latest_nCoV_data.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confirmed Cases around the globe :  42767.0\n",
      "Deaths Confirmed around the globe:  1013.0\n",
      "Recovered Cases around the globe :  3950.0\n"
     ]
    }
   ],
   "source": [
    "# Getting the latest numbers\n",
    "\n",
    "#formatted_text('***Latest Numbers Globaly***')\n",
    "print('Confirmed Cases around the globe : ',latest_nCoV_data['Confirmed'].sum())\n",
    "print('Deaths Confirmed around the globe: ',latest_nCoV_data['Deaths'].sum())\n",
    "print('Recovered Cases around the globe : ',latest_nCoV_data['Recovered'].sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0              Anhui\n",
      "1            Beijing\n",
      "2          Chongqing\n",
      "3             Fujian\n",
      "4              Gansu\n",
      "5          Guangdong\n",
      "6            Guangxi\n",
      "7            Guizhou\n",
      "8             Hainan\n",
      "9              Hebei\n",
      "10      Heilongjiang\n",
      "11             Henan\n",
      "12         Hong Kong\n",
      "13             Hubei\n",
      "14             Hunan\n",
      "15    Inner Mongolia\n",
      "16           Jiangsu\n",
      "17           Jiangxi\n",
      "18             Jilin\n",
      "19          Liaoning\n",
      "20             Macau\n",
      "21           Ningxia\n",
      "22           Qinghai\n",
      "23           Shaanxi\n",
      "24          Shandong\n",
      "25          Shanghai\n",
      "26            Shanxi\n",
      "27           Sichuan\n",
      "28            Taiwan\n",
      "29           Tianjin\n",
      "30          Xinjiang\n",
      "31            Yunnan\n",
      "32          Zhejiang\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "tempState = data['Province/State'].mode()\n",
    "print(tempState)\n",
    "#df['Province/State'].fillna(tempState, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Converting\n",
    "<br>Convert the Date Time stamp to Date</br>\n",
    "**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import datetime\n",
    "# 1/22/2020 12:00\n",
    "# 1/26/2020 23:00\n",
    "# 1/23/20 12:00 PM\n",
    "# 2020-01-02 23:33:00\n",
    "# \n",
    "def try_parsing_date_time(text):\n",
    "    for fmt in ('%Y-%m-%d', '%d.%m.%Y', '%m/%d/%Y %h:%m', '%m/%d/%Y %H:%M','%m/%d/%Y %H:%M:%S','%m/%d/%y %I:%M %p', '%m/%d/%Y %I:%M %p', '%Y-%d-%m %H:%M:%S'):\n",
    "        try:\n",
    "            return datetime.strptime(text, fmt)\n",
    "        except ValueError:\n",
    "            pass\n",
    "    raise ValueError('no valid date time format found', text)\n",
    "\n",
    "\n",
    "def try_parsing_date(text):\n",
    "    for fmt in ('%m/%d/%Y', '%m/%d/%y', '%Y-%d-%m', '%d.%m.%Y'):\n",
    "        try:\n",
    "            return datetime.strptime(text, fmt)\n",
    "        except ValueError:\n",
    "            pass\n",
    "    raise ValueError('no valid date format found', text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      2020-01-22 12:00:00\n",
       "1      2020-01-22 12:00:00\n",
       "2      2020-01-22 12:00:00\n",
       "3      2020-01-22 12:00:00\n",
       "4      2020-01-22 12:00:00\n",
       "               ...        \n",
       "1194   2020-02-10 19:30:00\n",
       "1195   2020-02-10 19:30:00\n",
       "1196   2020-02-10 19:30:00\n",
       "1197   2020-02-10 19:30:00\n",
       "1198   2020-02-10 19:30:00\n",
       "Name: Date, Length: 1199, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Date']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      2020-01-22 12:00:00\n",
       "1      2020-01-22 12:00:00\n",
       "2      2020-01-22 12:00:00\n",
       "3      2020-01-22 12:00:00\n",
       "4      2020-01-22 12:00:00\n",
       "               ...        \n",
       "1194   2020-02-10 19:30:00\n",
       "1195   2020-02-10 19:30:00\n",
       "1196   2020-02-10 19:30:00\n",
       "1197   2020-02-10 19:30:00\n",
       "1198   2020-02-10 19:30:00\n",
       "Name: Date, Length: 1199, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Date']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['China', 'Others', 'Singapore', 'Hong Kong', 'Thailand', 'South Korea', 'Japan', 'Malaysia', 'Taiwan', 'Germany', 'Vietnam', 'France', 'Macau', 'UK', 'United Arab Emirates', 'Australia', 'Canada', 'India', 'Italy', 'Philippines', 'Russia', 'Spain', 'US', 'Belgium', 'Cambodia', 'Finland', 'Nepal', 'Sri Lanka', 'Sweden']\n",
      "\n",
      "Total countries affected by virus:  29\n"
     ]
    }
   ],
   "source": [
    "#No. Of Countries Currently affected by it.\n",
    "allCountries = latest_nCoV_data['Country'].unique().tolist()\n",
    "print(allCountries)\n",
    "\n",
    "print(\"\\nTotal countries affected by virus: \",len(allCountries))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>Country</th>\n",
       "      <th>Confirmed</th>\n",
       "      <th>Deaths</th>\n",
       "      <th>Recovered</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Australia</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Belgium</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Cambodia</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Canada</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>China</td>\n",
       "      <td>42310.0</td>\n",
       "      <td>1011.0</td>\n",
       "      <td>3921.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Finland</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>France</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Germany</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Hong Kong</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>India</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Italy</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Japan</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Macau</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Malaysia</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Nepal</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Others</td>\n",
       "      <td>135.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Philippines</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Russia</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Singapore</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>South Korea</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Spain</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Sri Lanka</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Sweden</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Taiwan</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Thailand</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>UK</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>US</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Vietnam</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Country  Confirmed  Deaths  Recovered\n",
       "1              Australia       15.0     0.0        2.0\n",
       "2                Belgium        1.0     0.0        0.0\n",
       "3               Cambodia        1.0     0.0        0.0\n",
       "4                 Canada        7.0     0.0        0.0\n",
       "5                  China    42310.0  1011.0     3921.0\n",
       "6                Finland        1.0     0.0        0.0\n",
       "7                 France       11.0     0.0        0.0\n",
       "8                Germany       14.0     0.0        0.0\n",
       "9              Hong Kong       38.0     1.0        0.0\n",
       "10                 India        3.0     0.0        0.0\n",
       "11                 Italy        3.0     0.0        0.0\n",
       "12                 Japan       26.0     0.0        4.0\n",
       "13                 Macau       10.0     0.0        1.0\n",
       "14              Malaysia       18.0     0.0        1.0\n",
       "15                 Nepal        1.0     0.0        0.0\n",
       "16                Others      135.0     0.0        0.0\n",
       "17           Philippines        3.0     1.0        0.0\n",
       "18                Russia        2.0     0.0        0.0\n",
       "19             Singapore       45.0     0.0        2.0\n",
       "20           South Korea       27.0     0.0        3.0\n",
       "21                 Spain        2.0     0.0        0.0\n",
       "22             Sri Lanka        1.0     0.0        1.0\n",
       "23                Sweden        1.0     0.0        0.0\n",
       "24                Taiwan       18.0     0.0        1.0\n",
       "25              Thailand       32.0     0.0       10.0\n",
       "26                    UK        8.0     0.0        0.0\n",
       "27                    US       12.0     0.0        3.0\n",
       "28  United Arab Emirates        8.0     0.0        0.0\n",
       "29               Vietnam       14.0     0.0        1.0"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CountryWiseData = pd.DataFrame(latest_nCoV_data.groupby('Country')['Confirmed', 'Deaths', 'Recovered'].sum())\n",
    "CountryWiseData['Country'] = CountryWiseData.index\n",
    "CountryWiseData.index = np.arange(1, len(allCountries)+1)\n",
    "\n",
    "CountryWiseData = CountryWiseData[['Country','Confirmed', 'Deaths', 'Recovered']]\n",
    "\n",
    "#formatted_text('***Country wise Analysis of ''Confirmed'', ''Deaths'', ''Recovered'' Cases***')\n",
    "CountryWiseData"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>**Implementing EDA**</h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fab97b143c8>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2160x1440 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#formatted_text('***Country wise Analysis of ''Confirmed'', ''Deaths'', ''Recovered'' Cases***')\n",
    "CountryWiseData.plot('Country',['Confirmed', 'Deaths', 'Recovered'],kind='bar',figsize=(30,20), fontsize=18)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Implementing EDA on the data**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>**Day wise distribution (WorldWide) for Confirmed, Deaths and Recovered Cases**</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-01-22 12:00:00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-01-22 12:00:00</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-01-22 12:00:00</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-01-22 12:00:00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-01-22 12:00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Date  Confirmed  Deaths  Recovered\n",
       "0 2020-01-22 12:00:00        1.0     0.0        0.0\n",
       "1 2020-01-22 12:00:00       14.0     0.0        0.0\n",
       "2 2020-01-22 12:00:00        6.0     0.0        0.0\n",
       "3 2020-01-22 12:00:00        1.0     0.0        0.0\n",
       "4 2020-01-22 12:00:00        0.0     0.0        0.0"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "date_wise_data = data[[\"Date\",\"Confirmed\",\"Deaths\",\"Recovered\"]]\n",
    "date_wise_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>555.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-01-23</td>\n",
       "      <td>653.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-01-24</td>\n",
       "      <td>941.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>36.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-01-25</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>49.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-01-26</td>\n",
       "      <td>2794.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>54.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2020-01-27</td>\n",
       "      <td>4473.0</td>\n",
       "      <td>107.0</td>\n",
       "      <td>63.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2020-01-28</td>\n",
       "      <td>6057.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>110.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2020-01-29</td>\n",
       "      <td>7783.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>133.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2020-01-30</td>\n",
       "      <td>9776.0</td>\n",
       "      <td>213.0</td>\n",
       "      <td>187.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2020-01-31</td>\n",
       "      <td>11374.0</td>\n",
       "      <td>259.0</td>\n",
       "      <td>252.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2020-02-01</td>\n",
       "      <td>14549.0</td>\n",
       "      <td>305.0</td>\n",
       "      <td>340.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2020-02-02</td>\n",
       "      <td>17295.0</td>\n",
       "      <td>362.0</td>\n",
       "      <td>487.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2020-02-03</td>\n",
       "      <td>20588.0</td>\n",
       "      <td>426.0</td>\n",
       "      <td>644.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2020-02-04</td>\n",
       "      <td>24503.0</td>\n",
       "      <td>492.0</td>\n",
       "      <td>899.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2020-02-05</td>\n",
       "      <td>24630.0</td>\n",
       "      <td>494.0</td>\n",
       "      <td>1029.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2020-02-06</td>\n",
       "      <td>30806.0</td>\n",
       "      <td>634.0</td>\n",
       "      <td>1487.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2020-02-07</td>\n",
       "      <td>31532.0</td>\n",
       "      <td>638.0</td>\n",
       "      <td>1763.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2020-02-08</td>\n",
       "      <td>37549.0</td>\n",
       "      <td>813.0</td>\n",
       "      <td>2701.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2020-02-09</td>\n",
       "      <td>40536.0</td>\n",
       "      <td>910.0</td>\n",
       "      <td>3312.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2020-02-10</td>\n",
       "      <td>42767.0</td>\n",
       "      <td>1013.0</td>\n",
       "      <td>3950.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Date  Confirmed  Deaths  Recovered\n",
       "0   2020-01-22      555.0     0.0        0.0\n",
       "1   2020-01-23      653.0    18.0       30.0\n",
       "2   2020-01-24      941.0    26.0       36.0\n",
       "3   2020-01-25     2019.0    56.0       49.0\n",
       "4   2020-01-26     2794.0    80.0       54.0\n",
       "5   2020-01-27     4473.0   107.0       63.0\n",
       "6   2020-01-28     6057.0   132.0      110.0\n",
       "7   2020-01-29     7783.0   170.0      133.0\n",
       "8   2020-01-30     9776.0   213.0      187.0\n",
       "9   2020-01-31    11374.0   259.0      252.0\n",
       "10  2020-02-01    14549.0   305.0      340.0\n",
       "11  2020-02-02    17295.0   362.0      487.0\n",
       "12  2020-02-03    20588.0   426.0      644.0\n",
       "13  2020-02-04    24503.0   492.0      899.0\n",
       "14  2020-02-05    24630.0   494.0     1029.0\n",
       "15  2020-02-06    30806.0   634.0     1487.0\n",
       "16  2020-02-07    31532.0   638.0     1763.0\n",
       "17  2020-02-08    37549.0   813.0     2701.0\n",
       "18  2020-02-09    40536.0   910.0     3312.0\n",
       "19  2020-02-10    42767.0  1013.0     3950.0"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "date_wise_data = date_wise_data.groupby([\"Date\"]).sum().reset_index()\n",
    "\n",
    "# strip off the time part from date for day-wise distribution\n",
    "date_wise_data.Date = date_wise_data.Date.apply(lambda x:x.date())\n",
    "\n",
    "#formatted_text('***Day wise distribution (WorldWide) for Confirmed, Deaths and Recovered Cases***')\n",
    "date_wise_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fab94cbebe0>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#*Day wise distribution (WorldWide) for Confirmed, Deaths and Recovered Cases\n",
    "date_wise_data.plot('Date',['Confirmed', 'Deaths', 'Recovered'],kind='bar',figsize=(20,15), fontsize=15, rot=30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>**Distribution in China**</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>Province/State</th>\n",
       "      <th>Country</th>\n",
       "      <th>Last Update</th>\n",
       "      <th>Confirmed</th>\n",
       "      <th>Deaths</th>\n",
       "      <th>Recovered</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1127</th>\n",
       "      <td>2020-02-10 19:30:00</td>\n",
       "      <td>Hubei</td>\n",
       "      <td>China</td>\n",
       "      <td>2020-02-10 23:33:02</td>\n",
       "      <td>31728.0</td>\n",
       "      <td>974.0</td>\n",
       "      <td>2222.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1128</th>\n",
       "      <td>2020-02-10 19:30:00</td>\n",
       "      <td>Guangdong</td>\n",
       "      <td>China</td>\n",
       "      <td>2020-02-10 13:43:02</td>\n",
       "      <td>1159.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>167.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1129</th>\n",
       "      <td>2020-02-10 19:30:00</td>\n",
       "      <td>Zhejiang</td>\n",
       "      <td>China</td>\n",
       "      <td>2020-02-10 14:13:12</td>\n",
       "      <td>1092.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>242.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1130</th>\n",
       "      <td>2020-02-10 19:30:00</td>\n",
       "      <td>Henan</td>\n",
       "      <td>China</td>\n",
       "      <td>2020-02-10 13:53:02</td>\n",
       "      <td>1073.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>191.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1131</th>\n",
       "      <td>2020-02-10 19:30:00</td>\n",
       "      <td>Hunan</td>\n",
       "      <td>China</td>\n",
       "      <td>2020-02-10 13:53:02</td>\n",
       "      <td>879.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>208.0</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",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1194</th>\n",
       "      <td>2020-02-10 19:30:00</td>\n",
       "      <td>Los Angeles, CA</td>\n",
       "      <td>US</td>\n",
       "      <td>2020-02-01 19:53:03</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1195</th>\n",
       "      <td>2020-02-10 19:30:00</td>\n",
       "      <td>Madison, WI</td>\n",
       "      <td>US</td>\n",
       "      <td>2020-02-05 21:53:02</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1196</th>\n",
       "      <td>2020-02-10 19:30:00</td>\n",
       "      <td>Orange, CA</td>\n",
       "      <td>US</td>\n",
       "      <td>2020-02-01 19:53:03</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1197</th>\n",
       "      <td>2020-02-10 19:30:00</td>\n",
       "      <td>Seattle, WA</td>\n",
       "      <td>US</td>\n",
       "      <td>2020-02-09 07:03:04</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1198</th>\n",
       "      <td>2020-02-10 19:30:00</td>\n",
       "      <td>Tempe, AZ</td>\n",
       "      <td>US</td>\n",
       "      <td>2020-02-01 19:43:03</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>72 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Date   Province/State Country         Last Update  \\\n",
       "1127 2020-02-10 19:30:00            Hubei   China 2020-02-10 23:33:02   \n",
       "1128 2020-02-10 19:30:00        Guangdong   China 2020-02-10 13:43:02   \n",
       "1129 2020-02-10 19:30:00         Zhejiang   China 2020-02-10 14:13:12   \n",
       "1130 2020-02-10 19:30:00            Henan   China 2020-02-10 13:53:02   \n",
       "1131 2020-02-10 19:30:00            Hunan   China 2020-02-10 13:53:02   \n",
       "...                  ...              ...     ...                 ...   \n",
       "1194 2020-02-10 19:30:00  Los Angeles, CA      US 2020-02-01 19:53:03   \n",
       "1195 2020-02-10 19:30:00      Madison, WI      US 2020-02-05 21:53:02   \n",
       "1196 2020-02-10 19:30:00       Orange, CA      US 2020-02-01 19:53:03   \n",
       "1197 2020-02-10 19:30:00      Seattle, WA      US 2020-02-09 07:03:04   \n",
       "1198 2020-02-10 19:30:00        Tempe, AZ      US 2020-02-01 19:43:03   \n",
       "\n",
       "      Confirmed  Deaths  Recovered  \n",
       "1127    31728.0   974.0     2222.0  \n",
       "1128     1159.0     1.0      167.0  \n",
       "1129     1092.0     0.0      242.0  \n",
       "1130     1073.0     6.0      191.0  \n",
       "1131      879.0     1.0      208.0  \n",
       "...         ...     ...        ...  \n",
       "1194        1.0     0.0        0.0  \n",
       "1195        1.0     0.0        0.0  \n",
       "1196        1.0     0.0        0.0  \n",
       "1197        1.0     0.0        1.0  \n",
       "1198        1.0     0.0        0.0  \n",
       "\n",
       "[72 rows x 7 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "latest_nCoV_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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>Confirmed</th>\n",
       "      <th>Deaths</th>\n",
       "      <th>Recovered</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Hubei</td>\n",
       "      <td>31728.0</td>\n",
       "      <td>974.0</td>\n",
       "      <td>2222.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Guangdong</td>\n",
       "      <td>1159.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>167.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Zhejiang</td>\n",
       "      <td>1092.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>242.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Henan</td>\n",
       "      <td>1073.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>191.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Hunan</td>\n",
       "      <td>879.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>208.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Anhui</td>\n",
       "      <td>830.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>88.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Jiangxi</td>\n",
       "      <td>771.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>105.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Jiangsu</td>\n",
       "      <td>492.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>81.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Chongqing</td>\n",
       "      <td>486.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>66.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Shandong</td>\n",
       "      <td>466.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>66.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Sichuan</td>\n",
       "      <td>405.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Beijing</td>\n",
       "      <td>337.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>44.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Heilongjiang</td>\n",
       "      <td>331.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Shanghai</td>\n",
       "      <td>299.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>48.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Fujian</td>\n",
       "      <td>261.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>39.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Hebei</td>\n",
       "      <td>218.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>41.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Shaanxi</td>\n",
       "      <td>213.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Guangxi</td>\n",
       "      <td>210.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Yunnan</td>\n",
       "      <td>149.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Hainan</td>\n",
       "      <td>138.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Shanxi</td>\n",
       "      <td>119.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Guizhou</td>\n",
       "      <td>109.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Liaoning</td>\n",
       "      <td>108.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Tianjin</td>\n",
       "      <td>95.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Gansu</td>\n",
       "      <td>86.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Jilin</td>\n",
       "      <td>81.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Inner Mongolia</td>\n",
       "      <td>58.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Ningxia</td>\n",
       "      <td>49.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Xinjiang</td>\n",
       "      <td>49.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Qinghai</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Tibet</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Province/State  Confirmed  Deaths  Recovered\n",
       "1            Hubei    31728.0   974.0     2222.0\n",
       "2        Guangdong     1159.0     1.0      167.0\n",
       "3         Zhejiang     1092.0     0.0      242.0\n",
       "4            Henan     1073.0     6.0      191.0\n",
       "5            Hunan      879.0     1.0      208.0\n",
       "6            Anhui      830.0     3.0       88.0\n",
       "7          Jiangxi      771.0     1.0      105.0\n",
       "8          Jiangsu      492.0     0.0       81.0\n",
       "9        Chongqing      486.0     2.0       66.0\n",
       "10        Shandong      466.0     1.0       66.0\n",
       "11         Sichuan      405.0     1.0       80.0\n",
       "12         Beijing      337.0     2.0       44.0\n",
       "13    Heilongjiang      331.0     7.0       30.0\n",
       "14        Shanghai      299.0     1.0       48.0\n",
       "15          Fujian      261.0     0.0       39.0\n",
       "16           Hebei      218.0     2.0       41.0\n",
       "17         Shaanxi      213.0     0.0       30.0\n",
       "18         Guangxi      210.0     1.0       24.0\n",
       "19          Yunnan      149.0     0.0       19.0\n",
       "20          Hainan      138.0     3.0       19.0\n",
       "21          Shanxi      119.0     0.0       25.0\n",
       "22         Guizhou      109.0     1.0       10.0\n",
       "23        Liaoning      108.0     0.0       13.0\n",
       "24         Tianjin       95.0     1.0        8.0\n",
       "25           Gansu       86.0     2.0       21.0\n",
       "26           Jilin       81.0     1.0       13.0\n",
       "27  Inner Mongolia       58.0     0.0        5.0\n",
       "28         Ningxia       49.0     0.0       13.0\n",
       "29        Xinjiang       49.0     0.0        0.0\n",
       "30         Qinghai       18.0     0.0        3.0\n",
       "31           Tibet        1.0     0.0        0.0"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "china_latest_data = latest_nCoV_data[latest_nCoV_data['Country']=='China'][['Province/State','Confirmed','Deaths','Recovered']]\n",
    "#data[data.Country == 'China'][['Province/State', 'Confirmed']].groupby('Province/State').max()\n",
    "# Reset Index\n",
    "china_latest_data.reset_index(drop=True, inplace=True)\n",
    "china_latest_data.index = pd.RangeIndex(start=1, stop=len(china_latest_data['Province/State']) + 1, step=1)\n",
    "\n",
    "#formatted_text('***Numbers in China for Confirmed, Deaths and Recovered Cases***')\n",
    "\n",
    "# Data Glimpse\n",
    "china_latest_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fab95065a20>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "china_latest_data.plot('Province/State',['Confirmed', 'Deaths', 'Recovered'],kind='bar',figsize=(20,15), fontsize=15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**INFERENCES**\n",
    "\n",
    "<br>Hubei is the worst affected by nCov with almost 95% of cases reported.</br>\n",
    "<br>The recovery from the virus has not been that rapid. As the number of cases being reported is increasing at a very rapid rate and the virus being a new one proper medical treatment not available to treat the virus could the possible reason for the slowed down recovery. This can also be the reason for the number of deaths increasing day by day. The virus does not match any other known virus. This raised concern because when a virus is new, we do not know how it affects people."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>**Rest Of China (Excluding Hubei)**</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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>Confirmed</th>\n",
       "      <th>Deaths</th>\n",
       "      <th>Recovered</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Guangdong</td>\n",
       "      <td>1159.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>167.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Zhejiang</td>\n",
       "      <td>1092.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>242.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Henan</td>\n",
       "      <td>1073.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>191.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Hunan</td>\n",
       "      <td>879.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>208.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Anhui</td>\n",
       "      <td>830.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>88.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Jiangxi</td>\n",
       "      <td>771.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>105.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Jiangsu</td>\n",
       "      <td>492.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>81.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Chongqing</td>\n",
       "      <td>486.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>66.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Shandong</td>\n",
       "      <td>466.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>66.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Sichuan</td>\n",
       "      <td>405.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Beijing</td>\n",
       "      <td>337.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>44.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Heilongjiang</td>\n",
       "      <td>331.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Shanghai</td>\n",
       "      <td>299.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>48.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Fujian</td>\n",
       "      <td>261.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>39.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Hebei</td>\n",
       "      <td>218.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>41.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Shaanxi</td>\n",
       "      <td>213.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Guangxi</td>\n",
       "      <td>210.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Yunnan</td>\n",
       "      <td>149.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Hainan</td>\n",
       "      <td>138.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Shanxi</td>\n",
       "      <td>119.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Guizhou</td>\n",
       "      <td>109.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Liaoning</td>\n",
       "      <td>108.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Tianjin</td>\n",
       "      <td>95.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Gansu</td>\n",
       "      <td>86.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Jilin</td>\n",
       "      <td>81.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Inner Mongolia</td>\n",
       "      <td>58.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Ningxia</td>\n",
       "      <td>49.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Xinjiang</td>\n",
       "      <td>49.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Qinghai</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Tibet</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Province/State  Confirmed  Deaths  Recovered\n",
       "1        Guangdong     1159.0     1.0      167.0\n",
       "2         Zhejiang     1092.0     0.0      242.0\n",
       "3            Henan     1073.0     6.0      191.0\n",
       "4            Hunan      879.0     1.0      208.0\n",
       "5            Anhui      830.0     3.0       88.0\n",
       "6          Jiangxi      771.0     1.0      105.0\n",
       "7          Jiangsu      492.0     0.0       81.0\n",
       "8        Chongqing      486.0     2.0       66.0\n",
       "9         Shandong      466.0     1.0       66.0\n",
       "10         Sichuan      405.0     1.0       80.0\n",
       "11         Beijing      337.0     2.0       44.0\n",
       "12    Heilongjiang      331.0     7.0       30.0\n",
       "13        Shanghai      299.0     1.0       48.0\n",
       "14          Fujian      261.0     0.0       39.0\n",
       "15           Hebei      218.0     2.0       41.0\n",
       "16         Shaanxi      213.0     0.0       30.0\n",
       "17         Guangxi      210.0     1.0       24.0\n",
       "18          Yunnan      149.0     0.0       19.0\n",
       "19          Hainan      138.0     3.0       19.0\n",
       "20          Shanxi      119.0     0.0       25.0\n",
       "21         Guizhou      109.0     1.0       10.0\n",
       "22        Liaoning      108.0     0.0       13.0\n",
       "23         Tianjin       95.0     1.0        8.0\n",
       "24           Gansu       86.0     2.0       21.0\n",
       "25           Jilin       81.0     1.0       13.0\n",
       "26  Inner Mongolia       58.0     0.0        5.0\n",
       "27         Ningxia       49.0     0.0       13.0\n",
       "28        Xinjiang       49.0     0.0        0.0\n",
       "29         Qinghai       18.0     0.0        3.0\n",
       "30           Tibet        1.0     0.0        0.0"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rest_of_China = china_latest_data[china_latest_data['Province/State'] !='Hubei'][[\"Province/State\", \"Confirmed\",\"Deaths\",\"Recovered\"]]\n",
    "\n",
    "# Reset Index to start from 1\n",
    "rest_of_China.reset_index(drop=True, inplace=True)\n",
    "rest_of_China.index = pd.RangeIndex(start=1, stop=len(china_latest_data['Province/State']), step=1)\n",
    "\n",
    "#formatted_text('***Numbers in rest of China for Confirmed, Deaths and Recovered Cases***')\n",
    "\n",
    "# Data Glimpse\n",
    "rest_of_China"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fab94e99198>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x1080 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rest_of_China.plot('Province/State',['Confirmed', 'Deaths', 'Recovered'],kind='bar',figsize=(20,15), fontsize=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Province/State  Confirmed  Deaths  Recovered\n",
      "1      Guangdong     1159.0     1.0      167.0\n",
      "2       Zhejiang     1092.0     0.0      242.0\n",
      "3          Henan     1073.0     6.0      191.0\n",
      "4          Hunan      879.0     1.0      208.0\n",
      "5          Anhui      830.0     3.0       88.0\n",
      "6        Jiangxi      771.0     1.0      105.0\n"
     ]
    }
   ],
   "source": [
    "#formatted_text('***Most number of Confirmed Cases Outside of Hubei***')\n",
    "print(rest_of_China[rest_of_China['Confirmed'] > 500])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>**INFREENCES**</br>\n",
    "\n",
    "There have been many confirmed cases reported outside of Hubei. Zhejiang, Guangdong, Henan, Hunan beng the top four.\n",
    "\n",
    "The recovery from the virus outside of Hubei has not been fast. The virus did not match any other known virus. This raised concern because when a virus is new, we do not know how it affects people. However the number of deaths have very minimal."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Rest Of World "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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>Country</th>\n",
       "      <th>Confirmed</th>\n",
       "      <th>Deaths</th>\n",
       "      <th>Recovered</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Australia</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Belgium</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Cambodia</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Canada</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Finland</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>France</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Germany</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Hong Kong</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>India</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Italy</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Japan</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Macau</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Malaysia</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Nepal</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Others</td>\n",
       "      <td>135.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Philippines</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Russia</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Singapore</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>South Korea</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Spain</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Sri Lanka</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Sweden</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Taiwan</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Thailand</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>UK</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>US</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Vietnam</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Country  Confirmed  Deaths  Recovered\n",
       "1              Australia       15.0     0.0        2.0\n",
       "2                Belgium        1.0     0.0        0.0\n",
       "3               Cambodia        1.0     0.0        0.0\n",
       "4                 Canada        7.0     0.0        0.0\n",
       "5                Finland        1.0     0.0        0.0\n",
       "6                 France       11.0     0.0        0.0\n",
       "7                Germany       14.0     0.0        0.0\n",
       "8              Hong Kong       38.0     1.0        0.0\n",
       "9                  India        3.0     0.0        0.0\n",
       "10                 Italy        3.0     0.0        0.0\n",
       "11                 Japan       26.0     0.0        4.0\n",
       "12                 Macau       10.0     0.0        1.0\n",
       "13              Malaysia       18.0     0.0        1.0\n",
       "14                 Nepal        1.0     0.0        0.0\n",
       "15                Others      135.0     0.0        0.0\n",
       "16           Philippines        3.0     1.0        0.0\n",
       "17                Russia        2.0     0.0        0.0\n",
       "18             Singapore       45.0     0.0        2.0\n",
       "19           South Korea       27.0     0.0        3.0\n",
       "20                 Spain        2.0     0.0        0.0\n",
       "21             Sri Lanka        1.0     0.0        1.0\n",
       "22                Sweden        1.0     0.0        0.0\n",
       "23                Taiwan       18.0     0.0        1.0\n",
       "24              Thailand       32.0     0.0       10.0\n",
       "25                    UK        8.0     0.0        0.0\n",
       "26                    US       12.0     0.0        3.0\n",
       "27  United Arab Emirates        8.0     0.0        0.0\n",
       "28               Vietnam       14.0     0.0        1.0"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rest_of_world = CountryWiseData[CountryWiseData['Country'] !='China'][[\"Country\", \"Confirmed\",\"Deaths\",\"Recovered\"]]\n",
    "\n",
    "# Reset Index\n",
    "rest_of_world.reset_index(drop=True, inplace=True)\n",
    "rest_of_world.index = pd.RangeIndex(start=1, stop=len(CountryWiseData['Country']), step=1)\n",
    "\n",
    "#formatted_text('***Numbers in rest of world for Confirmed, Deaths and Recovered Cases***')\n",
    "\n",
    "# Data Glimpse\n",
    "rest_of_world"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Numbers in rest of world for Confirmed, Deaths and Recovered Cases</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        Country  Confirmed  Deaths  Recovered\n",
      "8     Hong Kong       38.0     1.0        0.0\n",
      "11        Japan       26.0     0.0        4.0\n",
      "15       Others      135.0     0.0        0.0\n",
      "18    Singapore       45.0     0.0        2.0\n",
      "19  South Korea       27.0     0.0        3.0\n",
      "24     Thailand       32.0     0.0       10.0\n"
     ]
    }
   ],
   "source": [
    "#formatted_text('***Most number of Confirmed Cases Outside of China***')\n",
    "print(rest_of_world[rest_of_world['Confirmed'] > 20])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fab94d6c5f8>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x1080 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rest_of_world.plot('Country',['Confirmed', 'Deaths', 'Recovered'],kind='bar',figsize=(20,15), fontsize=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "32"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.Country.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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jlAjVmiQdHR266KKL9POf/1xS/q41iURCp5xyitasWaOtW7dKkrZu3ao1a9aoo6NDnZ2ds24DAAAAUMIzbM0Kz8v/ZwjfM+wWwAbe6cU3bnHe+QtVJYkkffjDH9YHPvABbdmyRZFIRLfccotaWlp044036vrrr9dtt92mlpYWbdmyJfidw20DAAAAkBcs2GrIJ+++5xt1AW3cQqbFyihTkn4SlSRzELokyYknnqivfvWr076/evVq3XnnnTP+zuG2AQAAACjwDLuo9L3J6goTGHj3ovyDyrbjuCJJckShmm4DAAAAYBGZVmng+eZMLZLMu4A2LV7JzJjniSQJAAAAgDnxDask8X3PqIVbjVuTxLDKGYk1SeaCJAkAAACAuTGuksSjkiTMTItXMjPmeSJJAgAAAGBOgkoDU9bp8M1cuNWUdVh8ExMGJsY8TyRJAAAAAMyNYdMTfM+shVuNSxqYFq9UEqtBMc8TSRIAAAAAc2PaRaVxlSSmrkliSLySeVPmFoAkCQAAAIA5MW26je95xsQqKbhwNiZpYFrSTyUhU0kyK5IkAAAAAObGL97dxpALLMMqDSan25iRGDLubj6SedVCC0CSBAAAAMDcFEv0TamuMKxyxtipGCYmDEyMeY5IkgAAAACYE9+wT6FNXcjUlMqZoDLKpKSQidUz80SSBAAAAIvCS6eVTSQq3YzjIjc0KHd8vNLNWHxe8aLSkMoK0+I1LCk0mRwxI15pMgFmVGJonkiSAAAAYFEM3n+f9nzspko347jY/6lPqP+ub1e6GYvPsIto0+KdrBQiKRR+JsY8NyRJAAAAsCjc5Ji8ZLLSzTguTInVN6yyYnJhTzPiNW5NEt+wNXYkptvMAUkSAAAALA7PM+ZiWp5vRqymffIeTE0woG8lY28BbEy8pUyMeY5IkgAAAGBR+K5nzCe0vudKrgGxmnZ3m2IFiWmVFYZcQJucHDEy9jkiSQIAAIDF4XuS75txMu55k+s5hFgxRiP6VCXTiwzoW8m8uxdNJsFM6d+SfjWkixeCJAkAAAAWRTBFwYALEN8zpGqmWFFhSNLA1DU6TEmCBYkCY+ItTZIYsg8vAEkSAAAALA6TFvk0ZP0V37RP3k1duNXApIERiaGyJIkB8S4QSRIAAAAsCrMqSXwj4jR2YU9DKkl8wyqFyqZRmTCmSZLMCUkSAAAALA6TLrh8MypJZFDiS1LJ7VINide4NUnMShqUJoWMSXQuQCiTJJ/5zGd0xhln6JlnnpEkPf7449q4caPWr1+vq666SolEIvjZw20DAABYDMntT2nHe66Rl0pVuimLKlj00oi7vpixJolpC3v6pq5JYli80x6HlT/LY5QJXZLkqaee0uOPP64VK1ZIyh/Y3vve9+qGG27Qtm3btG7dOn384x8/4jYAAIDFkj10SO7wsNxkstJNWVyeW/hqQPLAdQ2pJCleRBsQqzS5ro4JF9AqidOQeM1ek8SQfXgBQpUkyWQy+shHPqLNmzfLsixJ0hNPPKF4PK5169ZJkq644grdd999R9wGAACwWExZ/NI3ZOFW3/fzFx8hj1NSyS1TDbiglErubmNA30olC7eaEW9ZxYxxSZLKNaPaRSrdgGPpk5/8pDZu3KgTTzwx+F5PT09QVSJJHR0d8jxPQ0NDh93W1tY25+ft7Gw6NgEAi6Crq7nSTQBmxfhEtVrssZmtj6pPUntbnepDvB/0RW0lJXW0NyjeGd44fdfVs5IijnVcjmuVPHam6qOSpHjMMeIY/lzhorK9vV4NBsRb3GfjsciC+7eWxoXXFNehwuMlnY1y6uoq2p7Flht39FzhcWNjrKb66ngKTZLkt7/9rZ544glde+21x/25E4kxeaZk01FTurqa1dc3WulmADNifKJaHY+xOToyLklK9I0qHg3vfpCeyEqSEv0jinrRCrdm8fi5nCQpm84u+tip9LEzOZZfRyc1kTHiGF6sghroH1MyHv54M6n8PrvQ/q30+Jyv0dGJ4HF/34jsumwFW7P43PHx4PHYWKqm+mqhbNuad1FDaJIkjzzyiHbu3KlXv/rVkqSDBw/qbW97m97ylrfowIEDwc8NDAzIsiy1tbWpu7t71m0AAACLxpQ7Zhg2rSj0/SkZNx3DmH21IJiNYcLUE6ls2pgRIZcGyYf8swrNmiTveMc79NBDD+mBBx7QAw88oOXLl+sLX/iC3v72tyuVSunRRx+VJH3zm9/UpZdeKklau3btrNsAAAAWi++6ZV9Dy5S725gSp0rXmTHjAsv3DVuo1jctKWTYQqamxbtAoakkmY1t27rlllu0efNmpdNprVy5UrfeeusRtwEAACwaz5AKi2ISKOQn475Bd/Ext5LEjKRQcAtgU+KVYZUVxvTr0QltkuSBBx4IHp9//vm6++67Z/y5w20DAABYDKbc9UWmfApfvC2uAYmDoC8NuKAM7lokmZEAk0qSYOHvX0nl49iAmEuTX6E/Lh+F0Ey3AQAA4TH84E+17xP/UulmLJ7iyWnIp2f4plTMGBKnpJJKA3NizT8M/wW0VBKnAUkwSWUVUb4J98Q1ZBwfLZIkAACg6qT2PK/Ujmcr3YxFE6xJEvYLTVMqZkyJUzJmqpik8hhNubgsjuWwH5sKfNMWMvXNqpxZKJIkAACg+nhuuBc1DSpJQhyjzJmaYUqcUslFpQEXWOUX0GYkDdBf4A4AACAASURBVIybblMaphExkySZC5IkAACg6vi5cCdJjFmTJIgzvH0pKSjZD32ckplVMzJwuo0h8ZZNtzEgZt8zbwrZQpAkAQAAVcf33FB/cmvK3VBMWavDlDglldwi1oALLCpJws+46SemxbswJEkAAED18TzJ98P7aXXx0/gQV8tIMqfqwDUkTk1+Em1GrAauSVKsijIkXt+0JIlp8S4QSRIAAFB1goVNQ5pE8A25qDalwsKUOCUZW0kS9n21KJiOYUL/SlOSBgb0sWG3PF4okiQAAKDqBMmRsF6YGDLdJogv7CfjwZokIY9TmuzLsI9dybwLaKmkfw0Yy9KUPq5cM46X0tscm1IttBAkSQAAqEGHvvYVHfrKlyrdjMUT8ukoviELmvq+GRUzk8mgkMepkrFrwAVW6bgN/RguKvSrKbcALqsWMmBMU0kyN5FKNwAAAMxfZv++UJ+0B8mRkCZJJm8BHN4+lBT6ZFeRb0ickiYvsky4wCpNFBhTWWFI9VdB+bozIT8eS2Lh1rkhSQIAQA3y3VyoS/uDNUlCWmkxGV+4T8qNWavDlDhVUmFgQqyeedNtjFuTpJQJMbNw65yQJAEAoAb5OTfcJ+3BJ/MhjbF4IRLSJFDANWMail+y9orv+7Isq7INWkzFu9uYcIHll063MSBeiVsAh51p8S4QSRIAAGqQ77qh/iQ37NNtihUyoa8kMW1NEil/4RHmJIlBlSTT+tUExTVJTOhfTVl3xoA+9k1bg2WBWLgVAIAa5Lu5UK9/EPbpNsZMzwjiDPfJeNkFZcj7dHI6RrjjlKZcRIa8XwNUkoSbP+s/UIIkCQAg9FK7d2voxz+qdDOOLdeV7+Yq3YrFE/LpNn7I4wt45lWShD5Wk253bFiVgVQSpyHxliUKTBjTJi5GvAAkSQAAoTfyi4fU/993VroZx5TvukZUkoR2uk0xrrBWyhSYsnCrSZUkMqiSRAZXkpiSFCpPFBgQM9Nt5oQ1SQAAoee7Ofm5cFVdhH1NEhky3Sb0VQdBnCHtx6KyBT7DHasx68xo6t1tDLmgNGnNGZm3Rkd5jOGPd6GoJAEAhJ6fy1ddhOkEKPSVJCGfjmJMhUVxnwt7nJ5BJewld/IJPd+gaVQFxk238U2bbmNYvAtEkgQAEHp+Lpc/MQjTSa7rhq46plTwaXxYE0GG3N1msiIo5CfjrkEX00HiK+R9KpXHaML0IsmsWzxLUxZuNaCPWbh1TkKVJBkcHNTf/M3faP369br88st19dVXa2BgQJL0+OOPa+PGjVq/fr2uuuoqJRKJ4PcOtw0AUPv8XLbwNTxJBT8X7rvbhH26TTFpEOY+NGmdDhNj9Q24oPTLKkkMuaA0uJLEiJBL91sjAl6YUCVJLMvS29/+dm3btk133323TjzxRH384x+X7/t673vfqxtuuEHbtm3TunXr9PGPf1ySDrsNACBl+/rkZbOVbsZRCW4nG6Ykievmq0lCepIzubBpSC/Ewh6fNOXOICGOUzJrWgaVJKEW7KvGxGtWH5ff8diAfXiBQpUkaWtr00UXXRT8+9xzz9WBAwf0xBNPKB6Pa926dZKkK664Qvfdd58kHXYbAJjOz+W0+8YPauRnP610U45KMTkSliSJXzp1KKQXZJNrkoSjz6byDVi4tSwxEuI4pSn9GPILrSDWkMcpTe1XQy4oTUqCSeZVVpgW7wKF9u42nufpG9/4hl71qlepp6dHK1asCLZ1dHTI8zwNDQ0ddltbW9ucnquzs+mYtx84Vrq6mivdBNSw3FhSfjqtuJtelLF0vMbnocJHAh2tdYqHYJ/wslk9W3jc2V4vJx6vaHsWw47CxUlLU1wdFeizxR6be6381/q4E9rjtJtK6bnC48b6aGjjlCS/Ma6DhcftrfWqX+RYK/laHozYmpBkVbgdx8PoQL32Fh43NsRCH68k7ZLkSrJta8Hx1tLrNFoX1VDhcVtbg1pqqO0LMZJoCMZ0XTzcx+WjEdokyU033aSGhga9+c1v1g9/+MNFfa5EYkyeKdlW1JSurmb19Y1WuhmoYbnhYUnS2HDymI+l4zk+MxNpSVJ/77Biqv2EgpdOB4/7Dg3Lqa+vYGsWh1eo+hkeHJN7nI9jx2Ns5gpT2MbHUqE9TrsTE8HjsZGJ0MYpSaPD48Hjgf5RxSKNi/ZclX5vz6bzY9fLuaHuU0maSIwFj8dGwz2Gi7zCIsSuu7D+rfT4nK+J8cn308GBMaVrqO0LMTGQDB6nJtI11VcLZdvWvIsaQpkk2bJli55//nndfvvtsm1b3d3dOnDgQLB9YGBAlmWpra3tsNsAwHRhmaYSljiKyqaghHXhz2C6TUjjc8M9XUpS+dgM+dSM0gWGwzyFSjLrFrEmTRkrCmI25APg8jVJwh9z2WLEBsS7UKFak0SS/vVf/1VPPvmkPvvZzyoWi0mS1q5dq1QqpUcffVSS9M1vflOXXnrpEbcBgOn8wqfdfq0v3FpMjoRkfQs/V3JBFpLEz1TBYrtuOC9MTFuTJMxxSmatSSIDxm7AM/CC0qAkmKQpi/MaEnORafHOQ6gqSZ599lndfvvtOuWUU3TFFVdIkk444QR99rOf1S233KLNmzcrnU5r5cqVuvXWWyVJtm3Pug0AjpVMzwFF2ttl19XWtIiw3Dq3WHlR63EUlVZXhLHSwve8ksUDwxefVFJ5ENL4JJV/8h72C2qTYg0uokMep1R+ERn2fi0qxBz6O1IFDEuSmLgY8QKEKkly+umn649//OOM284//3zdfffd894GAMfCno/epPb1l6pzw8ZKN2VewlNJ4pZ9rXklFTFhTJKUfXobxvikYLpNmD+N90s+oQ1znJLKx2zIYy1WVPgGTMcwbSqGVNKvhsRbGqcx1UJFhoU7H6GbbgMA1cbP5eRNTMgdq73FsSbX8qj1JEkh2ROW6TaliYOQxFSqvFImnBecwae0IY1PklHVFWUJoTD3qTTZlyZUGhiU/AoU+9WQhIFv2nSbssSfIWN6AUiSAMAi84rVGJnaSzQESZKaryRhuk0tKYsprNNRimuuhDU+yawLTJPWJDHpItrASpIiEyqFJBmXNPDLKmcq2JAqR5IEABaZn8nfXs7LpI/wk9XHy2Yk1X5yYXK6TW3HEQh5ksSE6TZB0iDEyYOyBFCI45RMi7U43SbccUrlMZoQrySzKoWkKUmSyjXjuDEsKbRQJEkAYJEVK0hqsRrDz4ajAiPUC7eGJKZSYa+UkWTGHUJMusA0KVZjK0lC3q8FJt3iWdKUNUkM6GODq6PmgyQJgJo0sO0Hev6mGyvdjDnxMvlqDC+dqXBL5q+4lodXgwmeUsF0m5Cs31GaGAljEqF8uk04T1qNuAVwabl+iOOUpvSjKbGGPE5par8ackEZ3N3GjHjLEiMm9LFplTMLRJIEQE3K9BxQ5sD+SjdjTvzilJVsDSZJsrW/Jonv+5PrP4Sk6qJ84dbwJUlK1yEJYxJI0mS/hTU+qby6Iuyf0JZ+Gh325IFJdwPxDaoQKgpuvx7yvi0qDTPs41nlx+LQH5ePAkkSADXJS6XlZ7M1cQFVrCSpzSRJYapQLd/dpmxqSvWPl7kI+3SU0ruDhDI+Q6ZmlJ2Ah/yOLyZVkph01yIZducT3/cn4zTlArqsX8PfxyaEeCyQJAEgKf/GOPTTn8hL18bion46JUk10V4/BNNtihUltah8akrtxlEm9EmSkE+3MeUi05BkkCSjYvUNWtOgfCpGuPtVkpnrVZRVCxkQs2/ONMijQZIEgCQps2+ver/6H0r+7vFKN2VOismRmkiSBNNtaq8aIwy3AC6bYhPC6TZhTJKEfbpNWf+F+CTVpOoKk2Itra4I8/iVZNbUIsm8eGVW0k/SZIyWVdl2VDmSJAAkSW4ymf86nqxwS+bGS+UrSYoVJdXMC+5uU4OVJCGYblNaPRKeNUlK4ghjEqH0wiuE8ZWVsYcxviLXoLnvnhmJL0nl4zfsF5UmJb8kQytJzIo5SArZthmVMwtEkgQ4jnzPq9qLNG9iouxrtfOK021S1Z8k8TPFqpcaTJKErJIkLFUJob8FcC7cF5xla66EML4ik6YqlF1shDwh5BtUSVIWqxEX0ObsswHfl2x78nHYFWK0bFssUDI7kiTAcTRw71Y9/5HNlW7GjGouSZIqJB5qIElSvH1uLVaSBG3P5Wr2BLEsSRKWhEIu3NNRQj/dxgv5mitFJn0Kb9CaJGV9GfKEkFFVM9LkVCrLqtn3/Pnyfb+QMDAjEaaSShIjxvQCkSQBjqNMzwFleg5U5QmUOzGe/zo+XuGWzE0tLtzqZ7NV2feHUzbNpkYvVsuqEkKSJCmbQlSj/XI45es7hC++sgvqEPZfkSl38ZGmThELd6zlCz+G/CKrNL6Qj2FJk1UGjmPOBXRZJYkBfaySSpKw779HgSQJcBy5Y2OS78urwkREsU3eRPW1bSrf90sWbq3+SpJikkSqvWkrpXe18Wqs7UXlCYWwJElKLqxDElOZsoVpw3fSWhZTmE/KDUqSmBRrWfIrzONXJfFZVuhjlcrXqzApSWIFSZLKNuW4CKqFmG5zOCRJEFrpvXvVd+c3q6p0zh0dzX8dG6twS6arpek2fiYTvHn7qeqvJPFqOkky2d5aXby1bLpNDd/KuFTY725THl84+qyMF+4kUJFRd3zxDUl8Sfn4HCf/OOyfRHsllRVhj1UKxq65lSThj9kvWZOkmq6Rqg1JEoTW6CMPa3DbfXJHhivdlEAxOeKOjVa4JdMVp9vUQpKkdIpNTVSSlCQavEz1J3VKlSZGai3BU1S+cGtILrjDniQJ+Zod5Qtfhq//AgYlSUxZjFfKj1+rmCQJeUIoqB5xnNDHKmlKlYEZa3T4ni/Lzo9nE6qFguoR2zIiKbRQJEkQWtmBhCQpNzBQ4ZZMKiZHqrmSpBbWJClNjNTCmiSliRE/U1uJhrJKklqtwgjhnWDCfnebslvHhjAJVLbOSog/nS4mC6xIJPSJA3merEgkeBxqvhckSUJ/C9GSSpLQj2GpZE0Scyor8pVRBsXrGTilagFIkiC0ismRYrKk0rx0OlibohorSWpquk3JFJtauLtNaWKk1u5wU35nmNpK8BSVxxCOC+4w3ta4VBCTZYXywqRYdWBFo0ZUkliRSKiTQVI+IWRMksTzZDmFWMP+ybtn1vQTP6gyKE6nCnn/FlgGTbeZXLjVjDG9UCRJcMwN/OBejfz6V5VuRpAkqZZKEjc5WT3ijlZjJUntLNxaa5UkpYmR0vVJakEY1vMI43SbIIlg2zV716HDKSYOrGg0lPGVJQ9CvCbJZJwhTwZJ+eqKSLTwMMR9qsIUDEPWJCmbbhPyfpVUUjljznQbeZNrkpgQb7H6izVJDo8kScGuXbu0adMmrV+/Xps2bdLu3bsr3aRjauSXv1DinrsX/Xm8bFaJ79+lwR/cu+jPdTi+5yk7WGVJkpIpNlVZSTJeqCRJpar+BK+0esSvgUqSsoVbay1Jks1KlpV/XKuVJIXEiBWNSiGZmuK7ruQ4+RLwMCYRipUWsVgo4ytNAlX78fZoFC8wrWgk9BeYvufl45RCH6t8v2S6TfhjlQrTbUy4oPRLqgxK/h1mvu8ZWUnCdJvDI0lSsHnzZl155ZXatm2brrzySt1www2VbtIx43ue+r/zbSW+/91FXwsjvXuX/GxW6X175SaTi/pch+OOjgSfPhaTJZVWvLONVJ1rkhQXbpXvV/0UlqCSxLJqY+HWTEZWLCZJ8mpuuk1Wdl1d/nGtLtxaqICx6+pCM91GrisrxEmSYkx2NBrOC86SCguFucIimKpgyJokhSkoZsRqyEV0yRgOfawqqaQoVJJ44+PyUjNPw/Z9X9lEovaTR6V3t/E85YYGZ/3R9P59h91eE4LbPOcXbs0ND816HuGOJzWxc+dxbFz1iFS6AdUgkUho+/bt+tKXviRJ2rBhg2666SYNDAyoo6PjiL8/9vhvlJuosgs131dueFhOQ4Pc0VHlComCxPfvUt3q0xbtaZNP/D54/sFtP1Bs5cr5/QHflzsyKisel10XL9/mecoNDctpaZEVcQ77Z7L9/fkHlqWxRx/R4P33yaqrkx2PH/55iq9bY6OsaFTe+Lh8z5PT1DS/OGaQ2rEj36RoVCO/+oViK1YEzzOXGP1MRu74uKxIRJZty25oOPKTNtdrZHRua4x4yaTshkZ540klvneX6k49dbKCIJNRbmhIdl2dnKZm5YaH5DQ1y4pG5CaT8goJsUh7e3k8i2Ti2WclSU5TszK9vRr+2U+V3rtHsRNOnOxj5adZuKOjsuNx+a4np7FBueFh2XV1+e/X1clpbg7iXCy5wUE5jY3KZTIa/MG9Gt++XXUnnzxZ3pnNyk0m82MumZTT0irLceSOjSrS2iZ3dFReOnXcXt+yto+Oyq5vkDcxoYEf3KPk73+n+Ekn599cVTIunYi8iXE5ra2ybFtuMqlIS6vSe/dItq3Y8u7JT1mL5jE+j8bEs89Ikux4ncaffkq93/xPRZd0yY7F5KUm5DS3KDcyLDselzs2JisaleVEZDmO7Ib6/OuQSCg7OKh4d7fsxsZFb/ORpPfvCy5SRh56UPLc/PiPxfIxOBF5qZTs+sJYr2+Q09wkP5dT9tAheZm06k4+pbAf1MuKx45p+4r7XqS1dfLkcx4mduT3cSsa0/jT2/N91tUlp6lJ2d5e+W5Odn1DWZ9FWlslX8oND0mWpWxvr2LLlstpWcA+PsexWRz/kdbWeT1H9tChQnwR5QYS6vv2t+Q0NCjS2Sl3eFi5oaH8/h6JBOtE+Z4nP5NRrLs7OI556fS8n3s+/Jwrd2REkbYF9uNz+X5UxFFqx3OFfW+JnOZmZfv65GezshtK+jESkZeakBWJKtLeIXd0RHa8Tn4uO6djtTc+Lt/35cxzH11oP5bKJhJS4X17+Cc/Vm5gQPGTT5ZlO0rveV6R9g7Z9XXKDQ1Jkuz6BmX7ehVbvlyy7cKt7SUrHpfl2MoNDsqOx+W0tExrk99Up8F9B+U0NAQJ+Llyk0lZ0sKPY1MuohP3fF/R9g7FVqyQl84os3+v4iedLC+dzl9gp9PKHNivxrVny25oyJ97RaPyUilFWlvlpSbkJpNyWlqChHzZ0x3NscT1lBsezv+us7DPhVN79kjKT00Ye+xRHbjt06pbtVqR9nZlDh6UZduKdi5RdnBAuUS/3NEx1b/whXKamuRnc3LHRuVns/JdV7Fly5QbzF9g23V18j1fTvPk+aU3MSE/l8uP9XnyUmn56fTCjnclih/iFStJdn/o/bLicbW94pX5fXJsVNlEv5ymZqV371Lyid+r+cKXqPHsFym9f5/ke8qe0K3BXXsVP/lkeRMTwTHMchxF2tvL+rF4/jPvfW8e1wRHjHl4OIi39+tfVe9/fi0/XusbVLdqtTI9B+Q0N8lNjmv4pz+W09yijvWXKtvfp+jSZZLnKrV7V/68or5Bdl1cmZ6e/LnPihVHvvaYaztHx8rOSxYqtbNwPWLbSu3coZ3ve4/iJ5wop7lZ9ae/IP9BaS4nKxbT6K9/pWxfn9r/12WS5+WPR77kjgzLTY6pYc2ZwftRtr9fXjqlWPeK4Bh8tOMy2Ceamo5qXEebGtX5pxfP63csv+bTf0fvySef1HXXXad77rkn+N5ll12mW2+9VWedddYRf//Rv/lbpXv7FrOJR81pbFSso10Te/ct+nM1nnqKUr19Fa0kKWq/4HwNPvabSjcjYMdiajlzjYYe/12lmzKjzpdepMQvH650M+bEjsXUsvYsDf3mt5IKd0+o4qkU7S9elx+LhcX9qrmtU3VcdKEGHv61pPm/zlY0Kvl+xeO1IhG1nXuOBh99bMGvv11XV1VVVnUrVsiOOBrfs3d+Mdm27Eik6tfHsaJRtZ3zoul9Zln5/47wab1TXy+3yheiLh5zp1YEzdaftVg5ZEUiajvvXA0+8uj0uGx7ej8WS8Br8PS09UVna2zHznwioiTWWfuzxt4LSnW+9CVK/DK//lzpuJwWk2Up1t6uTJVMfV4Ip7FRbS86W4lf/kpOY+Pk+e2UsWrX1SnS2KhMYsoNA+Z4zKomS1/zavX+z48UaWpSpKlJqYMHg23FY2u0rU3NLzxDA79+ZPLuTpYlP5utubHd/uJ1GnvuOWUHh9R8xguU6u2TPFfZ4ZHgvd+OxdR23rkae/Y5ZQYGymJ0GhvkJifX9LPr6iTPq9r3WSsSUdfLL1HvAz9Rw0knKjs6KqeuTqmeg0GVqpfNqv6ElYovWaKh3z5e3qe2XfigKVX2N+14rOx1qBbxpV1a9/nb5/U7JEl09EmSg3/YJbcKFzR0mpryGTjXldPUJCsSCT7BWEyR9vZ8ZniBU0rsxgb5mez00n5Lcpqb84uezmHYFqsEsn19chob5WXSZQtP5p8nM20xSqepKT/1xPXyn+jYlrxjVCnkNDbKqqtTLpEoe56yn2mZOUbLcWTX18t3c/Jdb05rW3R0NGpgYI7JKttWtKtL7uhovhKgpP+K2X93YlxecjzfD8mk5Hn517mlRSquA3OcFiEsfS1lWYp2dio7MFB+EmJZ+f0gk5Fl2/ImJuS0NMubyH+C5Y6PyztOtzyOLlkid3xcfjYrp6VZuUTJCaNjy2lozG9rblZuKF/66DQ25j/pb2yQU99wXF/fsrZ3deU/DXO9fEXM4GSp6eS4zB9ncoMD+U9y6xvkjo0p0tYmKxKZ3jea5/g8SnZjg+y6euUSCUU6O+SOjEq+Jztel/8Us6lJXjotp6Ulf1zI5eS7brCf2fV1clpa831TJYsFO62tsiwpNzxSNv7thgb5uZzsunh+rLe1yR0by3+aZ1mKdHTkq+YGBvLHxnTq2E9Dsqz8J28jC19/qXQfz/fZiPxsTnZjQ76KxvPL+qw4pdFpaZafySjS1q7cyMiC9vG5js3i+F/I+50VjyvS2qpsX6+cllb5mbS8iZSsWKzQL+n8J2iNjflpkNlMvsJsZFROc3P+pL1QgbFojkE/Bsev/n5FOjrkjo4GFST5u954hX5sDt73fdeVNz4hp7lJXiqdrzCZQz9a8bgsy5p3MvNo+rFUpKMjPxYnxhVt71A20S/5+fMiN5mUXDf4JN0dGSkcMwcl25YVi8qSlT9X8TxF2zvkTUzM+IFTR0ejhjNWcI43H3ZdnXzfP7rjWOF8IVe4ULSikfwYsW1FO/Jx58/BWoJfyfb3549PjQ35/i9WdMbj+WPr4ODMUzqPZgwG542jwTIMC+E0NxeqYIbz1bRDg/KzhU+3fV/ueFKR9o6g0tMdHs6PQdsO9l8pv4B/pK09/zsTE9POL61YtFCVOf/krhWNyI7Fj8kHlFY0omjnknwbHSdYQDs7OCA7GpXT2pZfl6ZQEeKlUsoNDeWrgeJxtUZdDWfswjioDypj/Gy27PxBUv78p3C+ML9Gzu+a4EginZ3BsaNYPe57JZVIkmRZsgp3XMsNDyvS3JxfY9DOHz+8iQlZEUdeKq1IW5skKTeQKHt/ne3aYy6K51rHYm07u7FBTlO+/U5jU9CXbjIpWZbs+vqyPs6NjsiOxfMxxqKS5wfXlcX3o0hbW/7YNjwkr3AXyqMdl1YsKisSPepz9UhdXMtOP2l+z02SJD/dZv369Xr44YflOI5c19VFF12k+++/f07TbRKJMXkhX90btamrq1l9fdW3SCwgMT5RvRibqGaMT1QzxieqjW1b6uyc39IJLNwqqbOzU2vWrNHWrVslSVu3btWaNWvmlCABAAAAAADhwMKtBTfeeKOuv/563XbbbWppadGWLVsq3SQAAAAAAHAckSQpWL16te68885KNwMAAAAAAFQISZJjwLYX9/ahwNFgfKKaMT5RrRibqGaMT1QzxieqyULGIwu3AgAAAAAAiIVbAQAAAAAAJJEkAQAAAAAAkESSBAAAAAAAQBJJEgAAAAAAAEkkSQAAAAAAACSRJAEAAAAAAJBEkgQAAAAAAEASSRIAAAAAAABJJEkAAAAAAAAkkSQBAAAAAACQRJIEAAAAAABAEkkSAAAAAAAASSRJAAAAAAAAJJEkAQAAAAAAkESSBAAAAAAAQBJJEgAAAAAAAEkkSQAAAAAAACSRJAEAAAAAAJBEkgQAAAAAAEASSRIAAAAAAABJJEkAAAAAAAAkkSQBAAAAAACQRJIEAAAAAABAEkkSAAAAAAAASSRJAAAAAAAAJJEkAQAAAAAAkESSBAAAAAAAQBJJEgAAAAAAAEkkSQAAAAAAACSRJAEAAAAAAJBEkgQAAAAAAECSFKl0A8JgcDApz/Mr3Qxgms7OJiUSY5VuBjAjxieqFWMT1YzxiWrG+ES1sW1L7e2N8/odkiTHgOf5JElQtRibqGaMT1QrxiaqGeMT1YzxiVrHdBsAAAAAAACRJAEAAAAAAJDEdJtF47o5DQ72KZfLVLopxrJtR/X1TWpqapVlWZVuDgAAAABUVHGpiEiEeonZkCRZJIODfaqra1Bj43Iu0CvA9325bk6jo0MaHOxTR8fSSjcJAAAAACrq94/s0/bf9ejKd1xY6aZULdJHiySXy6ixsYUESYVYlqVIJKq2tk5lMqlKNwcAAAAAKi45mlZyNF3pZlQ1kiSLiARJ5VmWLYkVtgEAAADA98Xl0RGQJAEAAAAAwBC+T5bkcFiTxBBvfOPlisViikZjyuWyuuKKN+vyy/+s0s06Jq6++h36q796iy6++E8q3RQAAAAAqFq+71NIcgQkSQxy881btGrVadq58zldddWb9dKXXqwlS7oq3axpXNeV4ziVbgYAAAAAhIof/A+zIUlioFWrTlNzc4v6+nq1ZEmXvv71L+snP/mRXNfVkiVLdd11/6TOziXKZrO6447P6uGHfyHbdrRixUp97GMfl+u6+tznPq2HH/6FJOmii16m//t//159fX16xzvequ985x5FIvmh9U//9F5dcskrqiAYpwAAIABJREFUdOmlG/TLXz6kr3zli0qnM4pGo/r7v/9HrV17tn7zm0f1qU/9i84551w9/fR2vfWtb9O5556nT3/6X7Vjx7PKZDI677x1+vu/f7ccx9GuXTv10Y9+WK6b0ymnrFImw22WAQAAAOBIfN9nus0RkCQ5Dv74xEH94fcHF+Vvv/BFy3XG2cvn9Tu///3jam1t02mnvUDbtt2rffv26Y47/kO2beuuu76tz3zmE9q8+WZ99atf0oED+/XFL35d0WhUQ0NDkqTvf/8uPfvsM/riF78uSbr22nfp+9+/S294wxt16qmr9Ktf/VyXXPIKDQ8P6fHHf6MPfvAj2r9/n/7jP76gf/mXT6uxsUk7d+7Qtde+S9/5zj2SpJ07n9O1116vd7/7fZKk//f/btK5556v66//kDzP04c//EHdc8/3tXHjG3TTTTfoL/7iCl166QY9+eQTeuc733YMX1EAAAAACCm/sHgrZkWSxCAf/OB18n1fBw7s1z//862KRqN66KEH9Yc/PK2rrnqzJMl1c2pqapIk/eIXD+nqq/9B0WhUktTW1iZJevTRh3XZZRuC71922eV68MEf6w1veKMuvXSD7r13qy655BX64Q/v0yWXvEL19fV6+OFfav/+ffq7v3tH0B7XdTUwkJAknXDCiVq79kXBtoceelBPP/2UvvnNfCImlUpp6dJlSibHtGvXDq1ff5kkae3as7Vq1WmL+bIBAAAAQCgUEyS+73M31lmQJDkOzjh7/tUei6G4JskDD/yPbrrpQ/rGN74j3/f11rdepQ0bXj/t52crw/L96bc3Lv77T//01fr0p/9Fw8NDuvferbrmmvcEf+uii16qD33oI9P+3u7du1Rf3zD1WfTRj35cK1eeUPbdZHKMnRkAAAAAFoCpNkfGLYAN9KpXvUYvfvFL9LWv/YcuueTluuuub2tkZESSlMlk9Oyzz0iSLr74T/Stb31D2WxWkoLpNi9+8UW69967lcvllMvl9IMfbNW6dRdKkurq6nTJJa/QHXd8VuPjSZ1zznmSpAsvfIkefviX2rlzR9COp59+atY2Xnzxy/W1r31ZrusGz33gwH41Njbp1FNX64c/vE+StH37k9q587lj+fIAAAAAQKiRK5ldzVWSDA4O6n3ve5/27Nnz/7d35/FRlff+wD+zZ7JMhoSELCxBQIiiqEQRLdeXWBekwMXqT8QWl9r2asXeulR7LxcsigpIe/UqarW33laUqlUoqIC4gRgoVKIsJmCAQBayTNbJ7Oec3x+TOZlgIMlAcmbO83n/YZI5CT7fzJOZOZ/5Ps+B1WrFiBEjsHjxYmRkZKCkpAQLFy6E3+9Hfn4+li9fjszMTACI+Zhe/du/3Yuf/ORHuPXW29DS0oz588PLYGRZxuzZN2HMmLPxox/djpdeeg533DEXZrMFQ4cOxeOPL8PMmbNRWXkMd9wxFwBwySWTMWPGbPXfvv76mfjFL+7CXXf9m3rbsGHDsXDhY3jqqcfg9/sRCgVx3nkTUFh4brfj++UvH8DKlc/i9ttvgcFggMVixX33PYC8vHwsWPBbPPHEb/HXv67C2LGFOPfc8f34myIiIiIiItKHSCdJ+CM79LtjUBKs36a5uRllZWWYNGkSAGDp0qVoaWnBkiVLcM011+DJJ59EUVERVq5ciWPHjuHJJ5+EoigxHestl8sNWe76azx+vAI5OSPOaO0UG5Hvi6ysNNTXt2k9DKJucX5SvOLcpHjG+UnxjPMz/n28vhRle2vxswenwGTW/8ISo9GAzMzUvv1MP42l3zidTjUgAYALLrgA1dXV2LNnD2w2G4qKigAAc+bMwYYN4SUZsR4jIiIiIiIi0gt141ZthxHXEm65TTRZlvHGG29g6tSpqKmpQV5ennosIyMDsiyjubk55mORq7n0pLtkqq7OCLMAyVwiMBqNyMpK03oYmhG5dop/nJ8Urzg3KZ5xflI84/yMb7akcAQwODMVFqtJ49HEp4QOSR577DEkJyfjRz/6ET788EPNxtHdchtZlhEKyRqNiKLJsixs2x9bHimecX5SvOLcpHjG+UnxjPMz/vm84Yty1Ne3CRGSxLLcJmFDkqVLl6KiogIvvvgijEYjcnNzUV1drR5vbGyEwWCA0+mM+djp4rWntacoMrghERERERERUecymwTbmnRAJeR6kN///vfYu3cvnn/+eVitVgDA+PHj4fP5sGvXLgDA6tWrMW3atNM6djrMZiva21s5+TSiKApCoSCamxtgtSZpPRwiIiIiIiLtqVe30XgccSzhOkkOHjyIF198EQUFBZgzZw4AYOjQoXj++eexbNkyLFq0qMulfIHwnhSxHDsdgwZloampHm5382n/WxQbo9EEuz0VqanpWg+FiIiIiIhIc53hCFOSk0m4SwDHo+72JCGKB1wXSvGM85PiFecmxTPOT4pnnJ/xb+O7+3CorAF3/PIyJNktWg+n3wlxCWAiIiIiIiIi6jv1EsB8j/+kGJIQERERERERCYQLSk6OIQkRERERERGRANRwhBnJSTEkISIiIiIiIhKBmpEwJTkZhiREREREREREAlB4CeAeMSQhIiIiIiIiEkDnFYCZkpwMQxIiIiIiIiIS0sH9dXDVubUexsDh1W16xJCEiIiIiIiIhPT55m+xr6RG62EMGC636RlDEiIiIiIiIhKSIitQZBETAxFr7h2GJERERERERCQkRRGrq4KdJD1jSEJERERERESCUtTgQASKuieJODX3FUMSIiIiIiIiElK4k0ScwEDhxq09YkhCREREREREQlIURbDAQKhiY8KQhIiIiIiIiISkKBCqrYLLbXrGkISIiIiIiIiEJFonCTdu7RlDEiIiIiIiIhKTYHuSqESsuZcYkhAREREREZGQxOsk6fqRvoshCREREREREQlJtKvbQF1uI1DNfcSQhIiIiIiIiIQj4v4cItUaK4YkREREREREJC4BkwMBS+41hiREREREREQkHBH351C43KZHDEmIiIiIiIhIOCIGBgKVGrOEC0mWLl2KqVOnYuzYsThw4IB6+9SpU3Hddddh1qxZmDVrFrZu3aoeKykpwcyZM3HttdfizjvvhMvl6tUxIiIiIiIi0icRO0mg1ixS0X2TcCHJVVddhVWrViE/P/87x5599lmsXbsWa9euxZQpUwCE7/yHHnoICxcuxMaNG1FUVISnn366x2NERERERESkX2J2koi3WW1fJVxIUlRUhNzc3F5//549e2Cz2VBUVAQAmDNnDjZs2NDjMSIiIiIiItIxATtJIqWKFAz1lVnrAZxJDz74IBRFwcSJE3H//ffD4XCgpqYGeXl56vdkZGRAlmU0Nzef8pjT6ez1/zczM/WM1kF0JmVlpWk9BKKT4vykeMW5SfGM85PiWSLNT78vCACwWEwJNe7TYTKF+ySc6cnC1NxXuglJVq1ahdzcXAQCASxZsgSLFy8esKUzLpcbsswkjuJPVlYa6uvbtB4GUbc4PylecW5SPOP8pHiWaPPT7wuFP/pDCTXu0xEKSQCApiYPkuutGo+m/xmNhj43NSTccpuTiSzBsVqtmDt3Lr788kv19urqavX7GhsbYTAY4HQ6T3mMiIiIiIiI9EsRcOdWhRu39kgXIYnH40FbWzj5UxQF77//PgoLCwEA48ePh8/nw65duwAAq1evxrRp03o8RkREREREJBJJkrH3y2phuuQFzEgEKzY2Cbfc5vHHH8emTZvQ0NCAO+64A06nEy+++CLmz58PSZIgyzJGjRqFRYsWAQCMRiOWLVuGRYsWwe/3Iz8/H8uXL+/xGBERERERkUiqjzZj66aDyMpJxZA8h9bD6XfqlV4gTnDATpKeJVxIsmDBAixYsOA7t69Zs+akP3PRRRdh3bp1fT5GREREREQkCkkKnziL0kki4tVtIkSsubd0sdyGiIiIiIiITo/aWSFISKLWK1BiIFKtsWJIQkRERERERFDkjo+CnEdHyozULQIut+kZQxIiIiIiIiISr7NCtHqBqJo1HkccY0hCREREREREKlFCA7VMMcoFEFWqIPdxLBiSEBERERERkbphqyjnz8J1zkDQyx73EUMSIiIiIiIiEnDj1o6P2g5jYHG5TY8YkhAREREREZF64iwLcgYtZCdJN59RVwxJiIiIiIiIqDMsEOT8WcSlJwo7SXrEkISIiIiIiIjUcEQWZLmNmFe36fggUs19xJCEiIiIiIiI1GU2opxAC3l1G4FqjRVDEiIiIiIiIhJuU08h9yQR7D6OBUMSIiIiIiIigiyHP4oSGoi4J0mEKPdxLBiSEBERERERETtJBMBOkp4xJCEiIiIiIqLOzgpRNm7tIFJIohKx5l5iSEJEREREREQCbtwqXleFyEuMeoshCREREREREQm43CbyUZCCIeYSo75iSEJERERERESQBQsNhOyqEKnWGDEkISIiIiIios5OElH2JBEwJYlUKlDJfcaQhIiIiIiIiITLDESrF+Bym95gSEJERERERERqB4ksyAm0kIGBQKXGiiEJERERERERdYYFgoQGgpULQNBgqI8YkhAREREREZG6cas4W5KIFxiIGAz1VcKFJEuXLsXUqVMxduxYHDhwQL398OHDuPnmm3Httdfi5ptvxpEjR077GBERERERkTBE27g1QrByAYhZcy8lXEhy1VVXYdWqVcjPz+9y+6JFizB37lxs3LgRc+fOxcKFC0/7GBERERERkSg6uwzEOIMWrZMkuk5Rao5FwoUkRUVFyM3N7XKby+XC/v378YMf/AAA8IMf/AD79+9HY2NjzMeIiIiIiIhE0hkaaDyQASLy0hMRa+4ts9YDOBNqamowZMgQmEwmAIDJZEJ2djZqamqgKEpMxzIyMjSrh4iIiIiIaKApgi23Ea+TpMtXWg0j7ukiJNFaZmaq1kMgOqmsrDSth0B0UpyfFK84NymecX5Sf0lKsgIA7MnWmOdZIs3PpnoPgHB4kEjjjpUUktXPk5NtQtQcC12EJLm5uaitrYUkSTCZTJAkCXV1dcjNzYWiKDEd6wuXyw1ZkLSVEktWVhrq69u0HgZRtzg/KV5xblI84/yk/uRp9wMA2t3+mOZZos3P5maP+nldXSsMBoOGo+l/0SGJ2+1LqPsqVkajoc9NDQm3J0l3MjMzUVhYiPXr1wMA1q9fj8LCQmRkZMR8jIiIiIiISCSibdwaveJEhJKFuV9PU8J1kjz++OPYtGkTGhoacMcdd8DpdOK9997Do48+ikceeQQrV66Ew+HA0qVL1Z+J9RgREREREZEoxNuj48Srvei7kyT6XhXkLo5JwoUkCxYswIIFC75z+6hRo/DWW291+zOxHiMiIiIiIhKF2kkin/r79KJLUCBCaNClc0aEgmOji+U2REREREREdHpE6ySJJkLNXWrUf7kxY0hCREREREREUSGJxgMZIF2X22g4kAGisJOkVxiSEBERERERdWPf7mps3XRQ62EMmMh5syzICbTIoYFg5fYJQxIiIiIiIqJuVB9tRkV5o9bDGDCKLNZyG/E6SU7cqJa6w5CEiIiIiIioG7KsCHUyKfLGrSLczwKUeEYwJCEiIiIiol4JBiVsfHcf2lp8Wg9lQMiyAlkS58xStI1bReskid6tVYx6Y8OQhIiIiIiIeqWl0YtDZQ2orW7VeigDQpEVyLI4Z5OihSSiXRJXtM6ZWDEkISIiIiKiXomcWIkSHMjChSQdH4VZbiPYJXFFqPEMYEhCRERERES9EgkMRFmCEg5JBEkMENVJIsjZtGidFdy4tXcYkhARERERUa9EwhFhLhErK+oVX0TQ2UkiRs2i7UkSXaII9caKIQkREREREfWKcJ0kimDLbdRLAGs8kAEiWicJRFteFCOGJERERERE1Cude5KIsQQlfAlgQU6gIeLGrYJ1kogWCsWIIQkREREREfVKpKtCmOUYkc4ZUeqNfBSl3i5l6r/mriGJduOIdwxJiIiIiIioV2Qp3EEiSmggixaSROoVo1zh9iQRIQg6ExiSEBERERFRr0RW2YgSGgjXOaOc+Im+dd3IVP81c7lN7zAkISIiIiKiXlH3JBFk41bhlttE7l9RTqCjO0kEuI/F65yJDUMSIiIiIiLqFdGWn4hWr3oSLUa5Qu/RoYhyJ8eAIQkREREREfWKcKGBYJc8jgQFwty/XTor9F9zlxL1X27MGJIQEREREVGvdG7cKsglgBXBQiHBLgEsXCeJYKFQrBiSEBERERFRr4i2J4m6casgJ5TqahsxyhW6k0SAcmPGkISIiIiIiHpFXW4jyBlW53IbMTpnFMFCIdGWnCgn/YKiMSQhIiIiIqJekQXbo0O4PVginSTC1CtWJwmX2/SOWesBnGlTp06F1WqFzWYDADz44IOYMmUKSkpKsHDhQvj9fuTn52P58uXIzMwEgFMeIyIiIiLqSW11K5LsFqQPsms9lH6ldhoIchItXkgS6STReCADRLTlJ6LVGytddpI8++yzWLt2LdauXYspU6ZAURQ89NBDWLhwITZu3IiioiI8/fTTAHDKY0REREQUO3ebH1UVTVoPY0B8tL4Uu7ZVaD2MfidcaCBavcJt3CpWZ0XXGvVfb6x0GZKcaM+ePbDZbCgqKgIAzJkzBxs2bOjxGBERERHFbs+uKmx4Z5/WwxgQoYCEYEDSehj9TrSQRBasc6ZzuY224xgoXSIDMe5ilWj19oXultsA4SU2iqJg4sSJuP/++1FTU4O8vDz1eEZGBmRZRnNz8ymPOZ1OLYZPREREpAvBYDg4UBQFBoNB6+H0K0lShNjcU6SQRFEU9URShHoBdpLoXdflNvqvN1a6C0lWrVqF3NxcBAIBLFmyBIsXL8bVV1/dr//PzMzUfv33iU5HVlaa1kMgOinOT4pXnJtnhtVsgqKEXyuZTPpuYFYUBUajcUDmjpbz055kBQCYzQNTq5aiQ6+0NLvu6wUAkzH8d2o0xX7/JtLvKTKfASA9PTmhxh6LgDekfm6zWXRfb6x0F5Lk5uYCAKxWK+bOnYu7774b8+bNQ3V1tfo9jY2NMBgMcDqdyM3NPemx3nK53MKky5RYsrLSUF/fpvUwiLrF+UnxinPzzGlvDwAAao+3wmI1aTya/hUKSvB5g/0+d7Sen263DwAGpFathUKdIUlTUzvq620ajmZgBEPhJWOhoBTT/av1/Owrj8evft7U2I4Uh/UU3534mpo86uc+n/7/hgHAaDT0ualBV5G+x+NBW1v4jlYUBe+//z4KCwsxfvx4+Hw+7Nq1CwCwevVqTJs2DQBOeYyIiIiIYid1vBMffbKpR4qiQJIUtV49E2q5TVSNItQLRG1UK8hSDEHKVIm2vChWuuokcblcmD9/PiRJgizLGDVqFBYtWgSj0Yhly5Zh0aJFXS7zC+CUx4iIiIgodpHlCnoPDyIn0HqvExArJImuUZb0Xy8QvXGrGPVCtD06eAngXtFVSDJs2DCsWbOm22MXXXQR1q1b1+djRERERBQbqePEUtJ5J0nkBFoS4ERaEehqL9EhiRAn0IjeuFXjgQyQrp0VGg5kgHS9ArAABcdIV8ttiIiIiBJBu9uPf35RofsTL0mQThK1Tp2HQYBYnSRCLreJdJLo/LEpQrSrvYgWCsWKIQkRERHRADt8wIV/bDkCd6u/529OYKKEB5FOEqEuASxA10zX5Tb6v28BqGfOIgQGgNihgWDl9glDEiIiIqIBJkWuICFIeCBMJ4nO6wSiQhIBzihlATtJZNGW20R/LkDRStfWGe0GEucYkhARERENsEg4EgpKGo+kf0U6SPTeSdIZkuj/pEO9+okAgVD0CaUoIYloG7d2DQ20G8dAYUbSOwxJiIiIiAaYGpLoPTxQr/qi71fjsiAb1AJi7UnSZeNWAeoFojbmFaNc8a5uE0URIRWKEUMSIiIiogEmsZNEVyKdJLKs6P5ES9SQRBKgXkDEjVvF2pNEtM6ZWDEkISIiorjj8wbR2NCu9TD6TedyG32HB7Isxl4d0Z0yeg+ERApJFAE7SSJnzuKEJNGf679m0eqNFUMSIiIiiju7tx/Fuje+1noY/UYKCrLcJhR+Ea73OqP359D70iJFoJBEyI1bBVtuI1onSYTBIFa9fcWQhIiIiOKO1xOE1xPQ7TtdocjVbfS+3CayDEX3nSRyt5/rkbCXABYkJIkswRClc0a0zopIjQajgSHJKTAkISIiorgTDEhQFP12IEiiLLeRxOiYkYVcbqPvOoGuQYEoIUnkEsAiXOIZQJeURISSIzUa2UpySgxJiIiIKO4EOzosgn59dlp0Xt1Gn/UB4XcsI0tP9L4ERaxOknB9InQadO0k0ff9qlJO+KhzihJeehL+XICiozpJqo+14KP1pRoPKD4xJCEiIkpArc1etDR5tR5GvwkFwickgUBI45H0j0hIEtRxJ0mXK4PovLuiy8atOg+ERNqTpMt+FQLUC3TWLML9C4TrNRgjKYm2YxkIkSkdeUw+sLdWw9HEL4YkRERECWjLxoP49IMDWg+j36idJAF9dlqIcAngLktQdN5dEV2f3vdfiTRUKIr+33kXbU8SRVGE26MDAIwdIcneL6t1vzQwIno+v7RsCxrr9Xs1uVgwJCEiIkpAHncAnvaA1sPoN5FwJKD75Tb6fUHeZQmKjusExNqTRKR9OrqEJDrvEIoWWX5S8a1L24EMAEXpDEnqj7fhg7f3ovTr4xqPqv90F3zJsoLjVS0ajCZ+MSQhIiLda2ny4sjBBq2HcUb5/SH4vUGth9FvQmoniT6X24jQSSJWSCLQniSKOMFBl0BIgK6KSChkNIVPETe8s0/3f7uKosAQSYUAVB5pwifvl+m2i+bEsi7+3ggAQLtbv2+6xIIhCRER6d7XOyvx4dpvdPWiJ+APwe8L6aqmaJHlNgGdLrfpvASwfk9ApJCYy230vidJdCDEThKdiVz5pKOzQlGA/3uuGGU63rciupMkmkfnoYHVZoLVZkLR9wqQnGKFu9Wv9ZDiCkMSIiLSPU97AKGQrJv9LWRZQcAvQZYV3dQUTVE669JjfUBnOCLOcht9n2BKAi23EWmfjuj6RNi4NRK6Rz/u+n0hfP7hQa2G1O8URYHZHD4lLhiTietvHA8AaNNpaBC5j2f/6ELc8cvLAQCpDhva2/RZb6wYkhAR0Sm1u/0J/y6wr2NZitejj+Up0UtQ/D79LUeRQrLaEqzXTpLI35Sel9uItARFpEsAKwJdFje61rK9taitbtVwNP3vZI2JaelJAzuQAaQogM1uwQ9vuxDX/Os5SHXYAADuVp/GI+sfkfvYYDSoHTQpaTZ2kpyAIQkREZ1UKCTjjT/sxP7dNVoP5bREwhGvTjY6jQ5GfDrclyT6srhBv/5CIEVRBOkkEai7QqBLAMsCXRb3xE6ZtatK8JeV29HWotcT6HC9hRNyMOGSobj13y7BiNGZaGvx6XZpZyQ1yM51wGQyqoFQxbeNulxy0939mJpmQ5PLgwP79Lusqq8YkhARDYB9u6vR2JB4l1dzt/oQDEhwJfil4fTWSRIdkuixkyS61VuPnSTRJ9R67iQRqbtCqE1qZQWmjuUJ69/co+slNydu1ipJCtytfhw70qTRiPpX5AR60OAUXDZ1FBxOO/KHpyPgl3T5XAOEM5LojVutNjMA4MC+Wny0/huthtXvokqGPdkCAPhoXSmqKvQ5t/uKIQkRJaS9/6zC39/4Suth9EowIGHLxoPY888qrYfSZ5H2y0R+10xRFPginSQefbwrFPDrPCSJCg70uCdJdPeInjdulUUKDqKCr882HICrzq3haPpXdK1NDR68tGwLPttwQMMR9Z9Ip0xOvgMZWSnq7Q21+rx/1aUYUSfQDqcdALDjs8O6fDwOX92m+2OVR5rRUOtGeWk9vvi4XBfdNJ33cWfRucPSYbWZkWS3YMvGgygvrceqF3fgeKW4lwVmSEJECamivBFVFc3w++K/M6C50RP+6PJoPJK+00NIEvCH1BcF3vb4ny+94fd1vlDV5XKbgL5DkujAIHKVGz2KLDux2kwI+PVbJxDuJIl0VwDAlk0HcbxKn/tXKIqi7tsQsb+kRpfdQpEumet+OB43/6QIP/nV5cgbno76mjaNR9Y/IiFA9Am0wxlefrK/pAbvv70XPm8QWzcdxKGyek3GeKaFO0m63nbR5OEoGJMJq82EDe/sw6Y1+/HVPypRfbRZm0GeSep93HlT3nAn7vz3y3Dt7HPQ2uLDpjX70drsw1c7KzUapPYYkpBuNbk8+LL4aFylvhXlLmz/9JDWw+iWxx3AX/+4C/XHE+OJP7J0xVUX/8tAWpq8AIBml1fjkfRdW8fGZW2tibseOXqJjUc3e5IEoz7XXydJ9BKUgA73JIkEI1abCX5fSLddFpGT5vwRg9BQ58ZnGw6gotyVsI8lpyJJMqw2k/r18cpWvPuX3WhKwHC8J7KsoGB0Jm6bPxkXTBqm3v7NV8ehKIquHpMiIUlkg0urzYzsXAfqatqwed03+OLjcuz8/AiKPzmkviGSyJSOh6LokMSZmYyC0ZkYf1Eeqo8247UXdmDvl9X49IMDaHJ58PXOyoR+DAt3knRNSSZdMRLTfjgek68chbYWHwrGZCIp2YKSHZXYsvEg9u2uVl/bJZqTPfwaDAbkDXfih/MuwtTpYzHmnGwc+daFTWv2Y/UrO7Hjs8Ooq9Fn8Nsds9YDiBeHDx/GI488gubmZjidTixduhQFBQVaD+uMKd1zHO5WP4ouH9Hv/6+N7+6DMzMZk/5lZL//v05ld/FRlO2txbCRg5CVk6bpWCK+LD6K45WtOOeCXLV9MV4cPtiAxvp2lO2tjZvf18kE/CG1w6Gxvh15w50aj+jUIh0knvYA/L4QbEmJ89Ab+T3LkoJ2dwCpabYefiL+RHda6GVPkujgwKeTmqJFukeSU61obGhHMCDBbDGqxyJrxhNVZLnNWWMsS7z4AAAcdklEQVSzUPr1cXy++Vvkj3AiJz8dRqMByalWjUd4ZkSWZZw3MR+HDzRgf0kN9pfUYEheGiZfOQo5Qx04VNaArJxUGI0GuOraMWJ0psajjo0sKbBazZg6fRwcziTs312Dr3ZW4tMPynDJlAL4vCEk2S2oPtqM9Aw7xpyT/Z0Ts0Qhy0p4nqZYMfnKs1D0vRF47YUd2LrpIEq2H4W7zQ+H0w6vJ4DcoemYcs2YhL06inJCSAIA516YB58niNI9x7t8777d1SgYnQlFUXDxlAIA4S7MoQWDEua+VrrpMjCZjJjWcVlcADh8oAGXTT0LWzYexJv/uwuypOBQWQNgAIbkpeGaGefC4w6g3e2P+9eTQEdocJK7p3BCDrJyUpGZnYpd2yrwz20VXY6POScbF0wahlBQQuWRJhRekIuU1MR4nXSyOTl4SCoGD0nFkHwHKspdKC8Ndww1NRxFyT+OoXBCLkaPy0JmdgqOHHQhIysFWTlp8HoCcLcmxn3eG4n9KuMMWrRoEebOnYtZs2Zh7dq1WLhwIf785z/36mdlWYmLTaskSYbfG4I9xdJl4gf8IWzb/C0Cfgkjx2Ri0OCUU/wrp6eupg2HyhpgMhkwpjAbKWk2mC3GAX9ykGUFR751AQC+3lWFy68apb6oDgYkSCEZZosRioIBG5/XE8DxynACe2BvLcZPzIfFaoLBYFDfeTGZwuMwGo2QJBlmsxGGyBOzAigAoChQZ1vHJ5Enta7psAKvJwCfN6jeLkkyTCYDbEmW74zvaHkjgPCT3/lFQxEKSkjPsKu/m2BAgqc9AFmSIcsK0gfZO1uLezG2ruNTuoxVUcLfJ4VkJKdYO2s+iehNRL/aWYmhIwfBnmyB0WiELCvw+4Iwm01QFAXuNj9sSWb4fSE4M+wwW0zdj7mX4418jFwaLiMrpcf509zY+W7Dzs+P4LyJ+bDaTAgF5Y7gwQqT2QSP24+2Fh+cmckwm41ISraGX6goiPq99m28kdulkIQmlwdD8hydv4NeiL4E3ro3vsIV150NrycIo9GAwUNSoSgKjEYjjnzrgizLKBg9GEYjYE+2hl90nGLsfl9IPdk/1dhDQQktTV4MyXN0aWePpigKvO1B2OxmmExdvyeyxMZsMaK8tB77dlfDmWFHW4sfQ/LSIMsKrDYzaqtb4aprR/4IJ3KHpsPnDcJiNanjMJkMCIVk9cWyzxuE2WKCxWqCLMkwGAwwmY3qfIYSeRdSgSwDxytbYE+xIjv35C8gggEJkiTDlmQ+5bzydbxTm5WTij1fVsFsMcJiNUGSFJhMBqSlJ8GWZEZbiw/1x91wZthx7kX58HmC8HoC6rhP/F2dyNMeQNAfQlZOGhR01qUoilqnLHf87aZaz9hjaWReXDKlAJ9+cAB/fn47ZFlGmiMJ7jY/zi/KR2ODBw5nEvy+EBzOJIwdP0T9OwoEJMiSDKvNDJPZCCkkw55sgSxDDSmNRgO8ngBam33IyXfAGPW7iDzORX/t8wZhTzkzNUZCoILRmWht9qrhQcSYc7LR7vbDlmSBFJKRkmbFeRPzYbGacbyqBVabGYqswJ5sQUqaDQaDAZIkIxiQutwPUkhGk6sd2bkOdS53R5HDzxdJdkuX38PpinQEpaRZ8YObz4PZbEJzowf/2HoEa1aVwJ5sgdcT/juL/E4umVKA3GHpqChvREZWCqw2E1JSbZBlGSaTEe1t4d+LM9MOkyn891Z/vA2Z2eHHYrPF1OWENtqZvh+71BqSYTQZMPysDADAZVeNgmOQHTs+O4S/v/H1d77/y+KjKOgIhBrq3Bg5ZjCS7JbwaxNZQSgko9nlQc7QdCTZLbAlmZGcYkVzkxd+Twgwhk9k290BONKTvvO8GXksSbJ/9/n+dMmS0uV3bLGY8P/unIj9JTXYt7saowuzEfCHMLRgEA7ur8VrL+zA4CGpsFhM8LQHkDssHRarCe5WP8aOHwIg/HianGKF1WZGeoYd5o7H+rqaNgwekgpZUuDzBZGaZuvyHBbZc8piNfXpua23It1Q0b9fhzMJV04fi4IxmTCaDBicnYpQSMbbr/4TB/fXAQC+/aZzKUp2bhqG5Dlw9FAjho0chLzhTjS7PBg+KgN+XwjBgIScoQ6YzSaEQjIa691ISrYCigJ7sjX8eiHqsugnTt3w14Zubot8fuIPRH13x7HI12q9J/n7+N7Vo/G9q0fDYDCgoa4d+76shsEAtLb4YLYYUbKjEiU7OpdonF+Uj0GDk1F5pBk5+Q7kDktHyT8qkeawoWB0Jr4trcf4i/JgNhvh84bfRIrcj93X2fdau/zECfXC0H0nSfS/FznpH39RHnZvP4o0RxL+5doxqDrajJLtx9T7HAB2bavA6MJstLX64G7146LJw+H3hVBzrAWTrzwLJrMR+3ZXI3doOjKzUyCFZCQlWwAYYqq3x5q7qbe3l+12ZiRj5i0TUHmkCRMuGQa/L4jiTw6h9Ksa7PuyGlabWX2uzs5Ng6c9HJKcNXYwAv4Qxp2fi9yh6agod8HvCyEnP/waLsluQZLdAoMB6nOXyWSE0WRQz4Us1u4jCikkw+8PwZ5sOeOP4ScyKHrseewjl8uFa6+9Fjt27IDJZIIkSZg0aRI2bdqEjIyMHn/+mcc/SoiWK5PJMCCXpTNbwi9I42FmRf8Bx4skuyVu9xCIhAmJwuFMQmtzYuyV4cywdwlLEknecCc87QG0t/kTen+IKVePxjdfH9fNhnu2JDNu+dnF+OyDAzh80HXK70ukv+tot907GVVHm1F9rBk+TxDHDjfB4UyCq64d9hQLvO3BuH5M7cmMOecjZ2g6XHVuyLKCinIXfJ4QykvrYE+2wusJwJ5iRXubP6E3eP3xPZd22cMiGJBQ+vVx1Fa3IisnDa46N/z+ECqPNCV0nVk5qbjx9oldbgsGJBzYV4skuwUtTV6MOz8HR8sbUba3FscrWyDLijqXE8klUwowsRfdyUcPNeLj90rhSE9CKCTD4bTj6KFGKEo4nE6UTrif//pfThq+RVR860JNZQsKJ+Si+mhzOECWZJR9XYtGVzvS0pPQkiCvA66cPhbjzss55ff4vEHs3HoEEy8fgeSUcPdb5ZEmeN1BtDR70FDXjsMHGgCgSxBqNBri4s3laPkjnJh5y4Qev+9QWQNS0qwYkucAEN5zrv64G2azEe42P774uLwjvFWQkZWCuurwEvZ4rHnevZfG1PXi8wZxqKwen3/4LSRJwZC8NChK+E2Vs84ejH27q6Eo3718tpacGXbc959X9elnGJIA2Lt3Lx5++GG899576m3XX389li9fjnPPPbfHn9+x9XBcvAA1Gg1Islu6XXM/KDMZySlWVA3AhkNDRzhhMhnRUOdGICBpdlJlSzJj9LgsVJQ3wu8PqeMwm41qkAODAaGgNGCBTprDhuFnZeDooUYE/CEEAp3/76QkM6SOd6MjG8CFgnKXToFIatpdam4whP8TnRZHUuTIbSazEcGg1O0GegYDcM6EXBw+2ABZVmCxmNDWsdQCCP/e0hyd7+A0NrSrD4DRae6pxtYxrC5ji9wWfhfegHZ3oFf3R5rDhtGF2XDVt6O+pg2SJEOSwu/yW21mhIISjEYjUtKs8LQHkOZIQmNDe5erSpz4+/zO7zfyu+vmd5mcYkUoJKO1lxuann1ONizW8OaF1ceaEQpKMFtMSEm1wt0WgCzLsNstGDQ4BTXHWmDoeJf7xLH2abxRtxmMBjicdtTVtPZ5vheel4OsnDT4vEEc+dalXirOVe9Wu56ycx1ITrHg29J6mEzG0x971NwwmsJzr+542ynHnpxigc8b6vaJOc1hw4WThkNRFFQdbYbPGz65bqh1w2ozwesJIjsnDdm5DpSX1aGhrh12uwWBQCj8wthggBSSYbEYISvhd6STU6wIBWUEAiGYTEb1BbHBYFDfITEYDTB2fD5ocApam71od598XxSz2QiT2dir55QheQ6MPXcIFEVBY0M77HYr5I53VYNBCT5vEA5nEjIGp+DAvlrU1rQhJdWKlFQrgkEZAX+ox40WrVYzzGYjmho936kr8rnRYIDRZDhlXbGI3GfRFFkBDOETz8iLbovVhGNHmnC8sgWSrCAtzQZ7ihWmjjFFHs/a3X4YjQY1UJFlBbYkC1LTrKg7furgzGAId0edyT1trDYTii4bAbO553e/W5q8OHSgHooS7l5DxwlmW4sP7W5/uCvSHO4mam8LINLTZzAYkD7Ijtrqnv/uk1PCocyZfj5MTbPhwknDevWun98XQtXRJgQDEnKHpsPdFq6trcUHoyn8N5iWngRPewBNLg+kULjjZ3B2ChobPDCaDAgF5ZO+OO+P+zHaiLMyMGJU75cLBYMSQkEJtqRwgBJ5XQCEu6nyhztx7EgTQkEJXk8Q7W4/klOsSLJb0NrigyzJSEm1dfs81JfHkr4yGg2YUDQ05iU0kiTDaDBAkmUcPdQEo8kA5yA73G3+bu/bhrp2WCxGJKfa0Nbi6/I8DkB9rO6vNwIzBidj/IX5p/VvKLICg9GA5kYPGurcyMlzoLysHrYkC5JTrTh2uDH8OshkgDMzBb6Ojj+POwC/PwSLpaOzONJFCqgtmNEdvNGdm1EfOj7vrvP0u8dMJiMuunT4GelCCoUkNDV44MxMRm11K+pq2jD23CGoOORCwC8hJ9+BwwcbYEsKX1nF6wmGn5ei6+wY8Hc6lXtZ64nHTjyuKMCosYORP3zQaVYLtTM80tW3v6QaGVmpsFpNOHywAQqA4SMz4G4Ld5qYzMbwc9LJalK/jvrQbff4qWs+8Vj4cbnr82tfVVY0wWwxIacjMIoI+MOvw/burgYQ7obKGpKKdncAwaAEb3sAXk8QsqxAURT19VMoJMNmM0GWFQRPEpaHz3XN8PQxVE5OsaDosoI+/QxDEpx+SOJyueMqLSOKyMpKQ319YmzESuLh/KR4xblJ8Yzzk+IZ5yfFG6PRgMzM1L79TD+NJaHk5uaitrYWkhRO7yVJQl1dHXJzczUeGRERERERERENFIYkADIzM1FYWIj169cDANavX4/CwsJe7UdCRERERERERPrAq9t0ePTRR/HII49g5cqVcDgcWLp0qdZDIiIiIiIiIqIBxJCkw6hRo/DWW29pPQwiIiIiIiIi0ghDkjOgp0uCEWmJ85PiGecnxSvOTYpnnJ8Uzzg/KZ7EMh95dRsiIiIiIiIiInDjViIiIiIiIiIiAAxJiIiIiIiIiIgAMCQhIiIiIiIiIgLAkISIiIiIiIiICABDEiIiIiIiIiIiAAxJiIiIiIiIiIgAMCQhIiIiIiIiIgLAkISIiIiIiIiICABDEiIiIiIiIiIiAAxJeqW5uVnrIRARERERERFRPzM9+uijj2o9iHglSRJWrFiB3/3udzh8+DCampowbtw4rYdFpHr77bfh9XqRlJQEu90OWZZhMBi0HhYR5ybFtddeew2NjY0wmUxIT0+HoiicnxQ3OD+JiLTFTpJTePnll3H48GH84Q9/wMSJE7FkyRLs379f62ERobS0FNOnT8cHH3yAt99+G7/61a/g9/thNPJPmrTFuUnxrLy8HDfccAM+++wzfPHFF/jZz36G2tpanoBSXOD8pHj3/vvvY82aNSgvLwcAKIqi8YiI+gc7SU4iGAzinXfewZw5c3DOOedgzJgxaGxsxCeffIIpU6bAarVqPUQSTPQ7SR9//DFGjBiBxYsX44orrsBHH32Ejz76CNOmTdN4lCQizk1KFCUlJZBlGcuXL8eUKVOwb98+fPzxx7j88sths9m0Hh4JjvOT4lVLSwvmz5+PXbt2wWKxYOXKlRg9ejSGDRum9dCI+gXf2jsJi8UCv9+PDRs2qLfNnz8fhw8fRnFxsYYjIxEFAoEu7yTt2rULx48fBxCeq0899RSKi4uxc+dOAEz2aeBwblI8CwQCCIVC6tf//Oc/0dTUpH79n//5n6iqqsK2bdu0GB4JjvOTEkV9fT3S09Pxxhtv4OGHH8Ytt9yCZcuWoaqqSuuhEfULhiSncOedd2Lnzp2oqKgAACQnJ2PWrFl49dVXtR0YCeXVV1/FnDlzsGzZMrzzzjsAgOnTp6OsrAwNDQ0AAIfDgR//+MdYvnw5ALA1lwYE5ybFs7/85S/413/9VyxevBgvvPACAGDGjBkoLi5GTU0NACA1NRUzZ87EK6+8ouVQSUCcnxTv3G63+nlpaSmOHDkCAJBlGfPmzUN2djbWrFkDgG+AkP4wJDmFcePGYfLkyXj66afV24qKijBy5EgEAgENR0aiWLNmDTZt2oRFixahsLAQv/vd71BcXIwLLrgAI0eOxOrVq9XvveGGG2Cz2dR38Yn607vvvsu5SXFr06ZNWLduHVasWIEbbrgBf/nLX7Bu3TqMHj0a3//+9/E///M/6vfOmTMHgUBAXWNP1N82b97M+Ulxa/PmzZg9ezYWLVqEpUuXAgCuu+46NDY24rPPPlP3GLvrrrvwxhtvwOfz8Q0Q0h2GJKdgtVpxzz33YN++fXj++eexdetWLFu2DNnZ2dyThPpNdHJfXFyMW265BRMmTMCMGTPw85//HIsXL4bdbsf3v/99bN26VV3+dfDgQeTm5iInJ0eroZPO1dbWwuPxAAC++OILzJ07l3OT4kZ1dbXa0VRWVobrr78ehYWFuOCCC/DII4/g9ddfR2VlJW699VZs27YNmzZtAgB88803GDlyJNfWU79qaGiAy+UCEH5Xftq0aZyfFHfKy8vx3HPP4Ve/+hXuvfdebN++HcuXL4fZbMa8efPwpz/9Sf3eoqIiFBQU4PPPP9dwxET9gxu39sBut+PCCy9EdXU13nrrLcyaNQs/+clPtB4W6VAoFMIzzzyDV155BUePHkVmZiZCoRA+/fRTXH/99QCACRMmYNWqVVAUBbNnz0Z7eztefPFFlJSU4G9/+xumT5+O8ePHa1wJ6dHXX3+N6dOnIyUlBRMnTkRpaSl27typbsjKuUlaURQFK1aswDPPPIPhw4dj4sSJ2LdvHzZs2ICbbroJADB27Fhs3LgRzc3NuP766+FwOPDmm29i8+bNeOutt3D11Vdj4sSJGldCeiRJEp599lksW7YMxcXF8Hq9GDp0KF5//XXcfPPNADg/KX58/vnnkGUZt99+OwYNGoSioiL84Q9/wJgxYzBp0iSsXbsWVVVVuPTSS9Hc3IwtW7bgxhtvRFpamtZDJzqjGJL0wpAhQ1BUVIQbbrgB5513ntbDIR3avXs37rnnHmRlZeH222/H4cOH8c477+Cqq67C7t27kZ2djfz8fABAXl4e/vd//xe33HILLrzwQlx66aVISkrC/fffj4svvljjSkivDh06hO3bt6OhoQGTJk1CVlYWvvjiC+Tk5HBukqa2bNmCTz/9FH/+859xySWXAAAKCgrw7rvvIjMzE2eddRaA8Pz8/e9/j3nz5uHcc8/FlClTkJGRgXvvvReTJ0/WsgTSKVmW8fjjj6OlpQX//d//jREjRuCpp57C/Pnz8d5773F+kubefPNNVFVVwWAwYNCgQWhsbMRLL72EO++8EwCQkZGBiooKbNu2DT/84Q8xbtw4vPbaa9ixYwdWrlyJ8847D9dee626BIdILxiS9AHX21F/cblcGDJkCO655x7k5OTAarXim2++waWXXgqPx4MtW7bguuuuAwC0tbXB5XJh8uTJMJvNyMjIwLhx42C32zWugvRsz549uOaaa1BdXY0DBw5g+vTpaGhowCeffMK5SZr68MMPMXr0aFx44YXYvn07vvrqKwQCAYwcORIvv/wy5syZAyC8+XppaSkuvvhiJCcnIyUlBWeddRbnJ/Wb1tZWvPDCC3jiiSeQmZmJkpISpKWlYdy4ccjOzsYrr7zC+UmaqK6uxk9/+lMcOXIEwWAQzz33HC677DKcf/752L59OyorK9U3NyZOnIglS5bgyiuvRGFhIa666ipkZWXhpptuwuzZsxmQkC5xVhPFgbFjx2LmzJnq7uApKSmoqqrC+PHjMWPGDDQ3N2Px4sU4duwYXnnlFRgMBiQnJzO4owFTUVGB6upq/PrXv8aWLVuwc+dOdTPWxx57jHOTNFNZWYk9e/bg/fffx7Jly1BeXo577rkHqampGDRoEH79619jx44d+O1vfwtFUZCZmcn5SQMiPT0dTqcT//Ef/4F58+bhmWeegSzLuPXWW5GWlgan08n5SZo4duwYzj77bPzpT3/CI488giuuuAJPPPEEXC4Xbr31Vqxduxa1tbUAwgHe9773Pfj9fgBAZmYmLr/8cowZM0bLEoj6FUMSojhgsVi6nFju3bsXBQUFMBgMKCgowJIlS2AwGPDQQw9h+PDheOyxxzQeMYkmEAhgwoQJyMnJgcPhwN133w2LxYIVK1YAAOcmaWbu3LkoKSnBli1bsGrVKtx333144IEH8H//939YsWIFzj77bLz00ksYPnw4nn32Wb7rSQPqpZdewt13341BgwZh48aNWLBgAX75y1/i1Vdf5fykASNJkhp6AOHu0Lq6OvXr3/zmN/B4PPjkk09wxRVXYNKkSfj3f/937NixA8uXL8eRI0fUpbVEIjBrPQAi6iRJEkwmE/bt26eurd+wYQNGjRqF//qv/4LX62XrLWnCZDLhiSeegNfrRV5eHtra2tT9SDg3SUsjR45EUVERiouL1Tl44403Yu3atVAUBXfddRd+/OMfw2azaTxSEpHNZkNTUxNkWYbJZAIAzJ49G3/72984P2lArFu3Dg8//DAuv/xyPPvss7Db7Zg+fTpeffVVHDx4EGPGjIHBYMCcOXPwwgsv4MYbb8Rvf/tbvPrqq/jrX/8Km82Gl19+GU6nU+tSiAYM42qiOGIymaAoClwuF7xeLx544AGsXr0asiwDAE9CSTMZGRlIS0vDAw88gOeeew633XYbPvjgAwQCAQCcm6Qdq9WKBx98EMFgEKtXr0ZxcTF+/vOfo7CwUH1RzxNQ0tK4ceNQVlaGv//97ygtLcU999yDcePGIT09HQDnJ/Ufj8eD4uJiPPHEE6ipqcG2bdsAADk5OZgxYwaef/55AOFNhmfNmgW73Y4vv/wSAHD77bfjqaeewpNPPomMjAzNaiDSgkGJbIJARHGhvLwc06dPx/nnn4+bbrpJvYQlkZb8fj9fyFNc+/rrr7F792588sknmD59Oh87Ka68++672Lx5M6qqqnDrrbdyftKAqaysxNChQ/HHP/4Rmzdvxosvvoj09HQcPXoUP/3pT3HXXXfhpptuQnl5OVasWIFly5YhNTVV62ETaYohCVGccbvdeP3113H77bfDarVqPRyiLiJLwhRF4eaCFJdkWea+DhSX2tvbYbPZYDZztTtp44YbbsCMGTNw2223wWg0YvPmzfjjH/+IrKwslJWVYebMmfjFL36h9TCJNMeQhIiIiIiISKci4fFHH32E5cuX480334TD4QAQ7hTdunUrxo8fj5ycHI1HShQfGJIQERERERHpWCQoeeCBB+Dz+VBdXY1LLrkEv/nNb7QeGlHcYb8fERERERGRjkWWIba0tGDv3r247777MHfuXI1HRRSfGJIQERERERHp3FtvvYXRo0dj5cqV3PeO6BS43IaIiIiIiEjnuOk6Ue9w+3ciIiIiIiKdY0BC1DsMSYiIiIiIiIiIwJCEiIiIiIiIiAgAQxIiIiIiIiIiIgAMSYiIiIiIiIiIADAkISIiIiIiIiICwJCEiIiIiIiIiAgA8P8BsLjEl/R4fjgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1296x1296 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plots for the field after data cleaning \n",
    "data.plot(subplots=True,figsize=(18,18))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(\"** Countries, territories or areas with reported confirmed cases, Deaths, Recovered of 2019-nCoV, 31 January 2020**\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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>Province/State</th>\n",
       "      <th>Country</th>\n",
       "      <th>Last Update</th>\n",
       "      <th>Confirmed</th>\n",
       "      <th>Deaths</th>\n",
       "      <th>Recovered</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1127</th>\n",
       "      <td>2020-02-10 19:30:00</td>\n",
       "      <td>Hubei</td>\n",
       "      <td>China</td>\n",
       "      <td>2020-02-10 23:33:02</td>\n",
       "      <td>31728.0</td>\n",
       "      <td>974.0</td>\n",
       "      <td>2222.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1128</th>\n",
       "      <td>2020-02-10 19:30:00</td>\n",
       "      <td>Guangdong</td>\n",
       "      <td>China</td>\n",
       "      <td>2020-02-10 13:43:02</td>\n",
       "      <td>1159.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>167.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1129</th>\n",
       "      <td>2020-02-10 19:30:00</td>\n",
       "      <td>Zhejiang</td>\n",
       "      <td>China</td>\n",
       "      <td>2020-02-10 14:13:12</td>\n",
       "      <td>1092.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>242.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1130</th>\n",
       "      <td>2020-02-10 19:30:00</td>\n",
       "      <td>Henan</td>\n",
       "      <td>China</td>\n",
       "      <td>2020-02-10 13:53:02</td>\n",
       "      <td>1073.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>191.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1131</th>\n",
       "      <td>2020-02-10 19:30:00</td>\n",
       "      <td>Hunan</td>\n",
       "      <td>China</td>\n",
       "      <td>2020-02-10 13:53:02</td>\n",
       "      <td>879.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>208.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Date Province/State Country         Last Update  \\\n",
       "1127 2020-02-10 19:30:00          Hubei   China 2020-02-10 23:33:02   \n",
       "1128 2020-02-10 19:30:00      Guangdong   China 2020-02-10 13:43:02   \n",
       "1129 2020-02-10 19:30:00       Zhejiang   China 2020-02-10 14:13:12   \n",
       "1130 2020-02-10 19:30:00          Henan   China 2020-02-10 13:53:02   \n",
       "1131 2020-02-10 19:30:00          Hunan   China 2020-02-10 13:53:02   \n",
       "\n",
       "      Confirmed  Deaths  Recovered  \n",
       "1127    31728.0   974.0     2222.0  \n",
       "1128     1159.0     1.0      167.0  \n",
       "1129     1092.0     0.0      242.0  \n",
       "1130     1073.0     6.0      191.0  \n",
       "1131      879.0     1.0      208.0  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "from datetime import date\n",
    "data_2_feb = latest_nCoV_data[latest_nCoV_data['Date'] > pd.Timestamp(date(2020,2,2))]\n",
    "data_2_feb.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "import plotly.express as px\n",
    "pxdf = px.data.gapminder()\n",
    "\n",
    "country_isoAlpha = pxdf[['country', 'iso_alpha']].drop_duplicates()\n",
    "country_isoAlpha.rename(columns = {'country':'Country'}, inplace=True)\n",
    "country_isoAlpha.set_index('Country', inplace=True)\n",
    "country_map = country_isoAlpha.to_dict('index')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getCountryIsoAlpha(country):\n",
    "    try:\n",
    "        return country_map[country]['iso_alpha']\n",
    "    except:\n",
    "        return country\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Province/State</th>\n",
       "      <th>Country</th>\n",
       "      <th>Last Update</th>\n",
       "      <th>Confirmed</th>\n",
       "      <th>Deaths</th>\n",
       "      <th>Recovered</th>\n",
       "      <th>iso_alpha</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1127</th>\n",
       "      <td>2020-02-10 19:30:00</td>\n",
       "      <td>Hubei</td>\n",
       "      <td>China</td>\n",
       "      <td>2020-02-10 23:33:02</td>\n",
       "      <td>31728.0</td>\n",
       "      <td>974.0</td>\n",
       "      <td>2222.0</td>\n",
       "      <td>CHN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1128</th>\n",
       "      <td>2020-02-10 19:30:00</td>\n",
       "      <td>Guangdong</td>\n",
       "      <td>China</td>\n",
       "      <td>2020-02-10 13:43:02</td>\n",
       "      <td>1159.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>167.0</td>\n",
       "      <td>CHN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1129</th>\n",
       "      <td>2020-02-10 19:30:00</td>\n",
       "      <td>Zhejiang</td>\n",
       "      <td>China</td>\n",
       "      <td>2020-02-10 14:13:12</td>\n",
       "      <td>1092.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>CHN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1130</th>\n",
       "      <td>2020-02-10 19:30:00</td>\n",
       "      <td>Henan</td>\n",
       "      <td>China</td>\n",
       "      <td>2020-02-10 13:53:02</td>\n",
       "      <td>1073.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>191.0</td>\n",
       "      <td>CHN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1131</th>\n",
       "      <td>2020-02-10 19:30:00</td>\n",
       "      <td>Hunan</td>\n",
       "      <td>China</td>\n",
       "      <td>2020-02-10 13:53:02</td>\n",
       "      <td>879.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>208.0</td>\n",
       "      <td>CHN</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1194</th>\n",
       "      <td>2020-02-10 19:30:00</td>\n",
       "      <td>Los Angeles, CA</td>\n",
       "      <td>US</td>\n",
       "      <td>2020-02-01 19:53:03</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>US</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1195</th>\n",
       "      <td>2020-02-10 19:30:00</td>\n",
       "      <td>Madison, WI</td>\n",
       "      <td>US</td>\n",
       "      <td>2020-02-05 21:53:02</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>US</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1196</th>\n",
       "      <td>2020-02-10 19:30:00</td>\n",
       "      <td>Orange, CA</td>\n",
       "      <td>US</td>\n",
       "      <td>2020-02-01 19:53:03</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>US</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1197</th>\n",
       "      <td>2020-02-10 19:30:00</td>\n",
       "      <td>Seattle, WA</td>\n",
       "      <td>US</td>\n",
       "      <td>2020-02-09 07:03:04</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>US</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1198</th>\n",
       "      <td>2020-02-10 19:30:00</td>\n",
       "      <td>Tempe, AZ</td>\n",
       "      <td>US</td>\n",
       "      <td>2020-02-01 19:43:03</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>US</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>72 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Date   Province/State Country         Last Update  \\\n",
       "1127 2020-02-10 19:30:00            Hubei   China 2020-02-10 23:33:02   \n",
       "1128 2020-02-10 19:30:00        Guangdong   China 2020-02-10 13:43:02   \n",
       "1129 2020-02-10 19:30:00         Zhejiang   China 2020-02-10 14:13:12   \n",
       "1130 2020-02-10 19:30:00            Henan   China 2020-02-10 13:53:02   \n",
       "1131 2020-02-10 19:30:00            Hunan   China 2020-02-10 13:53:02   \n",
       "...                  ...              ...     ...                 ...   \n",
       "1194 2020-02-10 19:30:00  Los Angeles, CA      US 2020-02-01 19:53:03   \n",
       "1195 2020-02-10 19:30:00      Madison, WI      US 2020-02-05 21:53:02   \n",
       "1196 2020-02-10 19:30:00       Orange, CA      US 2020-02-01 19:53:03   \n",
       "1197 2020-02-10 19:30:00      Seattle, WA      US 2020-02-09 07:03:04   \n",
       "1198 2020-02-10 19:30:00        Tempe, AZ      US 2020-02-01 19:43:03   \n",
       "\n",
       "      Confirmed  Deaths  Recovered iso_alpha  \n",
       "1127    31728.0   974.0     2222.0       CHN  \n",
       "1128     1159.0     1.0      167.0       CHN  \n",
       "1129     1092.0     0.0      242.0       CHN  \n",
       "1130     1073.0     6.0      191.0       CHN  \n",
       "1131      879.0     1.0      208.0       CHN  \n",
       "...         ...     ...        ...       ...  \n",
       "1194        1.0     0.0        0.0        US  \n",
       "1195        1.0     0.0        0.0        US  \n",
       "1196        1.0     0.0        0.0        US  \n",
       "1197        1.0     0.0        1.0        US  \n",
       "1198        1.0     0.0        0.0        US  \n",
       "\n",
       "[72 rows x 8 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "latest_nCoV_data['iso_alpha'] = latest_nCoV_data['Country'].apply(getCountryIsoAlpha)\n",
    "latest_nCoV_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
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       "                        {\"responsive\": true}\n",
       "                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('089a6e65-13e9-4fd0-a31f-8ae45eb44abc');\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",
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       "var outputEl = gd.closest('.output');\n",
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       "    x.observe(outputEl, {childList: true});\n",
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       "\n",
       "                        })\n",
       "                };\n",
       "                });\n",
       "            </script>\n",
       "        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_plot = latest_nCoV_data.groupby('iso_alpha').sum().reset_index()\n",
    "fig = px.choropleth(data_plot, locations=\"iso_alpha\",\n",
    "                    color=\"Confirmed\", \n",
    "                    hover_name=\"iso_alpha\", # column to add to hover information\n",
    "                    color_continuous_scale=px.colors.sequential.Plasma)\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Country\n",
       "Australia                  15.0\n",
       "Belgium                     1.0\n",
       "Cambodia                    1.0\n",
       "Canada                      7.0\n",
       "China                   42310.0\n",
       "Finland                     1.0\n",
       "France                     11.0\n",
       "Germany                    14.0\n",
       "Hong Kong                  38.0\n",
       "India                       3.0\n",
       "Italy                       3.0\n",
       "Japan                      26.0\n",
       "Macau                      10.0\n",
       "Malaysia                   18.0\n",
       "Nepal                       1.0\n",
       "Others                    135.0\n",
       "Philippines                 3.0\n",
       "Russia                      2.0\n",
       "Singapore                  45.0\n",
       "South Korea                27.0\n",
       "Spain                       2.0\n",
       "Sri Lanka                   1.0\n",
       "Sweden                      1.0\n",
       "Taiwan                     18.0\n",
       "Thailand                   32.0\n",
       "UK                          8.0\n",
       "US                         12.0\n",
       "United Arab Emirates        8.0\n",
       "Vietnam                    14.0\n",
       "Name: Confirmed, dtype: float64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "latest_nCoV_data.groupby('Country')['Confirmed'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Country\n",
       "China          42310.0\n",
       "Others           135.0\n",
       "Singapore         45.0\n",
       "Hong Kong         38.0\n",
       "Thailand          32.0\n",
       "South Korea       27.0\n",
       "Japan             26.0\n",
       "Taiwan            18.0\n",
       "Malaysia          18.0\n",
       "Australia         15.0\n",
       "Name: Confirmed, dtype: float64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "latest_nCoV_data.groupby('Country')['Confirmed'].sum().sort_values(ascending=False)[0:10]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Country\n",
       "China                   1011.0\n",
       "Hong Kong                  1.0\n",
       "Philippines                1.0\n",
       "Vietnam                    0.0\n",
       "Malaysia                   0.0\n",
       "Belgium                    0.0\n",
       "Cambodia                   0.0\n",
       "Canada                     0.0\n",
       "Finland                    0.0\n",
       "France                     0.0\n",
       "Germany                    0.0\n",
       "India                      0.0\n",
       "Italy                      0.0\n",
       "Japan                      0.0\n",
       "Macau                      0.0\n",
       "Nepal                      0.0\n",
       "United Arab Emirates       0.0\n",
       "Others                     0.0\n",
       "Russia                     0.0\n",
       "Singapore                  0.0\n",
       "South Korea                0.0\n",
       "Spain                      0.0\n",
       "Sri Lanka                  0.0\n",
       "Sweden                     0.0\n",
       "Taiwan                     0.0\n",
       "Thailand                   0.0\n",
       "UK                         0.0\n",
       "US                         0.0\n",
       "Australia                  0.0\n",
       "Name: Deaths, dtype: float64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "latest_nCoV_data.groupby('Country')['Deaths'].sum().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2020-01-22 12:00:00')"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Initial Case time\n",
    "data.sort_values(by='Date')['Date'][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2020-02-10 19:30:00')"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#latest Case \n",
    "latest_nCoV_data['Date'].max()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Which Countries States were affected the most?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Province/State</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Anhui</th>\n",
       "      <td>830.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Beijing</th>\n",
       "      <td>337.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chongqing</th>\n",
       "      <td>486.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fujian</th>\n",
       "      <td>261.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gansu</th>\n",
       "      <td>86.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Guangdong</th>\n",
       "      <td>1159.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Guangxi</th>\n",
       "      <td>210.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Guizhou</th>\n",
       "      <td>109.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hainan</th>\n",
       "      <td>138.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hebei</th>\n",
       "      <td>218.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Heilongjiang</th>\n",
       "      <td>331.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Henan</th>\n",
       "      <td>1073.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hubei</th>\n",
       "      <td>31728.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hunan</th>\n",
       "      <td>879.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Inner Mongolia</th>\n",
       "      <td>58.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jiangsu</th>\n",
       "      <td>492.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jiangxi</th>\n",
       "      <td>771.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jilin</th>\n",
       "      <td>81.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Liaoning</th>\n",
       "      <td>108.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ningxia</th>\n",
       "      <td>49.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Qinghai</th>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shaanxi</th>\n",
       "      <td>213.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shandong</th>\n",
       "      <td>466.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shanghai</th>\n",
       "      <td>299.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shanxi</th>\n",
       "      <td>119.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sichuan</th>\n",
       "      <td>405.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tianjin</th>\n",
       "      <td>95.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tibet</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Xinjiang</th>\n",
       "      <td>49.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yunnan</th>\n",
       "      <td>149.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zhejiang</th>\n",
       "      <td>1092.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Confirmed\n",
       "Province/State           \n",
       "Anhui               830.0\n",
       "Beijing             337.0\n",
       "Chongqing           486.0\n",
       "Fujian              261.0\n",
       "Gansu                86.0\n",
       "Guangdong          1159.0\n",
       "Guangxi             210.0\n",
       "Guizhou             109.0\n",
       "Hainan              138.0\n",
       "Hebei               218.0\n",
       "Heilongjiang        331.0\n",
       "Henan              1073.0\n",
       "Hubei             31728.0\n",
       "Hunan               879.0\n",
       "Inner Mongolia       58.0\n",
       "Jiangsu             492.0\n",
       "Jiangxi             771.0\n",
       "Jilin                81.0\n",
       "Liaoning            108.0\n",
       "Ningxia              49.0\n",
       "Qinghai              18.0\n",
       "Shaanxi             213.0\n",
       "Shandong            466.0\n",
       "Shanghai            299.0\n",
       "Shanxi              119.0\n",
       "Sichuan             405.0\n",
       "Tianjin              95.0\n",
       "Tibet                 1.0\n",
       "Xinjiang             49.0\n",
       "Yunnan              149.0\n",
       "Zhejiang           1092.0"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "latest_nCoV_data[latest_nCoV_data.Country == 'China'][['Province/State', 'Confirmed']].groupby('Province/State').max()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Show the no.of Persons affected with Virus**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fab8c5a8860>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "latest_nCoV_data[['Confirmed', 'Deaths', 'Recovered']].max().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**How many entries are being recorded per Country?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,7))\n",
    "chart = sns.countplot(data=latest_nCoV_data, x='Country', palette='Set1')\n",
    "chart.set_xticklabels(chart.get_xticklabels(), rotation=45, horizontalalignment='right', fontweight='light');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Contribution from Following Kernels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://www.kaggle.com/ranjithks/ran-coronavirus-exploratory-data-analysis-eda\n",
    "<br>https://www.kaggle.com/parulpandey/wuhan-coronavirus-a-geographical-analysis</br>"
   ]
  }
 ],
 "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": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}