novel-corona-data-analysis.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/time_series_2019_ncov_recovered.csv\n",
      "/kaggle/input/novel-corona-virus-2019-dataset/2019_nCoV_data.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_deaths.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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      "text/plain": [
       "<IPython.lib.display.IFrame at 0x7f403ff4a748>"
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     },
     "execution_count": 3,
     "metadata": {},
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   ],
   "source": [
    "from IPython.display import IFrame\n",
    "IFrame('https://www.myheatmap.com/maps/PPk1_rfT1jQ%3D', width=800, height=600)"
   ]
  },
  {
   "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",
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       "    <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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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: 1127 entries, 0 to 1126\n",
      "Data columns (total 8 columns):\n",
      "Sno               1127 non-null int64\n",
      "Date              1127 non-null object\n",
      "Province/State    837 non-null object\n",
      "Country           1127 non-null object\n",
      "Last Update       1127 non-null object\n",
      "Confirmed         1127 non-null float64\n",
      "Deaths            1127 non-null float64\n",
      "Recovered         1127 non-null float64\n",
      "dtypes: float64(3), int64(1), object(4)\n",
      "memory usage: 70.6+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1127, 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",
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       "      <th>count</th>\n",
       "      <td>1127.000000</td>\n",
       "      <td>1127.000000</td>\n",
       "      <td>1127.000000</td>\n",
       "      <td>1127.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>564.000000</td>\n",
       "      <td>255.912156</td>\n",
       "      <td>5.443656</td>\n",
       "      <td>12.046140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>325.481182</td>\n",
       "      <td>1796.841832</td>\n",
       "      <td>52.486877</td>\n",
       "      <td>83.674768</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>282.500000</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>564.000000</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>845.500000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1127.000000</td>\n",
       "      <td>29631.000000</td>\n",
       "      <td>871.000000</td>\n",
       "      <td>1795.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Sno     Confirmed       Deaths    Recovered\n",
       "count  1127.000000   1127.000000  1127.000000  1127.000000\n",
       "mean    564.000000    255.912156     5.443656    12.046140\n",
       "std     325.481182   1796.841832    52.486877    83.674768\n",
       "min       1.000000      0.000000     0.000000     0.000000\n",
       "25%     282.500000      2.000000     0.000000     0.000000\n",
       "50%     564.000000     10.000000     0.000000     0.000000\n",
       "75%     845.500000     80.000000     0.000000     2.000000\n",
       "max    1127.000000  29631.000000   871.000000  1795.000000"
      ]
     },
     "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    290\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": [
    {
     "data": {
      "text/plain": [
       "Confirmed    288413.0\n",
       "Deaths         6135.0\n",
       "Recovered     13576.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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.drop('Last Update', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Date</th>\n",
       "      <th>Province/State</th>\n",
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       "      <th>1122</th>\n",
       "      <td>02/09/2020 23:20:00</td>\n",
       "      <td>Los Angeles, CA</td>\n",
       "      <td>US</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>1123</th>\n",
       "      <td>02/09/2020 23:20:00</td>\n",
       "      <td>Madison, WI</td>\n",
       "      <td>US</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1124</th>\n",
       "      <td>02/09/2020 23:20:00</td>\n",
       "      <td>Orange, CA</td>\n",
       "      <td>US</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1125</th>\n",
       "      <td>02/09/2020 23:20:00</td>\n",
       "      <td>Seattle, WA</td>\n",
       "      <td>US</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1126</th>\n",
       "      <td>02/09/2020 23:20:00</td>\n",
       "      <td>Tempe, AZ</td>\n",
       "      <td>US</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  Confirmed  Deaths  \\\n",
       "1122  02/09/2020 23:20:00  Los Angeles, CA      US        1.0     0.0   \n",
       "1123  02/09/2020 23:20:00      Madison, WI      US        1.0     0.0   \n",
       "1124  02/09/2020 23:20:00       Orange, CA      US        1.0     0.0   \n",
       "1125  02/09/2020 23:20:00      Seattle, WA      US        1.0     0.0   \n",
       "1126  02/09/2020 23:20:00        Tempe, AZ      US        1.0     0.0   \n",
       "\n",
       "      Recovered  \n",
       "1122        0.0  \n",
       "1123        0.0  \n",
       "1124        0.0  \n",
       "1125        1.0  \n",
       "1126        0.0  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Chongqing                       19\n",
       "Shandong                        19\n",
       "Guangxi                         19\n",
       "Shanghai                        19\n",
       "Beijing                         19\n",
       "Jiangxi                         19\n",
       "Jiangsu                         19\n",
       "Taiwan                          19\n",
       "Tianjin                         19\n",
       "Shanxi                          19\n",
       "Yunnan                          19\n",
       "Hong Kong                       19\n",
       "Guangdong                       19\n",
       "Fujian                          19\n",
       "Macau                           19\n",
       "Gansu                           19\n",
       "Heilongjiang                    19\n",
       "Sichuan                         19\n",
       "Anhui                           19\n",
       "Ningxia                         19\n",
       "Inner Mongolia                  19\n",
       "Liaoning                        19\n",
       "Jilin                           19\n",
       "Qinghai                         19\n",
       "Henan                           19\n",
       "Shaanxi                         19\n",
       "Xinjiang                        19\n",
       "Hunan                           19\n",
       "Zhejiang                        19\n",
       "Guizhou                         19\n",
       "Hainan                          19\n",
       "Hubei                           19\n",
       "Hebei                           19\n",
       "Tibet                           14\n",
       "New South Wales                 14\n",
       "Victoria                        14\n",
       "British Columbia                13\n",
       "Queensland                      12\n",
       "Washington                      10\n",
       "Tempe, AZ                        9\n",
       "Boston, MA                       9\n",
       "Orange, CA                       9\n",
       "Seattle, WA                      9\n",
       "Los Angeles, CA                  9\n",
       "South Australia                  9\n",
       "Santa Clara, CA                  9\n",
       "Chicago, IL                      9\n",
       "Ontario                          8\n",
       "London, ON                       7\n",
       "San Benito, CA                   7\n",
       "Illinois                         7\n",
       "Toronto, ON                      7\n",
       "California                       6\n",
       "Arizona                          6\n",
       "Bavaria                          5\n",
       "Madison, WI                      4\n",
       "Cruise Ship                      2\n",
       "Chicago                          1\n",
       "Diamond Princess cruise ship     1\n",
       "Name: Province/State, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Province/State'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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": 17,
   "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": "code",
   "execution_count": 18,
   "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": 19,
   "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": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       01/22/2020 12:00:00\n",
       "1       01/22/2020 12:00:00\n",
       "2       01/22/2020 12:00:00\n",
       "3       01/22/2020 12:00:00\n",
       "4       01/22/2020 12:00:00\n",
       "               ...         \n",
       "1122    02/09/2020 23:20:00\n",
       "1123    02/09/2020 23:20:00\n",
       "1124    02/09/2020 23:20:00\n",
       "1125    02/09/2020 23:20:00\n",
       "1126    02/09/2020 23:20:00\n",
       "Name: Date, Length: 1127, dtype: object"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Date']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Date'] = data['Date'].apply(try_parsing_date_time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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",
       "1122   2020-02-09 23:20:00\n",
       "1123   2020-02-09 23:20:00\n",
       "1124   2020-02-09 23:20:00\n",
       "1125   2020-02-09 23:20:00\n",
       "1126   2020-02-09 23:20:00\n",
       "Name: Date, Length: 1127, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Date']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Confirmed</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Australia</th>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belgium</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brazil</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cambodia</th>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Canada</th>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>China</th>\n",
       "      <td>587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Finland</th>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hong Kong</th>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>India</th>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Italy</th>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ivory Coast</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Japan</th>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Macau</th>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Malaysia</th>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mexico</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nepal</th>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Others</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Philippines</th>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Russia</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Singapore</th>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South Korea</th>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spain</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sri Lanka</th>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sweden</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Taiwan</th>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thailand</th>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UK</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>US</th>\n",
       "      <td>104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Arab Emirates</th>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vietnam</th>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Confirmed\n",
       "Country                        \n",
       "Australia                    52\n",
       "Belgium                       6\n",
       "Brazil                        1\n",
       "Cambodia                     14\n",
       "Canada                       35\n",
       "China                       587\n",
       "Finland                      12\n",
       "France                       17\n",
       "Germany                      14\n",
       "Hong Kong                    18\n",
       "India                        11\n",
       "Italy                        11\n",
       "Ivory Coast                   1\n",
       "Japan                        19\n",
       "Macau                        18\n",
       "Malaysia                     17\n",
       "Mexico                        1\n",
       "Nepal                        16\n",
       "Others                        3\n",
       "Philippines                  12\n",
       "Russia                       10\n",
       "Singapore                    18\n",
       "South Korea                  19\n",
       "Spain                        10\n",
       "Sri Lanka                    14\n",
       "Sweden                       10\n",
       "Taiwan                       18\n",
       "Thailand                     19\n",
       "UK                           10\n",
       "US                          104\n",
       "United Arab Emirates         12\n",
       "Vietnam                      18"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[['Country', 'Confirmed']].groupby(['Country']).count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>Confirmed</th>\n",
       "      <th>Deaths</th>\n",
       "      <th>Recovered</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>432</th>\n",
       "      <td>2020-01-30 21:30:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>India</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>491</th>\n",
       "      <td>2020-01-31 19:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>India</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>552</th>\n",
       "      <td>2020-02-01 23:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>India</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>611</th>\n",
       "      <td>2020-02-02 21:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>India</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>675</th>\n",
       "      <td>2020-02-03 21:40:00</td>\n",
       "      <td>NaN</td>\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>745</th>\n",
       "      <td>2020-02-04 22:00:00</td>\n",
       "      <td>NaN</td>\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>815</th>\n",
       "      <td>2020-02-05 12:20:00</td>\n",
       "      <td>NaN</td>\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>885</th>\n",
       "      <td>2020-02-06 20:05:00</td>\n",
       "      <td>NaN</td>\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>958</th>\n",
       "      <td>2020-02-07 20:24:00</td>\n",
       "      <td>NaN</td>\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>1030</th>\n",
       "      <td>2020-02-08 23:04:00</td>\n",
       "      <td>NaN</td>\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>1102</th>\n",
       "      <td>2020-02-09 23:20:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>India</td>\n",
       "      <td>3.0</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  Confirmed  Deaths  Recovered\n",
       "432  2020-01-30 21:30:00            NaN   India        1.0     0.0        0.0\n",
       "491  2020-01-31 19:00:00            NaN   India        1.0     0.0        0.0\n",
       "552  2020-02-01 23:00:00            NaN   India        1.0     0.0        0.0\n",
       "611  2020-02-02 21:00:00            NaN   India        2.0     0.0        0.0\n",
       "675  2020-02-03 21:40:00            NaN   India        3.0     0.0        0.0\n",
       "745  2020-02-04 22:00:00            NaN   India        3.0     0.0        0.0\n",
       "815  2020-02-05 12:20:00            NaN   India        3.0     0.0        0.0\n",
       "885  2020-02-06 20:05:00            NaN   India        3.0     0.0        0.0\n",
       "958  2020-02-07 20:24:00            NaN   India        3.0     0.0        0.0\n",
       "1030 2020-02-08 23:04:00            NaN   India        3.0     0.0        0.0\n",
       "1102 2020-02-09 23:20:00            NaN   India        3.0     0.0        0.0"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data.Country == 'India']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Implementing EDA on the data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "China                   587\n",
       "US                      104\n",
       "Australia                52\n",
       "Canada                   35\n",
       "Japan                    19\n",
       "Thailand                 19\n",
       "South Korea              19\n",
       "Hong Kong                18\n",
       "Singapore                18\n",
       "Macau                    18\n",
       "Taiwan                   18\n",
       "Vietnam                  18\n",
       "Malaysia                 17\n",
       "France                   17\n",
       "Nepal                    16\n",
       "Sri Lanka                14\n",
       "Germany                  14\n",
       "Cambodia                 14\n",
       "Philippines              12\n",
       "Finland                  12\n",
       "United Arab Emirates     12\n",
       "India                    11\n",
       "Italy                    11\n",
       "UK                       10\n",
       "Sweden                   10\n",
       "Russia                   10\n",
       "Spain                    10\n",
       "Belgium                   6\n",
       "Others                    3\n",
       "Ivory Coast               1\n",
       "Mexico                    1\n",
       "Brazil                    1\n",
       "Name: Country, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Country'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>**No.of Countries that were affected with Virus?**</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "32"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.Country.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x1296 with 4 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": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 698.225x540 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(data.iloc[:,0:8],hue=\"Country\")\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": 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>Date</th>\n",
       "      <th>Province/State</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>564</th>\n",
       "      <td>2020-02-02 21:00:00</td>\n",
       "      <td>Hubei</td>\n",
       "      <td>China</td>\n",
       "      <td>11177.0</td>\n",
       "      <td>350.0</td>\n",
       "      <td>295.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>565</th>\n",
       "      <td>2020-02-02 21:00:00</td>\n",
       "      <td>Zhejiang</td>\n",
       "      <td>China</td>\n",
       "      <td>724.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>36.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>566</th>\n",
       "      <td>2020-02-02 21:00:00</td>\n",
       "      <td>Guangdong</td>\n",
       "      <td>China</td>\n",
       "      <td>683.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>567</th>\n",
       "      <td>2020-02-02 21:00:00</td>\n",
       "      <td>Henan</td>\n",
       "      <td>China</td>\n",
       "      <td>566.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>568</th>\n",
       "      <td>2020-02-02 21:00:00</td>\n",
       "      <td>Hunan</td>\n",
       "      <td>China</td>\n",
       "      <td>521.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Date Province/State Country  Confirmed  Deaths  Recovered\n",
       "564 2020-02-02 21:00:00          Hubei   China    11177.0   350.0      295.0\n",
       "565 2020-02-02 21:00:00       Zhejiang   China      724.0     0.0       36.0\n",
       "566 2020-02-02 21:00:00      Guangdong   China      683.0     0.0       15.0\n",
       "567 2020-02-02 21:00:00          Henan   China      566.0     2.0       14.0\n",
       "568 2020-02-02 21:00:00          Hunan   China      521.0     0.0       16.0"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "from datetime import date\n",
    "data_2_feb = data[data['Date'] > pd.Timestamp(date(2020,2,2))]\n",
    "data_2_feb.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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": 31,
   "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": 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>Province/State</th>\n",
       "      <th>Country</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>0</th>\n",
       "      <td>2020-01-22 12:00:00</td>\n",
       "      <td>Anhui</td>\n",
       "      <td>China</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>CHN</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>14.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>CHN</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>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>CHN</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>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>CHN</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>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1122</th>\n",
       "      <td>2020-02-09 23:20:00</td>\n",
       "      <td>Los Angeles, CA</td>\n",
       "      <td>US</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>1123</th>\n",
       "      <td>2020-02-09 23:20:00</td>\n",
       "      <td>Madison, WI</td>\n",
       "      <td>US</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>1124</th>\n",
       "      <td>2020-02-09 23:20:00</td>\n",
       "      <td>Orange, CA</td>\n",
       "      <td>US</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>1125</th>\n",
       "      <td>2020-02-09 23:20:00</td>\n",
       "      <td>Seattle, WA</td>\n",
       "      <td>US</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>1126</th>\n",
       "      <td>2020-02-09 23:20:00</td>\n",
       "      <td>Tempe, AZ</td>\n",
       "      <td>US</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>1127 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Date   Province/State Country  Confirmed  Deaths  \\\n",
       "0    2020-01-22 12:00:00            Anhui   China        1.0     0.0   \n",
       "1    2020-01-22 12:00:00          Beijing   China       14.0     0.0   \n",
       "2    2020-01-22 12:00:00        Chongqing   China        6.0     0.0   \n",
       "3    2020-01-22 12:00:00           Fujian   China        1.0     0.0   \n",
       "4    2020-01-22 12:00:00            Gansu   China        0.0     0.0   \n",
       "...                  ...              ...     ...        ...     ...   \n",
       "1122 2020-02-09 23:20:00  Los Angeles, CA      US        1.0     0.0   \n",
       "1123 2020-02-09 23:20:00      Madison, WI      US        1.0     0.0   \n",
       "1124 2020-02-09 23:20:00       Orange, CA      US        1.0     0.0   \n",
       "1125 2020-02-09 23:20:00      Seattle, WA      US        1.0     0.0   \n",
       "1126 2020-02-09 23:20:00        Tempe, AZ      US        1.0     0.0   \n",
       "\n",
       "      Recovered iso_alpha  \n",
       "0           0.0       CHN  \n",
       "1           0.0       CHN  \n",
       "2           0.0       CHN  \n",
       "3           0.0       CHN  \n",
       "4           0.0       CHN  \n",
       "...         ...       ...  \n",
       "1122        0.0        US  \n",
       "1123        0.0        US  \n",
       "1124        0.0        US  \n",
       "1125        1.0        US  \n",
       "1126        0.0        US  \n",
       "\n",
       "[1127 rows x 7 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['iso_alpha'] = data['Country'].apply(getCountryIsoAlpha)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
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   ],
   "source": [
    "data_plot = 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",
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   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Country\n",
       "Australia                  164.0\n",
       "Belgium                      6.0\n",
       "Brazil                       0.0\n",
       "Cambodia                    14.0\n",
       "Canada                      59.0\n",
       "China                   285454.0\n",
       "Finland                     12.0\n",
       "France                      95.0\n",
       "Germany                    126.0\n",
       "Hong Kong                  262.0\n",
       "India                       26.0\n",
       "Italy                       25.0\n",
       "Ivory Coast                  0.0\n",
       "Japan                      286.0\n",
       "Macau                      129.0\n",
       "Malaysia                   145.0\n",
       "Mexico                       0.0\n",
       "Nepal                       16.0\n",
       "Others                     186.0\n",
       "Philippines                 23.0\n",
       "Russia                      20.0\n",
       "Singapore                  306.0\n",
       "South Korea                218.0\n",
       "Spain                       11.0\n",
       "Sri Lanka                   14.0\n",
       "Sweden                      10.0\n",
       "Taiwan                     170.0\n",
       "Thailand                   315.0\n",
       "UK                          23.0\n",
       "US                         137.0\n",
       "United Arab Emirates        60.0\n",
       "Vietnam                    101.0\n",
       "Name: Confirmed, dtype: float64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby('Country')['Confirmed'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Country\n",
       "China          285454.0\n",
       "Thailand          315.0\n",
       "Singapore         306.0\n",
       "Japan             286.0\n",
       "Hong Kong         262.0\n",
       "South Korea       218.0\n",
       "Others            186.0\n",
       "Taiwan            170.0\n",
       "Australia         164.0\n",
       "Malaysia          145.0\n",
       "Name: Confirmed, dtype: float64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby('Country')['Confirmed'].sum().sort_values(ascending=False)[0:10]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Country\n",
       "China                   6120.0\n",
       "Philippines                9.0\n",
       "Hong Kong                  6.0\n",
       "Vietnam                    0.0\n",
       "Macau                      0.0\n",
       "Belgium                    0.0\n",
       "Brazil                     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",
       "Ivory Coast                0.0\n",
       "Japan                      0.0\n",
       "Malaysia                   0.0\n",
       "United Arab Emirates       0.0\n",
       "Mexico                     0.0\n",
       "Nepal                      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": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby('Country')['Deaths'].sum().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2020-01-22 12:00:00')"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Initial Case time\n",
    "data.sort_values(by='Date')['Date'][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2020-02-09 23:20:00')"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#latest Case \n",
    "data['Date'].max()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Which Countries States were affected the most?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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>468.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fujian</th>\n",
       "      <td>261.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gansu</th>\n",
       "      <td>83.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Guangdong</th>\n",
       "      <td>1151.0</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>136.0</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>Hong Kong</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hubei</th>\n",
       "      <td>29631.0</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>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Liaoning</th>\n",
       "      <td>107.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Macau</th>\n",
       "      <td>1.0</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>459.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shanghai</th>\n",
       "      <td>295.0</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>Taiwan</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tianjin</th>\n",
       "      <td>94.0</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>141.0</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           468.0\n",
       "Fujian              261.0\n",
       "Gansu                83.0\n",
       "Guangdong          1151.0\n",
       "Guangxi             210.0\n",
       "Guizhou             109.0\n",
       "Hainan              136.0\n",
       "Hebei               218.0\n",
       "Heilongjiang        331.0\n",
       "Henan              1073.0\n",
       "Hong Kong             0.0\n",
       "Hubei             29631.0\n",
       "Hunan               879.0\n",
       "Inner Mongolia       58.0\n",
       "Jiangsu             492.0\n",
       "Jiangxi             771.0\n",
       "Jilin                80.0\n",
       "Liaoning            107.0\n",
       "Macau                 1.0\n",
       "Ningxia              49.0\n",
       "Qinghai              18.0\n",
       "Shaanxi             213.0\n",
       "Shandong            459.0\n",
       "Shanghai            295.0\n",
       "Shanxi              119.0\n",
       "Sichuan             405.0\n",
       "Taiwan                1.0\n",
       "Tianjin              94.0\n",
       "Tibet                 1.0\n",
       "Xinjiang             49.0\n",
       "Yunnan              141.0\n",
       "Zhejiang           1092.0"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[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": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f4018ea8208>"
      ]
     },
     "execution_count": 40,
     "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": [
    "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": 41,
   "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=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
}