Lockdown_Only_solution_to_contain_coronavirus.ipynb

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    "colab": {
      "name": "Lockdown Only solution to contain coronavirus.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyPin6rlWPsUitUeFoMPp/Rw",
      "include_colab_link": true
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/srivatsan88/YouTubeLI/blob/master/Lockdown_Only_solution_to_contain_coronavirus.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A4fveO-fbYb8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "\n",
        "import matplotlib as mpl\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "mpl.rcParams['figure.figsize'] = (14, 5)\n",
        "mpl.rcParams['axes.grid'] = False\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cDLW8AUKxdo9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "corona_confirmed_df = pd.read_csv('https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_19-covid-Confirmed.csv')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qEL1RFFhx7fj",
        "colab_type": "code",
        "outputId": "12807601-e4b8-4aa2-d480-8845e51a3a17",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        }
      },
      "source": [
        "corona_confirmed_df.head()"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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            ],
            "text/plain": [
              "  Province/State Country/Region      Lat  ...  3/15/20  3/16/20  3/17/20\n",
              "0            NaN       Thailand  15.0000  ...      114      147      177\n",
              "1            NaN          Japan  36.0000  ...      839      825      878\n",
              "2            NaN      Singapore   1.2833  ...      226      243      266\n",
              "3            NaN          Nepal  28.1667  ...        1        1        1\n",
              "4            NaN       Malaysia   2.5000  ...      428      566      673\n",
              "\n",
              "[5 rows x 60 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8QlDkDrhvyrS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "corona_death_df = pd.read_csv('https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_19-covid-Deaths.csv')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UTfIVXgRwBzJ",
        "colab_type": "code",
        "outputId": "d6f56377-6db5-4d8b-b747-720135415ecd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        }
      },
      "source": [
        "corona_death_df.head()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>2/27/20</th>\n",
              "      <th>2/28/20</th>\n",
              "      <th>2/29/20</th>\n",
              "      <th>3/1/20</th>\n",
              "      <th>3/2/20</th>\n",
              "      <th>3/3/20</th>\n",
              "      <th>3/4/20</th>\n",
              "      <th>3/5/20</th>\n",
              "      <th>3/6/20</th>\n",
              "      <th>3/7/20</th>\n",
              "      <th>3/8/20</th>\n",
              "      <th>3/9/20</th>\n",
              "      <th>3/10/20</th>\n",
              "      <th>3/11/20</th>\n",
              "      <th>3/12/20</th>\n",
              "      <th>3/13/20</th>\n",
              "      <th>3/14/20</th>\n",
              "      <th>3/15/20</th>\n",
              "      <th>3/16/20</th>\n",
              "      <th>3/17/20</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
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              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>NaN</td>\n",
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              "      <td>138.0000</td>\n",
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              "      <td>6</td>\n",
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              "      <td>6</td>\n",
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              "      <td>10</td>\n",
              "      <td>15</td>\n",
              "      <td>16</td>\n",
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              "      <td>22</td>\n",
              "      <td>27</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Singapore</td>\n",
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              "      <td>103.8333</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Nepal</td>\n",
              "      <td>28.1667</td>\n",
              "      <td>84.2500</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Malaysia</td>\n",
              "      <td>2.5000</td>\n",
              "      <td>112.5000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Province/State Country/Region      Lat  ...  3/15/20  3/16/20  3/17/20\n",
              "0            NaN       Thailand  15.0000  ...        1        1        1\n",
              "1            NaN          Japan  36.0000  ...       22       27       29\n",
              "2            NaN      Singapore   1.2833  ...        0        0        0\n",
              "3            NaN          Nepal  28.1667  ...        0        0        0\n",
              "4            NaN       Malaysia   2.5000  ...        0        0        2\n",
              "\n",
              "[5 rows x 60 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Zp_2vjdA70lw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "corona_recovered_df = pd.read_csv('https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_19-covid-Recovered.csv')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VWw1MPkA77Ls",
        "colab_type": "code",
        "outputId": "fd255ede-5828-4220-c05c-30ce11dfef29",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        }
      },
      "source": [
        "corona_recovered_df.head()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <td>22</td>\n",
              "      <td>22</td>\n",
              "      <td>22</td>\n",
              "      <td>22</td>\n",
              "      <td>22</td>\n",
              "      <td>22</td>\n",
              "      <td>22</td>\n",
              "      <td>22</td>\n",
              "      <td>32</td>\n",
              "      <td>32</td>\n",
              "      <td>32</td>\n",
              "      <td>43</td>\n",
              "      <td>43</td>\n",
              "      <td>43</td>\n",
              "      <td>46</td>\n",
              "      <td>76</td>\n",
              "      <td>76</td>\n",
              "      <td>76</td>\n",
              "      <td>101</td>\n",
              "      <td>118</td>\n",
              "      <td>118</td>\n",
              "      <td>118</td>\n",
              "      <td>118</td>\n",
              "      <td>118</td>\n",
              "      <td>144</td>\n",
              "      <td>144</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Singapore</td>\n",
              "      <td>1.2833</td>\n",
              "      <td>103.8333</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>9</td>\n",
              "      <td>15</td>\n",
              "      <td>15</td>\n",
              "      <td>17</td>\n",
              "      <td>18</td>\n",
              "      <td>18</td>\n",
              "      <td>24</td>\n",
              "      <td>29</td>\n",
              "      <td>34</td>\n",
              "      <td>34</td>\n",
              "      <td>37</td>\n",
              "      <td>37</td>\n",
              "      <td>51</td>\n",
              "      <td>51</td>\n",
              "      <td>53</td>\n",
              "      <td>62</td>\n",
              "      <td>62</td>\n",
              "      <td>62</td>\n",
              "      <td>72</td>\n",
              "      <td>72</td>\n",
              "      <td>78</td>\n",
              "      <td>78</td>\n",
              "      <td>78</td>\n",
              "      <td>78</td>\n",
              "      <td>78</td>\n",
              "      <td>78</td>\n",
              "      <td>78</td>\n",
              "      <td>78</td>\n",
              "      <td>78</td>\n",
              "      <td>96</td>\n",
              "      <td>96</td>\n",
              "      <td>97</td>\n",
              "      <td>105</td>\n",
              "      <td>105</td>\n",
              "      <td>109</td>\n",
              "      <td>114</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Nepal</td>\n",
              "      <td>28.1667</td>\n",
              "      <td>84.2500</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Malaysia</td>\n",
              "      <td>2.5000</td>\n",
              "      <td>112.5000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>7</td>\n",
              "      <td>7</td>\n",
              "      <td>7</td>\n",
              "      <td>13</td>\n",
              "      <td>15</td>\n",
              "      <td>15</td>\n",
              "      <td>15</td>\n",
              "      <td>15</td>\n",
              "      <td>15</td>\n",
              "      <td>18</td>\n",
              "      <td>18</td>\n",
              "      <td>18</td>\n",
              "      <td>18</td>\n",
              "      <td>18</td>\n",
              "      <td>18</td>\n",
              "      <td>18</td>\n",
              "      <td>18</td>\n",
              "      <td>22</td>\n",
              "      <td>22</td>\n",
              "      <td>22</td>\n",
              "      <td>22</td>\n",
              "      <td>23</td>\n",
              "      <td>24</td>\n",
              "      <td>24</td>\n",
              "      <td>24</td>\n",
              "      <td>26</td>\n",
              "      <td>26</td>\n",
              "      <td>26</td>\n",
              "      <td>35</td>\n",
              "      <td>42</td>\n",
              "      <td>42</td>\n",
              "      <td>49</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Province/State Country/Region      Lat  ...  3/15/20  3/16/20  3/17/20\n",
              "0            NaN       Thailand  15.0000  ...       35       35       41\n",
              "1            NaN          Japan  36.0000  ...      118      144      144\n",
              "2            NaN      Singapore   1.2833  ...      105      109      114\n",
              "3            NaN          Nepal  28.1667  ...        1        1        1\n",
              "4            NaN       Malaysia   2.5000  ...       42       42       49\n",
              "\n",
              "[5 rows x 60 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4RdVXNp7wD2p",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "corona_confirmed_df=corona_confirmed_df.melt(id_vars=['Province/State','Country/Region','Lat','Long'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E_PVdt7x6HQu",
        "colab_type": "code",
        "outputId": "712c3f0f-b453-4051-88c9-fb348bd36be5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "corona_confirmed_df.head()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <td>112.5000</td>\n",
              "      <td>1/22/20</td>\n",
              "      <td>0</td>\n",
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            "text/plain": [
              "  Province/State Country/Region      Lat      Long variable  value\n",
              "0            NaN       Thailand  15.0000  101.0000  1/22/20      2\n",
              "1            NaN          Japan  36.0000  138.0000  1/22/20      2\n",
              "2            NaN      Singapore   1.2833  103.8333  1/22/20      0\n",
              "3            NaN          Nepal  28.1667   84.2500  1/22/20      0\n",
              "4            NaN       Malaysia   2.5000  112.5000  1/22/20      0"
            ]
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          "metadata": {
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          "execution_count": 9
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      "metadata": {
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      },
      "source": [
        "corona_confirmed_df=corona_confirmed_df.rename({'variable':'Date','value': 'Confirmed'}, axis='columns')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
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      "source": [
        "corona_confirmed_df.head()"
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      "execution_count": 11,
      "outputs": [
        {
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          "data": {
            "text/html": [
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            "text/plain": [
              "  Province/State Country/Region      Lat      Long     Date  Confirmed\n",
              "0            NaN       Thailand  15.0000  101.0000  1/22/20          2\n",
              "1            NaN          Japan  36.0000  138.0000  1/22/20          2\n",
              "2            NaN      Singapore   1.2833  103.8333  1/22/20          0\n",
              "3            NaN          Nepal  28.1667   84.2500  1/22/20          0\n",
              "4            NaN       Malaysia   2.5000  112.5000  1/22/20          0"
            ]
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      "metadata": {
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        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "corona_death_df=corona_death_df.melt(id_vars=['Province/State','Country/Region','Lat','Long'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zyAf83_zCza2",
        "colab_type": "code",
        "colab": {}
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      "source": [
        "corona_death_df=corona_death_df.rename({'variable':'Date','value': 'Death'}, axis='columns')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O5x1c_Y1C5bS",
        "colab_type": "code",
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      "source": [
        "corona_death_df.head()"
      ],
      "execution_count": 14,
      "outputs": [
        {
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          "data": {
            "text/html": [
              "<div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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            "text/plain": [
              "  Province/State Country/Region      Lat      Long     Date  Death\n",
              "0            NaN       Thailand  15.0000  101.0000  1/22/20      0\n",
              "1            NaN          Japan  36.0000  138.0000  1/22/20      0\n",
              "2            NaN      Singapore   1.2833  103.8333  1/22/20      0\n",
              "3            NaN          Nepal  28.1667   84.2500  1/22/20      0\n",
              "4            NaN       Malaysia   2.5000  112.5000  1/22/20      0"
            ]
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      "source": [
        "corona_recovered_df=corona_recovered_df.melt(id_vars=['Province/State','Country/Region','Lat','Long']).rename({'variable':'Date','value': 'Recovered'}, axis='columns')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
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      "source": [
        "corona_recovered_df.head()"
      ],
      "execution_count": 16,
      "outputs": [
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              "  Province/State Country/Region      Lat      Long     Date  Recovered\n",
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      "source": [
        "combined_df = [corona_confirmed_df, corona_death_df, corona_recovered_df]\n",
        "combined_df = [df.set_index(['Province/State','Country/Region','Lat','Long','Date']) for df in combined_df]\n",
        "combined_df=combined_df[0].join(combined_df[1:])"
      ],
      "execution_count": 0,
      "outputs": []
    },
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      "execution_count": 18,
      "outputs": [
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              "                                                        Confirmed  ...  Recovered\n",
              "Province/State Country/Region Lat     Long     Date                ...           \n",
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              "               Nepal          28.1667 84.2500  1/22/20          0  ...          0\n",
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              "\n",
              "[5 rows x 3 columns]"
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      ],
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      "source": [
        "combined_df[['Lat', 'Long','Confirmed','Death','Recovered']] = combined_df[['Lat', 'Long','Confirmed','Death','Recovered']].apply(pd.to_numeric) "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3vxX9-SOF93f",
        "colab_type": "code",
        "colab": {}
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      "source": [
        "combined_df[['Date']] = combined_df[['Date']].apply(pd.to_datetime)"
      ],
      "execution_count": 0,
      "outputs": []
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        "combined_df.dtypes"
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      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "Province/State            object\n",
              "Country/Region            object\n",
              "Lat                      float64\n",
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      "outputs": [
        {
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              "\n",
              "[5 rows x 8 columns]"
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        "combined_df=combined_df.rename({'Province/State':'State','Country/Region': 'Country'}, axis='columns')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
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      "outputs": [
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              "  State    Country      Lat      Long       Date  Confirmed  Death  Recovered\n",
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              "3   NaN      Nepal  28.1667   84.2500 2020-01-22          0      0          0\n",
              "4   NaN   Malaysia   2.5000  112.5000 2020-01-22          0      0          0"
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          "execution_count": 26
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      "execution_count": 27,
      "outputs": [
        {
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              "  State    Country      Lat      Long       Date  Confirmed  Death  Recovered\n",
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              "4   NaN   Malaysia   2.5000  112.5000 2020-01-22          0      0          0"
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      "source": [
        "combined_df.query(\"Country=='US' & State=='Washington'\")"
      ],
      "execution_count": 28,
      "outputs": [
        {
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              "      <td>0</td>\n",
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              "    <tr>\n",
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              "    <tr>\n",
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              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15278</th>\n",
              "      <td>Washington</td>\n",
              "      <td>US</td>\n",
              "      <td>47.4009</td>\n",
              "      <td>-121.4905</td>\n",
              "      <td>2020-02-24</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15738</th>\n",
              "      <td>Washington</td>\n",
              "      <td>US</td>\n",
              "      <td>47.4009</td>\n",
              "      <td>-121.4905</td>\n",
              "      <td>2020-02-25</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16198</th>\n",
              "      <td>Washington</td>\n",
              "      <td>US</td>\n",
              "      <td>47.4009</td>\n",
              "      <td>-121.4905</td>\n",
              "      <td>2020-02-26</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16658</th>\n",
              "      <td>Washington</td>\n",
              "      <td>US</td>\n",
              "      <td>47.4009</td>\n",
              "      <td>-121.4905</td>\n",
              "      <td>2020-02-27</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17118</th>\n",
              "      <td>Washington</td>\n",
              "      <td>US</td>\n",
              "      <td>47.4009</td>\n",
              "      <td>-121.4905</td>\n",
              "      <td>2020-02-28</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17578</th>\n",
              "      <td>Washington</td>\n",
              "      <td>US</td>\n",
              "      <td>47.4009</td>\n",
              "      <td>-121.4905</td>\n",
              "      <td>2020-02-29</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18038</th>\n",
              "      <td>Washington</td>\n",
              "      <td>US</td>\n",
              "      <td>47.4009</td>\n",
              "      <td>-121.4905</td>\n",
              "      <td>2020-03-01</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18498</th>\n",
              "      <td>Washington</td>\n",
              "      <td>US</td>\n",
              "      <td>47.4009</td>\n",
              "      <td>-121.4905</td>\n",
              "      <td>2020-03-02</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18958</th>\n",
              "      <td>Washington</td>\n",
              "      <td>US</td>\n",
              "      <td>47.4009</td>\n",
              "      <td>-121.4905</td>\n",
              "      <td>2020-03-03</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19418</th>\n",
              "      <td>Washington</td>\n",
              "      <td>US</td>\n",
              "      <td>47.4009</td>\n",
              "      <td>-121.4905</td>\n",
              "      <td>2020-03-04</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19878</th>\n",
              "      <td>Washington</td>\n",
              "      <td>US</td>\n",
              "      <td>47.4009</td>\n",
              "      <td>-121.4905</td>\n",
              "      <td>2020-03-05</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20338</th>\n",
              "      <td>Washington</td>\n",
              "      <td>US</td>\n",
              "      <td>47.4009</td>\n",
              "      <td>-121.4905</td>\n",
              "      <td>2020-03-06</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20798</th>\n",
              "      <td>Washington</td>\n",
              "      <td>US</td>\n",
              "      <td>47.4009</td>\n",
              "      <td>-121.4905</td>\n",
              "      <td>2020-03-07</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21258</th>\n",
              "      <td>Washington</td>\n",
              "      <td>US</td>\n",
              "      <td>47.4009</td>\n",
              "      <td>-121.4905</td>\n",
              "      <td>2020-03-08</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21718</th>\n",
              "      <td>Washington</td>\n",
              "      <td>US</td>\n",
              "      <td>47.4009</td>\n",
              "      <td>-121.4905</td>\n",
              "      <td>2020-03-09</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22178</th>\n",
              "      <td>Washington</td>\n",
              "      <td>US</td>\n",
              "      <td>47.4009</td>\n",
              "      <td>-121.4905</td>\n",
              "      <td>2020-03-10</td>\n",
              "      <td>267</td>\n",
              "      <td>23</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22638</th>\n",
              "      <td>Washington</td>\n",
              "      <td>US</td>\n",
              "      <td>47.4009</td>\n",
              "      <td>-121.4905</td>\n",
              "      <td>2020-03-11</td>\n",
              "      <td>366</td>\n",
              "      <td>29</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23098</th>\n",
              "      <td>Washington</td>\n",
              "      <td>US</td>\n",
              "      <td>47.4009</td>\n",
              "      <td>-121.4905</td>\n",
              "      <td>2020-03-12</td>\n",
              "      <td>442</td>\n",
              "      <td>31</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23558</th>\n",
              "      <td>Washington</td>\n",
              "      <td>US</td>\n",
              "      <td>47.4009</td>\n",
              "      <td>-121.4905</td>\n",
              "      <td>2020-03-13</td>\n",
              "      <td>568</td>\n",
              "      <td>37</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24018</th>\n",
              "      <td>Washington</td>\n",
              "      <td>US</td>\n",
              "      <td>47.4009</td>\n",
              "      <td>-121.4905</td>\n",
              "      <td>2020-03-14</td>\n",
              "      <td>572</td>\n",
              "      <td>37</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24478</th>\n",
              "      <td>Washington</td>\n",
              "      <td>US</td>\n",
              "      <td>47.4009</td>\n",
              "      <td>-121.4905</td>\n",
              "      <td>2020-03-15</td>\n",
              "      <td>643</td>\n",
              "      <td>40</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24938</th>\n",
              "      <td>Washington</td>\n",
              "      <td>US</td>\n",
              "      <td>47.4009</td>\n",
              "      <td>-121.4905</td>\n",
              "      <td>2020-03-16</td>\n",
              "      <td>904</td>\n",
              "      <td>48</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25398</th>\n",
              "      <td>Washington</td>\n",
              "      <td>US</td>\n",
              "      <td>47.4009</td>\n",
              "      <td>-121.4905</td>\n",
              "      <td>2020-03-17</td>\n",
              "      <td>1076</td>\n",
              "      <td>55</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            State Country      Lat  ...  Confirmed Death  Recovered\n",
              "98     Washington      US  47.4009  ...          0     0          0\n",
              "558    Washington      US  47.4009  ...          0     0          0\n",
              "1018   Washington      US  47.4009  ...          0     0          0\n",
              "1478   Washington      US  47.4009  ...          0     0          0\n",
              "1938   Washington      US  47.4009  ...          0     0          0\n",
              "2398   Washington      US  47.4009  ...          0     0          0\n",
              "2858   Washington      US  47.4009  ...          0     0          0\n",
              "3318   Washington      US  47.4009  ...          0     0          0\n",
              "3778   Washington      US  47.4009  ...          0     0          0\n",
              "4238   Washington      US  47.4009  ...          0     0          0\n",
              "4698   Washington      US  47.4009  ...          0     0          0\n",
              "5158   Washington      US  47.4009  ...          0     0          0\n",
              "5618   Washington      US  47.4009  ...          0     0          0\n",
              "6078   Washington      US  47.4009  ...          0     0          0\n",
              "6538   Washington      US  47.4009  ...          0     0          0\n",
              "6998   Washington      US  47.4009  ...          0     0          0\n",
              "7458   Washington      US  47.4009  ...          0     0          0\n",
              "7918   Washington      US  47.4009  ...          0     0          0\n",
              "8378   Washington      US  47.4009  ...          0     0          0\n",
              "8838   Washington      US  47.4009  ...          0     0          0\n",
              "9298   Washington      US  47.4009  ...          0     0          0\n",
              "9758   Washington      US  47.4009  ...          0     0          0\n",
              "10218  Washington      US  47.4009  ...          0     0          0\n",
              "10678  Washington      US  47.4009  ...          0     0          0\n",
              "11138  Washington      US  47.4009  ...          0     0          0\n",
              "11598  Washington      US  47.4009  ...          0     0          0\n",
              "12058  Washington      US  47.4009  ...          0     0          0\n",
              "12518  Washington      US  47.4009  ...          0     0          0\n",
              "12978  Washington      US  47.4009  ...          0     0          0\n",
              "13438  Washington      US  47.4009  ...          0     0          0\n",
              "13898  Washington      US  47.4009  ...          0     0          0\n",
              "14358  Washington      US  47.4009  ...          0     0          0\n",
              "14818  Washington      US  47.4009  ...          0     0          0\n",
              "15278  Washington      US  47.4009  ...          0     0          0\n",
              "15738  Washington      US  47.4009  ...          0     0          0\n",
              "16198  Washington      US  47.4009  ...          0     0          0\n",
              "16658  Washington      US  47.4009  ...          0     0          0\n",
              "17118  Washington      US  47.4009  ...          0     0          0\n",
              "17578  Washington      US  47.4009  ...          0     0          0\n",
              "18038  Washington      US  47.4009  ...          0     0          0\n",
              "18498  Washington      US  47.4009  ...          0     0          0\n",
              "18958  Washington      US  47.4009  ...          0     0          0\n",
              "19418  Washington      US  47.4009  ...          0     0          0\n",
              "19878  Washington      US  47.4009  ...          0     0          0\n",
              "20338  Washington      US  47.4009  ...          0     0          0\n",
              "20798  Washington      US  47.4009  ...          0     0          0\n",
              "21258  Washington      US  47.4009  ...          0     0          0\n",
              "21718  Washington      US  47.4009  ...          0     0          0\n",
              "22178  Washington      US  47.4009  ...        267    23          1\n",
              "22638  Washington      US  47.4009  ...        366    29          1\n",
              "23098  Washington      US  47.4009  ...        442    31          1\n",
              "23558  Washington      US  47.4009  ...        568    37          1\n",
              "24018  Washington      US  47.4009  ...        572    37          1\n",
              "24478  Washington      US  47.4009  ...        643    40          1\n",
              "24938  Washington      US  47.4009  ...        904    48          1\n",
              "25398  Washington      US  47.4009  ...       1076    55          1\n",
              "\n",
              "[56 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WQVFDj1Hnfs_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "93a31d93-e61c-4011-a189-ff6926207498"
      },
      "source": [
        "combined_df.query(\"Country=='China' & State=='Hubei'\")"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\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>State</th>\n",
              "      <th>Country</th>\n",
              "      <th>Lat</th>\n",
              "      <th>Long</th>\n",
              "      <th>Date</th>\n",
              "      <th>Confirmed</th>\n",
              "      <th>Death</th>\n",
              "      <th>Recovered</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>154</th>\n",
              "      <td>Hubei</td>\n",
              "      <td>China</td>\n",
              "      <td>30.9756</td>\n",
              "      <td>112.2707</td>\n",
              "      <td>2020-01-22</td>\n",
              "      <td>444</td>\n",
              "      <td>17</td>\n",
              "      <td>28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>614</th>\n",
              "      <td>Hubei</td>\n",
              "      <td>China</td>\n",
              "      <td>30.9756</td>\n",
              "      <td>112.2707</td>\n",
              "      <td>2020-01-23</td>\n",
              "      <td>444</td>\n",
              "      <td>17</td>\n",
              "      <td>28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1074</th>\n",
              "      <td>Hubei</td>\n",
              "      <td>China</td>\n",
              "      <td>30.9756</td>\n",
              "      <td>112.2707</td>\n",
              "      <td>2020-01-24</td>\n",
              "      <td>549</td>\n",
              "      <td>24</td>\n",
              "      <td>31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1534</th>\n",
              "      <td>Hubei</td>\n",
              "      <td>China</td>\n",
              "      <td>30.9756</td>\n",
              "      <td>112.2707</td>\n",
              "      <td>2020-01-25</td>\n",
              "      <td>761</td>\n",
              "      <td>40</td>\n",
              "      <td>32</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1994</th>\n",
              "      <td>Hubei</td>\n",
              "      <td>China</td>\n",
              "      <td>30.9756</td>\n",
              "      <td>112.2707</td>\n",
              "      <td>2020-01-26</td>\n",
              "      <td>1058</td>\n",
              "      <td>52</td>\n",
              "      <td>42</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2454</th>\n",
              "      <td>Hubei</td>\n",
              "      <td>China</td>\n",
              "      <td>30.9756</td>\n",
              "      <td>112.2707</td>\n",
              "      <td>2020-01-27</td>\n",
              "      <td>1423</td>\n",
              "      <td>76</td>\n",
              "      <td>45</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2914</th>\n",
              "      <td>Hubei</td>\n",
              "      <td>China</td>\n",
              "      <td>30.9756</td>\n",
              "      <td>112.2707</td>\n",
              "      <td>2020-01-28</td>\n",
              "      <td>3554</td>\n",
              "      <td>125</td>\n",
              "      <td>80</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3374</th>\n",
              "      <td>Hubei</td>\n",
              "      <td>China</td>\n",
              "      <td>30.9756</td>\n",
              "      <td>112.2707</td>\n",
              "      <td>2020-01-29</td>\n",
              "      <td>3554</td>\n",
              "      <td>125</td>\n",
              "      <td>88</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3834</th>\n",
              "      <td>Hubei</td>\n",
              "      <td>China</td>\n",
              "      <td>30.9756</td>\n",
              "      <td>112.2707</td>\n",
              "      <td>2020-01-30</td>\n",
              "      <td>4903</td>\n",
              "      <td>162</td>\n",
              "      <td>90</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4294</th>\n",
              "      <td>Hubei</td>\n",
              "      <td>China</td>\n",
              "      <td>30.9756</td>\n",
              "      <td>112.2707</td>\n",
              "      <td>2020-01-31</td>\n",
              "      <td>5806</td>\n",
              "      <td>204</td>\n",
              "      <td>141</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4754</th>\n",
              "      <td>Hubei</td>\n",
              "      <td>China</td>\n",
              "      <td>30.9756</td>\n",
              "      <td>112.2707</td>\n",
              "      <td>2020-02-01</td>\n",
              "      <td>7153</td>\n",
              "      <td>249</td>\n",
              "      <td>168</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5214</th>\n",
              "      <td>Hubei</td>\n",
              "      <td>China</td>\n",
              "      <td>30.9756</td>\n",
              "      <td>112.2707</td>\n",
              "      <td>2020-02-02</td>\n",
              "      <td>11177</td>\n",
              "      <td>350</td>\n",
              "      <td>295</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5674</th>\n",
              "      <td>Hubei</td>\n",
              "      <td>China</td>\n",
              "      <td>30.9756</td>\n",
              "      <td>112.2707</td>\n",
              "      <td>2020-02-03</td>\n",
              "      <td>13522</td>\n",
              "      <td>414</td>\n",
              "      <td>386</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6134</th>\n",
              "      <td>Hubei</td>\n",
              "      <td>China</td>\n",
              "      <td>30.9756</td>\n",
              "      <td>112.2707</td>\n",
              "      <td>2020-02-04</td>\n",
              "      <td>16678</td>\n",
              "      <td>479</td>\n",
              "      <td>522</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6594</th>\n",
              "      <td>Hubei</td>\n",
              "      <td>China</td>\n",
              "      <td>30.9756</td>\n",
              "      <td>112.2707</td>\n",
              "      <td>2020-02-05</td>\n",
              "      <td>19665</td>\n",
              "      <td>549</td>\n",
              "      <td>633</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7054</th>\n",
              "      <td>Hubei</td>\n",
              "      <td>China</td>\n",
              "      <td>30.9756</td>\n",
              "      <td>112.2707</td>\n",
              "      <td>2020-02-06</td>\n",
              "      <td>22112</td>\n",
              "      <td>618</td>\n",
              "      <td>817</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7514</th>\n",
              "      <td>Hubei</td>\n",
              "      <td>China</td>\n",
              "      <td>30.9756</td>\n",
              "      <td>112.2707</td>\n",
              "      <td>2020-02-07</td>\n",
              "      <td>24953</td>\n",
              "      <td>699</td>\n",
              "      <td>1115</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7974</th>\n",
              "      <td>Hubei</td>\n",
              "      <td>China</td>\n",
              "      <td>30.9756</td>\n",
              "      <td>112.2707</td>\n",
              "      <td>2020-02-08</td>\n",
              "      <td>27100</td>\n",
              "      <td>780</td>\n",
              "      <td>1439</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8434</th>\n",
              "      <td>Hubei</td>\n",
              "      <td>China</td>\n",
              "      <td>30.9756</td>\n",
              "      <td>112.2707</td>\n",
              "      <td>2020-02-09</td>\n",
              "      <td>29631</td>\n",
              "      <td>871</td>\n",
              "      <td>1795</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8894</th>\n",
              "      <td>Hubei</td>\n",
              "      <td>China</td>\n",
              "      <td>30.9756</td>\n",
              "      <td>112.2707</td>\n",
              "      <td>2020-02-10</td>\n",
              "      <td>31728</td>\n",
              "      <td>974</td>\n",
              "      <td>2222</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9354</th>\n",
              "      <td>Hubei</td>\n",
              "      <td>China</td>\n",
              "      <td>30.9756</td>\n",
              "      <td>112.2707</td>\n",
              "      <td>2020-02-11</td>\n",
              "      <td>33366</td>\n",
              "      <td>1068</td>\n",
              "      <td>2639</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9814</th>\n",
              "      <td>Hubei</td>\n",
              "      <td>China</td>\n",
              "      <td>30.9756</td>\n",
              "      <td>112.2707</td>\n",
              "      <td>2020-02-12</td>\n",
              "      <td>33366</td>\n",
              "      <td>1068</td>\n",
              "      <td>2686</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10274</th>\n",
              "      <td>Hubei</td>\n",
              "      <td>China</td>\n",
              "      <td>30.9756</td>\n",
              "      <td>112.2707</td>\n",
              "      <td>2020-02-13</td>\n",
              "      <td>48206</td>\n",
              "      <td>1310</td>\n",
              "      <td>3459</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10734</th>\n",
              "      <td>Hubei</td>\n",
              "      <td>China</td>\n",
              "      <td>30.9756</td>\n",
              "      <td>112.2707</td>\n",
              "      <td>2020-02-14</td>\n",
              "      <td>54406</td>\n",
              "      <td>1457</td>\n",
              "      <td>4774</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11194</th>\n",
              "      <td>Hubei</td>\n",
              "      <td>China</td>\n",
              "      <td>30.9756</td>\n",
              "      <td>112.2707</td>\n",
              "      <td>2020-02-15</td>\n",
              "      <td>56249</td>\n",
              "      <td>1596</td>\n",
              "      <td>5623</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11654</th>\n",
              "      <td>Hubei</td>\n",
              "      <td>China</td>\n",
              "      <td>30.9756</td>\n",
              "      <td>112.2707</td>\n",
              "      <td>2020-02-16</td>\n",
              "      <td>58182</td>\n",
              "      <td>1696</td>\n",
              "      <td>6639</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12114</th>\n",
              "      <td>Hubei</td>\n",
              "      <td>China</td>\n",
              "      <td>30.9756</td>\n",
              "      <td>112.2707</td>\n",
              "      <td>2020-02-17</td>\n",
              "      <td>59989</td>\n",
              "      <td>1789</td>\n",
              "      <td>7862</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12574</th>\n",
              "      <td>Hubei</td>\n",
              "      <td>China</td>\n",
              "      <td>30.9756</td>\n",
              "      <td>112.2707</td>\n",
              "      <td>2020-02-18</td>\n",
              "      <td>61682</td>\n",
              "      <td>1921</td>\n",
              "      <td>9128</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13034</th>\n",
              "      <td>Hubei</td>\n",
              "      <td>China</td>\n",
              "      <td>30.9756</td>\n",
              "      <td>112.2707</td>\n",
              "      <td>2020-02-19</td>\n",
              "      <td>62031</td>\n",
              "      <td>2029</td>\n",
              "      <td>10337</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13494</th>\n",
              "      <td>Hubei</td>\n",
              "      <td>China</td>\n",
              "      <td>30.9756</td>\n",
              "      <td>112.2707</td>\n",
              "      <td>2020-02-20</td>\n",
              "      <td>62442</td>\n",
              "      <td>2144</td>\n",
              "      <td>11788</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13954</th>\n",
              "      <td>Hubei</td>\n",
              "      <td>China</td>\n",
              "      <td>30.9756</td>\n",
              "      <td>112.2707</td>\n",
              "      <td>2020-02-21</td>\n",
              "      <td>62662</td>\n",
              "      <td>2144</td>\n",
              "      <td>11881</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14414</th>\n",
              "      <td>Hubei</td>\n",
              "      <td>China</td>\n",
              "      <td>30.9756</td>\n",
              "      <td>112.2707</td>\n",
              "      <td>2020-02-22</td>\n",
              "      <td>64084</td>\n",
              "      <td>2346</td>\n",
              "      <td>15299</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14874</th>\n",
              "      <td>Hubei</td>\n",
              "      <td>China</td>\n",
              "      <td>30.9756</td>\n",
              "      <td>112.2707</td>\n",
              "      <td>2020-02-23</td>\n",
              "      <td>64084</td>\n",
              "      <td>2346</td>\n",
              "      <td>15343</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15334</th>\n",
              "      <td>Hubei</td>\n",
              "      <td>China</td>\n",
              "      <td>30.9756</td>\n",
              "      <td>112.2707</td>\n",
              "      <td>2020-02-24</td>\n",
              "      <td>64287</td>\n",
              "      <td>2495</td>\n",
              "      <td>16748</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15794</th>\n",
              "      <td>Hubei</td>\n",
              "      <td>China</td>\n",
              "      <td>30.9756</td>\n",
              "      <td>112.2707</td>\n",
              "      <td>2020-02-25</td>\n",
              "      <td>64786</td>\n",
              "      <td>2563</td>\n",
              "      <td>18971</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16254</th>\n",
              "      <td>Hubei</td>\n",
              "      <td>China</td>\n",
              "      <td>30.9756</td>\n",
              "      <td>112.2707</td>\n",
              "      <td>2020-02-26</td>\n",
              "      <td>65187</td>\n",
              "      <td>2615</td>\n",
              "      <td>20969</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16714</th>\n",
              "      <td>Hubei</td>\n",
              "      <td>China</td>\n",
              "      <td>30.9756</td>\n",
              "      <td>112.2707</td>\n",
              "      <td>2020-02-27</td>\n",
              "      <td>65596</td>\n",
              "      <td>2641</td>\n",
              "      <td>23383</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17174</th>\n",
              "      <td>Hubei</td>\n",
              "      <td>China</td>\n",
              "      <td>30.9756</td>\n",
              "      <td>112.2707</td>\n",
              "      <td>2020-02-28</td>\n",
              "      <td>65914</td>\n",
              "      <td>2682</td>\n",
              "      <td>26403</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17634</th>\n",
              "      <td>Hubei</td>\n",
              "      <td>China</td>\n",
              "      <td>30.9756</td>\n",
              "      <td>112.2707</td>\n",
              "      <td>2020-02-29</td>\n",
              "      <td>66337</td>\n",
              "      <td>2727</td>\n",
              "      <td>28993</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18094</th>\n",
              "      <td>Hubei</td>\n",
              "      <td>China</td>\n",
              "      <td>30.9756</td>\n",
              "      <td>112.2707</td>\n",
              "      <td>2020-03-01</td>\n",
              "      <td>66907</td>\n",
              "      <td>2761</td>\n",
              "      <td>31536</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18554</th>\n",
              "      <td>Hubei</td>\n",
              "      <td>China</td>\n",
              "      <td>30.9756</td>\n",
              "      <td>112.2707</td>\n",
              "      <td>2020-03-02</td>\n",
              "      <td>67103</td>\n",
              "      <td>2803</td>\n",
              "      <td>33934</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19014</th>\n",
              "      <td>Hubei</td>\n",
              "      <td>China</td>\n",
              "      <td>30.9756</td>\n",
              "      <td>112.2707</td>\n",
              "      <td>2020-03-03</td>\n",
              "      <td>67217</td>\n",
              "      <td>2835</td>\n",
              "      <td>36208</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19474</th>\n",
              "      <td>Hubei</td>\n",
              "      <td>China</td>\n",
              "      <td>30.9756</td>\n",
              "      <td>112.2707</td>\n",
              "      <td>2020-03-04</td>\n",
              "      <td>67332</td>\n",
              "      <td>2871</td>\n",
              "      <td>38557</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19934</th>\n",
              "      <td>Hubei</td>\n",
              "      <td>China</td>\n",
              "      <td>30.9756</td>\n",
              "      <td>112.2707</td>\n",
              "      <td>2020-03-05</td>\n",
              "      <td>67466</td>\n",
              "      <td>2902</td>\n",
              "      <td>40592</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20394</th>\n",
              "      <td>Hubei</td>\n",
              "      <td>China</td>\n",
              "      <td>30.9756</td>\n",
              "      <td>112.2707</td>\n",
              "      <td>2020-03-06</td>\n",
              "      <td>67592</td>\n",
              "      <td>2931</td>\n",
              "      <td>42033</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20854</th>\n",
              "      <td>Hubei</td>\n",
              "      <td>China</td>\n",
              "      <td>30.9756</td>\n",
              "      <td>112.2707</td>\n",
              "      <td>2020-03-07</td>\n",
              "      <td>67666</td>\n",
              "      <td>2959</td>\n",
              "      <td>43500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21314</th>\n",
              "      <td>Hubei</td>\n",
              "      <td>China</td>\n",
              "      <td>30.9756</td>\n",
              "      <td>112.2707</td>\n",
              "      <td>2020-03-08</td>\n",
              "      <td>67707</td>\n",
              "      <td>2986</td>\n",
              "      <td>45235</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21774</th>\n",
              "      <td>Hubei</td>\n",
              "      <td>China</td>\n",
              "      <td>30.9756</td>\n",
              "      <td>112.2707</td>\n",
              "      <td>2020-03-09</td>\n",
              "      <td>67743</td>\n",
              "      <td>3008</td>\n",
              "      <td>46488</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22234</th>\n",
              "      <td>Hubei</td>\n",
              "      <td>China</td>\n",
              "      <td>30.9756</td>\n",
              "      <td>112.2707</td>\n",
              "      <td>2020-03-10</td>\n",
              "      <td>67760</td>\n",
              "      <td>3024</td>\n",
              "      <td>47743</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22694</th>\n",
              "      <td>Hubei</td>\n",
              "      <td>China</td>\n",
              "      <td>30.9756</td>\n",
              "      <td>112.2707</td>\n",
              "      <td>2020-03-11</td>\n",
              "      <td>67773</td>\n",
              "      <td>3046</td>\n",
              "      <td>49134</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23154</th>\n",
              "      <td>Hubei</td>\n",
              "      <td>China</td>\n",
              "      <td>30.9756</td>\n",
              "      <td>112.2707</td>\n",
              "      <td>2020-03-12</td>\n",
              "      <td>67781</td>\n",
              "      <td>3056</td>\n",
              "      <td>50318</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23614</th>\n",
              "      <td>Hubei</td>\n",
              "      <td>China</td>\n",
              "      <td>30.9756</td>\n",
              "      <td>112.2707</td>\n",
              "      <td>2020-03-13</td>\n",
              "      <td>67786</td>\n",
              "      <td>3062</td>\n",
              "      <td>51553</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24074</th>\n",
              "      <td>Hubei</td>\n",
              "      <td>China</td>\n",
              "      <td>30.9756</td>\n",
              "      <td>112.2707</td>\n",
              "      <td>2020-03-14</td>\n",
              "      <td>67790</td>\n",
              "      <td>3075</td>\n",
              "      <td>52960</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24534</th>\n",
              "      <td>Hubei</td>\n",
              "      <td>China</td>\n",
              "      <td>30.9756</td>\n",
              "      <td>112.2707</td>\n",
              "      <td>2020-03-15</td>\n",
              "      <td>67794</td>\n",
              "      <td>3085</td>\n",
              "      <td>54288</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24994</th>\n",
              "      <td>Hubei</td>\n",
              "      <td>China</td>\n",
              "      <td>30.9756</td>\n",
              "      <td>112.2707</td>\n",
              "      <td>2020-03-16</td>\n",
              "      <td>67798</td>\n",
              "      <td>3099</td>\n",
              "      <td>55142</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25454</th>\n",
              "      <td>Hubei</td>\n",
              "      <td>China</td>\n",
              "      <td>30.9756</td>\n",
              "      <td>112.2707</td>\n",
              "      <td>2020-03-17</td>\n",
              "      <td>67799</td>\n",
              "      <td>3111</td>\n",
              "      <td>56003</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       State Country      Lat      Long       Date  Confirmed  Death  Recovered\n",
              "154    Hubei   China  30.9756  112.2707 2020-01-22        444     17         28\n",
              "614    Hubei   China  30.9756  112.2707 2020-01-23        444     17         28\n",
              "1074   Hubei   China  30.9756  112.2707 2020-01-24        549     24         31\n",
              "1534   Hubei   China  30.9756  112.2707 2020-01-25        761     40         32\n",
              "1994   Hubei   China  30.9756  112.2707 2020-01-26       1058     52         42\n",
              "2454   Hubei   China  30.9756  112.2707 2020-01-27       1423     76         45\n",
              "2914   Hubei   China  30.9756  112.2707 2020-01-28       3554    125         80\n",
              "3374   Hubei   China  30.9756  112.2707 2020-01-29       3554    125         88\n",
              "3834   Hubei   China  30.9756  112.2707 2020-01-30       4903    162         90\n",
              "4294   Hubei   China  30.9756  112.2707 2020-01-31       5806    204        141\n",
              "4754   Hubei   China  30.9756  112.2707 2020-02-01       7153    249        168\n",
              "5214   Hubei   China  30.9756  112.2707 2020-02-02      11177    350        295\n",
              "5674   Hubei   China  30.9756  112.2707 2020-02-03      13522    414        386\n",
              "6134   Hubei   China  30.9756  112.2707 2020-02-04      16678    479        522\n",
              "6594   Hubei   China  30.9756  112.2707 2020-02-05      19665    549        633\n",
              "7054   Hubei   China  30.9756  112.2707 2020-02-06      22112    618        817\n",
              "7514   Hubei   China  30.9756  112.2707 2020-02-07      24953    699       1115\n",
              "7974   Hubei   China  30.9756  112.2707 2020-02-08      27100    780       1439\n",
              "8434   Hubei   China  30.9756  112.2707 2020-02-09      29631    871       1795\n",
              "8894   Hubei   China  30.9756  112.2707 2020-02-10      31728    974       2222\n",
              "9354   Hubei   China  30.9756  112.2707 2020-02-11      33366   1068       2639\n",
              "9814   Hubei   China  30.9756  112.2707 2020-02-12      33366   1068       2686\n",
              "10274  Hubei   China  30.9756  112.2707 2020-02-13      48206   1310       3459\n",
              "10734  Hubei   China  30.9756  112.2707 2020-02-14      54406   1457       4774\n",
              "11194  Hubei   China  30.9756  112.2707 2020-02-15      56249   1596       5623\n",
              "11654  Hubei   China  30.9756  112.2707 2020-02-16      58182   1696       6639\n",
              "12114  Hubei   China  30.9756  112.2707 2020-02-17      59989   1789       7862\n",
              "12574  Hubei   China  30.9756  112.2707 2020-02-18      61682   1921       9128\n",
              "13034  Hubei   China  30.9756  112.2707 2020-02-19      62031   2029      10337\n",
              "13494  Hubei   China  30.9756  112.2707 2020-02-20      62442   2144      11788\n",
              "13954  Hubei   China  30.9756  112.2707 2020-02-21      62662   2144      11881\n",
              "14414  Hubei   China  30.9756  112.2707 2020-02-22      64084   2346      15299\n",
              "14874  Hubei   China  30.9756  112.2707 2020-02-23      64084   2346      15343\n",
              "15334  Hubei   China  30.9756  112.2707 2020-02-24      64287   2495      16748\n",
              "15794  Hubei   China  30.9756  112.2707 2020-02-25      64786   2563      18971\n",
              "16254  Hubei   China  30.9756  112.2707 2020-02-26      65187   2615      20969\n",
              "16714  Hubei   China  30.9756  112.2707 2020-02-27      65596   2641      23383\n",
              "17174  Hubei   China  30.9756  112.2707 2020-02-28      65914   2682      26403\n",
              "17634  Hubei   China  30.9756  112.2707 2020-02-29      66337   2727      28993\n",
              "18094  Hubei   China  30.9756  112.2707 2020-03-01      66907   2761      31536\n",
              "18554  Hubei   China  30.9756  112.2707 2020-03-02      67103   2803      33934\n",
              "19014  Hubei   China  30.9756  112.2707 2020-03-03      67217   2835      36208\n",
              "19474  Hubei   China  30.9756  112.2707 2020-03-04      67332   2871      38557\n",
              "19934  Hubei   China  30.9756  112.2707 2020-03-05      67466   2902      40592\n",
              "20394  Hubei   China  30.9756  112.2707 2020-03-06      67592   2931      42033\n",
              "20854  Hubei   China  30.9756  112.2707 2020-03-07      67666   2959      43500\n",
              "21314  Hubei   China  30.9756  112.2707 2020-03-08      67707   2986      45235\n",
              "21774  Hubei   China  30.9756  112.2707 2020-03-09      67743   3008      46488\n",
              "22234  Hubei   China  30.9756  112.2707 2020-03-10      67760   3024      47743\n",
              "22694  Hubei   China  30.9756  112.2707 2020-03-11      67773   3046      49134\n",
              "23154  Hubei   China  30.9756  112.2707 2020-03-12      67781   3056      50318\n",
              "23614  Hubei   China  30.9756  112.2707 2020-03-13      67786   3062      51553\n",
              "24074  Hubei   China  30.9756  112.2707 2020-03-14      67790   3075      52960\n",
              "24534  Hubei   China  30.9756  112.2707 2020-03-15      67794   3085      54288\n",
              "24994  Hubei   China  30.9756  112.2707 2020-03-16      67798   3099      55142\n",
              "25454  Hubei   China  30.9756  112.2707 2020-03-17      67799   3111      56003"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VlRPqeV4nfkI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "combined_df['Active']=combined_df['Confirmed'] - combined_df['Death'] - combined_df['Recovered']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZY-SEdVMnfYq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "e25791a4-36d4-4bdd-a508-f84ee0b786f2"
      },
      "source": [
        "combined_df.query(\"Country=='China' & State=='Hubei'\")"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>State</th>\n",
              "      <th>Country</th>\n",
              "      <th>Lat</th>\n",
              "      <th>Long</th>\n",
              "      <th>Date</th>\n",
              "      <th>Confirmed</th>\n",
              "      <th>Death</th>\n",
              "      <th>Recovered</th>\n",
              "      <th>Active</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>154</th>\n",
              "      <td>Hubei</td>\n",
              "      <td>China</td>\n",
              "      <td>30.9756</td>\n",
              "      <td>112.2707</td>\n",
              "      <td>2020-01-22</td>\n",
              "      <td>444</td>\n",
              "      <td>17</td>\n",
              "      <td>28</td>\n",
              "      <td>399</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>614</th>\n",
              "      <td>Hubei</td>\n",
              "      <td>China</td>\n",
              "      <td>30.9756</td>\n",
              "      <td>112.2707</td>\n",
              "      <td>2020-01-23</td>\n",
              "      <td>444</td>\n",
              "      <td>17</td>\n",
              "      <td>28</td>\n",
              "      <td>399</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1074</th>\n",
              "      <td>Hubei</td>\n",
              "      <td>China</td>\n",
              "      <td>30.9756</td>\n",
              "      <td>112.2707</td>\n",
              "      <td>2020-01-24</td>\n",
              "      <td>549</td>\n",
              "      <td>24</td>\n",
              "      <td>31</td>\n",
              "      <td>494</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1534</th>\n",
              "      <td>Hubei</td>\n",
              "      <td>China</td>\n",
              "      <td>30.9756</td>\n",
              "      <td>112.2707</td>\n",
              "      <td>2020-01-25</td>\n",
              "      <td>761</td>\n",
              "      <td>40</td>\n",
              "      <td>32</td>\n",
              "      <td>689</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1994</th>\n",
              "      <td>Hubei</td>\n",
              "      <td>China</td>\n",
              "      <td>30.9756</td>\n",
              "      <td>112.2707</td>\n",
              "      <td>2020-01-26</td>\n",
              "      <td>1058</td>\n",
              "      <td>52</td>\n",
              "      <td>42</td>\n",
              "      <td>964</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2454</th>\n",
              "      <td>Hubei</td>\n",
              "      <td>China</td>\n",
              "      <td>30.9756</td>\n",
              "      <td>112.2707</td>\n",
              "      <td>2020-01-27</td>\n",
              "      <td>1423</td>\n",
              "      <td>76</td>\n",
              "      <td>45</td>\n",
              "      <td>1302</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2914</th>\n",
              "      <td>Hubei</td>\n",
              "      <td>China</td>\n",
              "      <td>30.9756</td>\n",
              "      <td>112.2707</td>\n",
              "      <td>2020-01-28</td>\n",
              "      <td>3554</td>\n",
              "      <td>125</td>\n",
              "      <td>80</td>\n",
              "      <td>3349</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3374</th>\n",
              "      <td>Hubei</td>\n",
              "      <td>China</td>\n",
              "      <td>30.9756</td>\n",
              "      <td>112.2707</td>\n",
              "      <td>2020-01-29</td>\n",
              "      <td>3554</td>\n",
              "      <td>125</td>\n",
              "      <td>88</td>\n",
              "      <td>3341</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3834</th>\n",
              "      <td>Hubei</td>\n",
              "      <td>China</td>\n",
              "      <td>30.9756</td>\n",
              "      <td>112.2707</td>\n",
              "      <td>2020-01-30</td>\n",
              "      <td>4903</td>\n",
              "      <td>162</td>\n",
              "      <td>90</td>\n",
              "      <td>4651</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4294</th>\n",
              "      <td>Hubei</td>\n",
              "      <td>China</td>\n",
              "      <td>30.9756</td>\n",
              "      <td>112.2707</td>\n",
              "      <td>2020-01-31</td>\n",
              "      <td>5806</td>\n",
              "      <td>204</td>\n",
              "      <td>141</td>\n",
              "      <td>5461</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4754</th>\n",
              "      <td>Hubei</td>\n",
              "      <td>China</td>\n",
              "      <td>30.9756</td>\n",
              "      <td>112.2707</td>\n",
              "      <td>2020-02-01</td>\n",
              "      <td>7153</td>\n",
              "      <td>249</td>\n",
              "      <td>168</td>\n",
              "      <td>6736</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5214</th>\n",
              "      <td>Hubei</td>\n",
              "      <td>China</td>\n",
              "      <td>30.9756</td>\n",
              "      <td>112.2707</td>\n",
              "      <td>2020-02-02</td>\n",
              "      <td>11177</td>\n",
              "      <td>350</td>\n",
              "      <td>295</td>\n",
              "      <td>10532</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5674</th>\n",
              "      <td>Hubei</td>\n",
              "      <td>China</td>\n",
              "      <td>30.9756</td>\n",
              "      <td>112.2707</td>\n",
              "      <td>2020-02-03</td>\n",
              "      <td>13522</td>\n",
              "      <td>414</td>\n",
              "      <td>386</td>\n",
              "      <td>12722</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6134</th>\n",
              "      <td>Hubei</td>\n",
              "      <td>China</td>\n",
              "      <td>30.9756</td>\n",
              "      <td>112.2707</td>\n",
              "      <td>2020-02-04</td>\n",
              "      <td>16678</td>\n",
              "      <td>479</td>\n",
              "      <td>522</td>\n",
              "      <td>15677</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6594</th>\n",
              "      <td>Hubei</td>\n",
              "      <td>China</td>\n",
              "      <td>30.9756</td>\n",
              "      <td>112.2707</td>\n",
              "      <td>2020-02-05</td>\n",
              "      <td>19665</td>\n",
              "      <td>549</td>\n",
              "      <td>633</td>\n",
              "      <td>18483</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7054</th>\n",
              "      <td>Hubei</td>\n",
              "      <td>China</td>\n",
              "      <td>30.9756</td>\n",
              "      <td>112.2707</td>\n",
              "      <td>2020-02-06</td>\n",
              "      <td>22112</td>\n",
              "      <td>618</td>\n",
              "      <td>817</td>\n",
              "      <td>20677</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7514</th>\n",
              "      <td>Hubei</td>\n",
              "      <td>China</td>\n",
              "      <td>30.9756</td>\n",
              "      <td>112.2707</td>\n",
              "      <td>2020-02-07</td>\n",
              "      <td>24953</td>\n",
              "      <td>699</td>\n",
              "      <td>1115</td>\n",
              "      <td>23139</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7974</th>\n",
              "      <td>Hubei</td>\n",
              "      <td>China</td>\n",
              "      <td>30.9756</td>\n",
              "      <td>112.2707</td>\n",
              "      <td>2020-02-08</td>\n",
              "      <td>27100</td>\n",
              "      <td>780</td>\n",
              "      <td>1439</td>\n",
              "      <td>24881</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8434</th>\n",
              "      <td>Hubei</td>\n",
              "      <td>China</td>\n",
              "      <td>30.9756</td>\n",
              "      <td>112.2707</td>\n",
              "      <td>2020-02-09</td>\n",
              "      <td>29631</td>\n",
              "      <td>871</td>\n",
              "      <td>1795</td>\n",
              "      <td>26965</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8894</th>\n",
              "      <td>Hubei</td>\n",
              "      <td>China</td>\n",
              "      <td>30.9756</td>\n",
              "      <td>112.2707</td>\n",
              "      <td>2020-02-10</td>\n",
              "      <td>31728</td>\n",
              "      <td>974</td>\n",
              "      <td>2222</td>\n",
              "      <td>28532</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9354</th>\n",
              "      <td>Hubei</td>\n",
              "      <td>China</td>\n",
              "      <td>30.9756</td>\n",
              "      <td>112.2707</td>\n",
              "      <td>2020-02-11</td>\n",
              "      <td>33366</td>\n",
              "      <td>1068</td>\n",
              "      <td>2639</td>\n",
              "      <td>29659</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9814</th>\n",
              "      <td>Hubei</td>\n",
              "      <td>China</td>\n",
              "      <td>30.9756</td>\n",
              "      <td>112.2707</td>\n",
              "      <td>2020-02-12</td>\n",
              "      <td>33366</td>\n",
              "      <td>1068</td>\n",
              "      <td>2686</td>\n",
              "      <td>29612</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10274</th>\n",
              "      <td>Hubei</td>\n",
              "      <td>China</td>\n",
              "      <td>30.9756</td>\n",
              "      <td>112.2707</td>\n",
              "      <td>2020-02-13</td>\n",
              "      <td>48206</td>\n",
              "      <td>1310</td>\n",
              "      <td>3459</td>\n",
              "      <td>43437</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10734</th>\n",
              "      <td>Hubei</td>\n",
              "      <td>China</td>\n",
              "      <td>30.9756</td>\n",
              "      <td>112.2707</td>\n",
              "      <td>2020-02-14</td>\n",
              "      <td>54406</td>\n",
              "      <td>1457</td>\n",
              "      <td>4774</td>\n",
              "      <td>48175</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11194</th>\n",
              "      <td>Hubei</td>\n",
              "      <td>China</td>\n",
              "      <td>30.9756</td>\n",
              "      <td>112.2707</td>\n",
              "      <td>2020-02-15</td>\n",
              "      <td>56249</td>\n",
              "      <td>1596</td>\n",
              "      <td>5623</td>\n",
              "      <td>49030</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11654</th>\n",
              "      <td>Hubei</td>\n",
              "      <td>China</td>\n",
              "      <td>30.9756</td>\n",
              "      <td>112.2707</td>\n",
              "      <td>2020-02-16</td>\n",
              "      <td>58182</td>\n",
              "      <td>1696</td>\n",
              "      <td>6639</td>\n",
              "      <td>49847</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12114</th>\n",
              "      <td>Hubei</td>\n",
              "      <td>China</td>\n",
              "      <td>30.9756</td>\n",
              "      <td>112.2707</td>\n",
              "      <td>2020-02-17</td>\n",
              "      <td>59989</td>\n",
              "      <td>1789</td>\n",
              "      <td>7862</td>\n",
              "      <td>50338</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12574</th>\n",
              "      <td>Hubei</td>\n",
              "      <td>China</td>\n",
              "      <td>30.9756</td>\n",
              "      <td>112.2707</td>\n",
              "      <td>2020-02-18</td>\n",
              "      <td>61682</td>\n",
              "      <td>1921</td>\n",
              "      <td>9128</td>\n",
              "      <td>50633</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13034</th>\n",
              "      <td>Hubei</td>\n",
              "      <td>China</td>\n",
              "      <td>30.9756</td>\n",
              "      <td>112.2707</td>\n",
              "      <td>2020-02-19</td>\n",
              "      <td>62031</td>\n",
              "      <td>2029</td>\n",
              "      <td>10337</td>\n",
              "      <td>49665</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13494</th>\n",
              "      <td>Hubei</td>\n",
              "      <td>China</td>\n",
              "      <td>30.9756</td>\n",
              "      <td>112.2707</td>\n",
              "      <td>2020-02-20</td>\n",
              "      <td>62442</td>\n",
              "      <td>2144</td>\n",
              "      <td>11788</td>\n",
              "      <td>48510</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13954</th>\n",
              "      <td>Hubei</td>\n",
              "      <td>China</td>\n",
              "      <td>30.9756</td>\n",
              "      <td>112.2707</td>\n",
              "      <td>2020-02-21</td>\n",
              "      <td>62662</td>\n",
              "      <td>2144</td>\n",
              "      <td>11881</td>\n",
              "      <td>48637</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14414</th>\n",
              "      <td>Hubei</td>\n",
              "      <td>China</td>\n",
              "      <td>30.9756</td>\n",
              "      <td>112.2707</td>\n",
              "      <td>2020-02-22</td>\n",
              "      <td>64084</td>\n",
              "      <td>2346</td>\n",
              "      <td>15299</td>\n",
              "      <td>46439</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14874</th>\n",
              "      <td>Hubei</td>\n",
              "      <td>China</td>\n",
              "      <td>30.9756</td>\n",
              "      <td>112.2707</td>\n",
              "      <td>2020-02-23</td>\n",
              "      <td>64084</td>\n",
              "      <td>2346</td>\n",
              "      <td>15343</td>\n",
              "      <td>46395</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15334</th>\n",
              "      <td>Hubei</td>\n",
              "      <td>China</td>\n",
              "      <td>30.9756</td>\n",
              "      <td>112.2707</td>\n",
              "      <td>2020-02-24</td>\n",
              "      <td>64287</td>\n",
              "      <td>2495</td>\n",
              "      <td>16748</td>\n",
              "      <td>45044</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15794</th>\n",
              "      <td>Hubei</td>\n",
              "      <td>China</td>\n",
              "      <td>30.9756</td>\n",
              "      <td>112.2707</td>\n",
              "      <td>2020-02-25</td>\n",
              "      <td>64786</td>\n",
              "      <td>2563</td>\n",
              "      <td>18971</td>\n",
              "      <td>43252</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16254</th>\n",
              "      <td>Hubei</td>\n",
              "      <td>China</td>\n",
              "      <td>30.9756</td>\n",
              "      <td>112.2707</td>\n",
              "      <td>2020-02-26</td>\n",
              "      <td>65187</td>\n",
              "      <td>2615</td>\n",
              "      <td>20969</td>\n",
              "      <td>41603</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16714</th>\n",
              "      <td>Hubei</td>\n",
              "      <td>China</td>\n",
              "      <td>30.9756</td>\n",
              "      <td>112.2707</td>\n",
              "      <td>2020-02-27</td>\n",
              "      <td>65596</td>\n",
              "      <td>2641</td>\n",
              "      <td>23383</td>\n",
              "      <td>39572</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17174</th>\n",
              "      <td>Hubei</td>\n",
              "      <td>China</td>\n",
              "      <td>30.9756</td>\n",
              "      <td>112.2707</td>\n",
              "      <td>2020-02-28</td>\n",
              "      <td>65914</td>\n",
              "      <td>2682</td>\n",
              "      <td>26403</td>\n",
              "      <td>36829</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17634</th>\n",
              "      <td>Hubei</td>\n",
              "      <td>China</td>\n",
              "      <td>30.9756</td>\n",
              "      <td>112.2707</td>\n",
              "      <td>2020-02-29</td>\n",
              "      <td>66337</td>\n",
              "      <td>2727</td>\n",
              "      <td>28993</td>\n",
              "      <td>34617</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18094</th>\n",
              "      <td>Hubei</td>\n",
              "      <td>China</td>\n",
              "      <td>30.9756</td>\n",
              "      <td>112.2707</td>\n",
              "      <td>2020-03-01</td>\n",
              "      <td>66907</td>\n",
              "      <td>2761</td>\n",
              "      <td>31536</td>\n",
              "      <td>32610</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18554</th>\n",
              "      <td>Hubei</td>\n",
              "      <td>China</td>\n",
              "      <td>30.9756</td>\n",
              "      <td>112.2707</td>\n",
              "      <td>2020-03-02</td>\n",
              "      <td>67103</td>\n",
              "      <td>2803</td>\n",
              "      <td>33934</td>\n",
              "      <td>30366</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19014</th>\n",
              "      <td>Hubei</td>\n",
              "      <td>China</td>\n",
              "      <td>30.9756</td>\n",
              "      <td>112.2707</td>\n",
              "      <td>2020-03-03</td>\n",
              "      <td>67217</td>\n",
              "      <td>2835</td>\n",
              "      <td>36208</td>\n",
              "      <td>28174</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19474</th>\n",
              "      <td>Hubei</td>\n",
              "      <td>China</td>\n",
              "      <td>30.9756</td>\n",
              "      <td>112.2707</td>\n",
              "      <td>2020-03-04</td>\n",
              "      <td>67332</td>\n",
              "      <td>2871</td>\n",
              "      <td>38557</td>\n",
              "      <td>25904</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19934</th>\n",
              "      <td>Hubei</td>\n",
              "      <td>China</td>\n",
              "      <td>30.9756</td>\n",
              "      <td>112.2707</td>\n",
              "      <td>2020-03-05</td>\n",
              "      <td>67466</td>\n",
              "      <td>2902</td>\n",
              "      <td>40592</td>\n",
              "      <td>23972</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20394</th>\n",
              "      <td>Hubei</td>\n",
              "      <td>China</td>\n",
              "      <td>30.9756</td>\n",
              "      <td>112.2707</td>\n",
              "      <td>2020-03-06</td>\n",
              "      <td>67592</td>\n",
              "      <td>2931</td>\n",
              "      <td>42033</td>\n",
              "      <td>22628</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20854</th>\n",
              "      <td>Hubei</td>\n",
              "      <td>China</td>\n",
              "      <td>30.9756</td>\n",
              "      <td>112.2707</td>\n",
              "      <td>2020-03-07</td>\n",
              "      <td>67666</td>\n",
              "      <td>2959</td>\n",
              "      <td>43500</td>\n",
              "      <td>21207</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21314</th>\n",
              "      <td>Hubei</td>\n",
              "      <td>China</td>\n",
              "      <td>30.9756</td>\n",
              "      <td>112.2707</td>\n",
              "      <td>2020-03-08</td>\n",
              "      <td>67707</td>\n",
              "      <td>2986</td>\n",
              "      <td>45235</td>\n",
              "      <td>19486</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21774</th>\n",
              "      <td>Hubei</td>\n",
              "      <td>China</td>\n",
              "      <td>30.9756</td>\n",
              "      <td>112.2707</td>\n",
              "      <td>2020-03-09</td>\n",
              "      <td>67743</td>\n",
              "      <td>3008</td>\n",
              "      <td>46488</td>\n",
              "      <td>18247</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22234</th>\n",
              "      <td>Hubei</td>\n",
              "      <td>China</td>\n",
              "      <td>30.9756</td>\n",
              "      <td>112.2707</td>\n",
              "      <td>2020-03-10</td>\n",
              "      <td>67760</td>\n",
              "      <td>3024</td>\n",
              "      <td>47743</td>\n",
              "      <td>16993</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22694</th>\n",
              "      <td>Hubei</td>\n",
              "      <td>China</td>\n",
              "      <td>30.9756</td>\n",
              "      <td>112.2707</td>\n",
              "      <td>2020-03-11</td>\n",
              "      <td>67773</td>\n",
              "      <td>3046</td>\n",
              "      <td>49134</td>\n",
              "      <td>15593</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23154</th>\n",
              "      <td>Hubei</td>\n",
              "      <td>China</td>\n",
              "      <td>30.9756</td>\n",
              "      <td>112.2707</td>\n",
              "      <td>2020-03-12</td>\n",
              "      <td>67781</td>\n",
              "      <td>3056</td>\n",
              "      <td>50318</td>\n",
              "      <td>14407</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23614</th>\n",
              "      <td>Hubei</td>\n",
              "      <td>China</td>\n",
              "      <td>30.9756</td>\n",
              "      <td>112.2707</td>\n",
              "      <td>2020-03-13</td>\n",
              "      <td>67786</td>\n",
              "      <td>3062</td>\n",
              "      <td>51553</td>\n",
              "      <td>13171</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24074</th>\n",
              "      <td>Hubei</td>\n",
              "      <td>China</td>\n",
              "      <td>30.9756</td>\n",
              "      <td>112.2707</td>\n",
              "      <td>2020-03-14</td>\n",
              "      <td>67790</td>\n",
              "      <td>3075</td>\n",
              "      <td>52960</td>\n",
              "      <td>11755</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24534</th>\n",
              "      <td>Hubei</td>\n",
              "      <td>China</td>\n",
              "      <td>30.9756</td>\n",
              "      <td>112.2707</td>\n",
              "      <td>2020-03-15</td>\n",
              "      <td>67794</td>\n",
              "      <td>3085</td>\n",
              "      <td>54288</td>\n",
              "      <td>10421</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24994</th>\n",
              "      <td>Hubei</td>\n",
              "      <td>China</td>\n",
              "      <td>30.9756</td>\n",
              "      <td>112.2707</td>\n",
              "      <td>2020-03-16</td>\n",
              "      <td>67798</td>\n",
              "      <td>3099</td>\n",
              "      <td>55142</td>\n",
              "      <td>9557</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25454</th>\n",
              "      <td>Hubei</td>\n",
              "      <td>China</td>\n",
              "      <td>30.9756</td>\n",
              "      <td>112.2707</td>\n",
              "      <td>2020-03-17</td>\n",
              "      <td>67799</td>\n",
              "      <td>3111</td>\n",
              "      <td>56003</td>\n",
              "      <td>8685</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       State Country      Lat      Long  ... Confirmed  Death  Recovered  Active\n",
              "154    Hubei   China  30.9756  112.2707  ...       444     17         28     399\n",
              "614    Hubei   China  30.9756  112.2707  ...       444     17         28     399\n",
              "1074   Hubei   China  30.9756  112.2707  ...       549     24         31     494\n",
              "1534   Hubei   China  30.9756  112.2707  ...       761     40         32     689\n",
              "1994   Hubei   China  30.9756  112.2707  ...      1058     52         42     964\n",
              "2454   Hubei   China  30.9756  112.2707  ...      1423     76         45    1302\n",
              "2914   Hubei   China  30.9756  112.2707  ...      3554    125         80    3349\n",
              "3374   Hubei   China  30.9756  112.2707  ...      3554    125         88    3341\n",
              "3834   Hubei   China  30.9756  112.2707  ...      4903    162         90    4651\n",
              "4294   Hubei   China  30.9756  112.2707  ...      5806    204        141    5461\n",
              "4754   Hubei   China  30.9756  112.2707  ...      7153    249        168    6736\n",
              "5214   Hubei   China  30.9756  112.2707  ...     11177    350        295   10532\n",
              "5674   Hubei   China  30.9756  112.2707  ...     13522    414        386   12722\n",
              "6134   Hubei   China  30.9756  112.2707  ...     16678    479        522   15677\n",
              "6594   Hubei   China  30.9756  112.2707  ...     19665    549        633   18483\n",
              "7054   Hubei   China  30.9756  112.2707  ...     22112    618        817   20677\n",
              "7514   Hubei   China  30.9756  112.2707  ...     24953    699       1115   23139\n",
              "7974   Hubei   China  30.9756  112.2707  ...     27100    780       1439   24881\n",
              "8434   Hubei   China  30.9756  112.2707  ...     29631    871       1795   26965\n",
              "8894   Hubei   China  30.9756  112.2707  ...     31728    974       2222   28532\n",
              "9354   Hubei   China  30.9756  112.2707  ...     33366   1068       2639   29659\n",
              "9814   Hubei   China  30.9756  112.2707  ...     33366   1068       2686   29612\n",
              "10274  Hubei   China  30.9756  112.2707  ...     48206   1310       3459   43437\n",
              "10734  Hubei   China  30.9756  112.2707  ...     54406   1457       4774   48175\n",
              "11194  Hubei   China  30.9756  112.2707  ...     56249   1596       5623   49030\n",
              "11654  Hubei   China  30.9756  112.2707  ...     58182   1696       6639   49847\n",
              "12114  Hubei   China  30.9756  112.2707  ...     59989   1789       7862   50338\n",
              "12574  Hubei   China  30.9756  112.2707  ...     61682   1921       9128   50633\n",
              "13034  Hubei   China  30.9756  112.2707  ...     62031   2029      10337   49665\n",
              "13494  Hubei   China  30.9756  112.2707  ...     62442   2144      11788   48510\n",
              "13954  Hubei   China  30.9756  112.2707  ...     62662   2144      11881   48637\n",
              "14414  Hubei   China  30.9756  112.2707  ...     64084   2346      15299   46439\n",
              "14874  Hubei   China  30.9756  112.2707  ...     64084   2346      15343   46395\n",
              "15334  Hubei   China  30.9756  112.2707  ...     64287   2495      16748   45044\n",
              "15794  Hubei   China  30.9756  112.2707  ...     64786   2563      18971   43252\n",
              "16254  Hubei   China  30.9756  112.2707  ...     65187   2615      20969   41603\n",
              "16714  Hubei   China  30.9756  112.2707  ...     65596   2641      23383   39572\n",
              "17174  Hubei   China  30.9756  112.2707  ...     65914   2682      26403   36829\n",
              "17634  Hubei   China  30.9756  112.2707  ...     66337   2727      28993   34617\n",
              "18094  Hubei   China  30.9756  112.2707  ...     66907   2761      31536   32610\n",
              "18554  Hubei   China  30.9756  112.2707  ...     67103   2803      33934   30366\n",
              "19014  Hubei   China  30.9756  112.2707  ...     67217   2835      36208   28174\n",
              "19474  Hubei   China  30.9756  112.2707  ...     67332   2871      38557   25904\n",
              "19934  Hubei   China  30.9756  112.2707  ...     67466   2902      40592   23972\n",
              "20394  Hubei   China  30.9756  112.2707  ...     67592   2931      42033   22628\n",
              "20854  Hubei   China  30.9756  112.2707  ...     67666   2959      43500   21207\n",
              "21314  Hubei   China  30.9756  112.2707  ...     67707   2986      45235   19486\n",
              "21774  Hubei   China  30.9756  112.2707  ...     67743   3008      46488   18247\n",
              "22234  Hubei   China  30.9756  112.2707  ...     67760   3024      47743   16993\n",
              "22694  Hubei   China  30.9756  112.2707  ...     67773   3046      49134   15593\n",
              "23154  Hubei   China  30.9756  112.2707  ...     67781   3056      50318   14407\n",
              "23614  Hubei   China  30.9756  112.2707  ...     67786   3062      51553   13171\n",
              "24074  Hubei   China  30.9756  112.2707  ...     67790   3075      52960   11755\n",
              "24534  Hubei   China  30.9756  112.2707  ...     67794   3085      54288   10421\n",
              "24994  Hubei   China  30.9756  112.2707  ...     67798   3099      55142    9557\n",
              "25454  Hubei   China  30.9756  112.2707  ...     67799   3111      56003    8685\n",
              "\n",
              "[56 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NAotS0iinfL-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "max_df=combined_df[combined_df.Date==combined_df['Date'].max()]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lJrnZJAhrW4W",
        "colab_type": "text"
      },
      "source": [
        "Country by Max active cases"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aJfmL5kGPF6U",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        },
        "outputId": "cc5797f5-276e-4196-c1cf-171efb082b5c"
      },
      "source": [
        "max_df[['Country','Active']].sort_values('Active',ascending=False).head(10)"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Active</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>25316</th>\n",
              "      <td>Italy</td>\n",
              "      <td>26062</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25318</th>\n",
              "      <td>Spain</td>\n",
              "      <td>10187</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25455</th>\n",
              "      <td>Iran</td>\n",
              "      <td>9792</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25311</th>\n",
              "      <td>Germany</td>\n",
              "      <td>9166</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25454</th>\n",
              "      <td>China</td>\n",
              "      <td>8685</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25457</th>\n",
              "      <td>France</td>\n",
              "      <td>7492</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25456</th>\n",
              "      <td>Korea, South</td>\n",
              "      <td>6832</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25331</th>\n",
              "      <td>Switzerland</td>\n",
              "      <td>2669</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25703</th>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>1843</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25399</th>\n",
              "      <td>US</td>\n",
              "      <td>1693</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              Country  Active\n",
              "25316           Italy   26062\n",
              "25318           Spain   10187\n",
              "25455            Iran    9792\n",
              "25311         Germany    9166\n",
              "25454           China    8685\n",
              "25457          France    7492\n",
              "25456    Korea, South    6832\n",
              "25331     Switzerland    2669\n",
              "25703  United Kingdom    1843\n",
              "25399              US    1693"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cks_wvvQWZZe",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 390
        },
        "outputId": "ea5779a6-14bd-4dae-944b-642b2502c176"
      },
      "source": [
        "max_df[['Country','Confirmed']].groupby(['Country']).sum().sort_values('Confirmed',ascending=False).head(10)\n"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>China</th>\n",
              "      <td>81058</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Italy</th>\n",
              "      <td>31506</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Iran</th>\n",
              "      <td>16169</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Spain</th>\n",
              "      <td>11748</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Germany</th>\n",
              "      <td>9257</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Korea, South</th>\n",
              "      <td>8320</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>France</th>\n",
              "      <td>7699</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>US</th>\n",
              "      <td>6421</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Switzerland</th>\n",
              "      <td>2700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>United Kingdom</th>\n",
              "      <td>1960</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                Confirmed\n",
              "Country                  \n",
              "China               81058\n",
              "Italy               31506\n",
              "Iran                16169\n",
              "Spain               11748\n",
              "Germany              9257\n",
              "Korea, South         8320\n",
              "France               7699\n",
              "US                   6421\n",
              "Switzerland          2700\n",
              "United Kingdom       1960"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lCB-UuiJtuLz",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 462
        },
        "outputId": "fa2d2db8-7580-405a-f978-adebaa679b3d"
      },
      "source": [
        "combined_df[['Date','Country','State','Active']].query(\"Country=='China' & State=='Hubei'\").plot(x='Date',y='Active',kind='bar', title=\"Corona Trend for Country China and State Hubei\")"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fcd184076d8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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sscUWA99j991355ZbbuEnP/kJq1evZvPNN2fHHXfk2GOP5dxzz2X33XcHqrNO1157LX/w\nB38wpzLP5Z6gA4F9yu8nAxdRNYIOBE7JzAQujojNImLbMu95mXkbQEScByyJiIuATTLz4jL+FODF\n2AiSpAXFxowkLTy33XYbF1xwAVdffTURwf33309EsOeeew79XgcddBCnn346N998MwcffDBQPQPo\n6KOP5rWvfe1Iyz3be4ISODciLo+II8u4rTPzpvL7zcDW5fftgRt6sqvKuEHjV/UZL0mSJGmCnX76\n6bz61a/m+uuv57rrruOGG25g5513ZrfdduOf/umfuO+++4CqsQSw8cYbc9ddd/V9r4MPPpjly5dz\n+umnc9BBBwGw//77c9JJJ3H33XcDcOONN3LLLbfMudyzbQQ9KzOfRnWp2xsiYq3zT+WsT865NDOI\niCMj4rKIuGz16tXzvThJkiRJA5x66qm85CUvWWvcS1/6Um666SZ22mkndt11V3bbbTc+97nPAXDk\nkUeyZMmSBztG6PW7v/u73HXXXWy//fZsu+22ADzvec/jFa94Bc985jN5ylOewste9rJpG1HDiKr9\nMkQg4l3A3VT38OyTmTeVy90uyszfiYh/Kr+fWub/HtWlcPuU+V9bxv8T1SV0FwEXZuYTyviX9843\nnT322CMvu+yyocouSZq7upe1NZ2TpC645ppreOITnzjuYkyEfusiIi7PzD2mzjvjPUER8Uhgvcy8\nq/z+POA9wJnAYcCy8vOMEjkTOCoillN1jHBnaSidA3xgTS9y5X2OzszbIuLnEbE3VccIhwIfH7rW\nkiT1sPEkSZrObDpG2Br4t9Jr9SLgc5n55Yi4FDgtIo4Argf+pMy/AngBsBL4JfCnAKWx817g0jLf\ne9Z0kgC8Hvg08HCqDhHsFEGSJEnSvJixEZSZPwR26zP+VmC/PuMTeMM073UScFKf8ZcBT55FeSVJ\nkiRpTubSRbYkqcW8XEySFobMpFy11VnD9nMw297hJEmSJE2YjTbaiFtvvXXoRsBCkpnceuutbLTR\nRrPOeCZIkqQR8MyapHHYYYcdWLVqFV1/fMxGG23EDjvsMOv5bQRJktTDxoykNtlggw3Yeeedx12M\n1vFyOEmSJEmd4pkgSWo5z1xIkjQczwRJkiRJ6hQbQZIkSZI6xUaQJEmSpE6xESRJkiSpU2wESZIk\nSeoUG0GSJEmSOsVGkCRJkqRO8TlBkiSN0XTPefIZT5I0f2wESZLUMj4gV5LmxsvhJEmSJHWKjSBJ\nkiRJnWIjSJIkSVKn2AiSJEmS1Ck2giRJkiR1io0gSZIkSZ1iF9mSJHWEXWtLUsUzQZIkSZI6xUaQ\nJEmSpE6xESRJkiSpU2wESZIkSeoUG0GSJEmSOsVGkCRJkqROsREkSZIkqVN8TpAkTQif4SJJUjNs\nBEmSpIFsoEtaaLwcTpIkSVKn2AiSJEmS1Ck2giRJkiR1io0gSZIkSZ1iI0iSJElSp9gIkiRJktQp\nNoIkSZIkdYrPCZIkSfPC5wtJmlSeCZIkSZLUKTaCJEmSJHWKjSBJkiRJnWIjSJIkSVKnzLoRFBHr\nR8Q3IuLfy+udI+LrEbEyIj4fERuW8Q8rr1eW6Yt73uPoMv57EbF/z/glZdzKiFg6uupJkiRJ0tqG\nORP0JuCantcfBD6amY8DbgeOKOOPAG4v4z9a5iMingQcAvwusAQ4vjSs1geOA54PPAl4eZlXkiRJ\nkkZuVo2giNgBOAD4ZHkdwB8Cp5dZTgZeXH4/sLymTN+vzH8gsDwzf52ZPwJWAs8ow8rM/GFm/gZY\nXuaVJEmSpJGb7ZmgY4C3AQ+U11sCd2TmfeX1KmD78vv2wA0AZfqdZf4Hx0/JTDd+HRFxZERcFhGX\nrV69epZFlyRJkqSHzNgIiog/Am7JzMsbKM9AmXlCZu6RmXtstdVW4y6OJEmSpBZaNIt5fg94UUS8\nANgI2AQ4FtgsIhaVsz07ADeW+W8EdgRWRcQiYFPg1p7xa/RmphsvSZI6ZvHSs6addt2yAxosiaSF\nasYzQZl5dGbukJmLqTo2uCAzXwlcCLyszHYYcEb5/czymjL9gszMMv6Q0nvczsAuwCXApcAupbe5\nDcsyzhxJ7SRJkiRpitmcCZrO24HlEfE+4BvAiWX8icBnImIlcBtVo4bM/HZEnAZ8B7gPeENm3g8Q\nEUcB5wDrAydl5rfnUC5JkiRJmtZQjaDMvAi4qPz+Q6qe3abOcw9w0DT59wPv7zN+BbBimLJIkiRJ\nUh3DPCdIkiRJklrPRpAkSZKkTrERJEmSJKlTbARJkiRJ6hQbQZIkSZI6xUaQJEmSpE6xESRJkiSp\nU2wESZIkSeoUG0GSJEmSOsVGkCRJkqROsREkSZIkqVMWjbsAkiRJo7B46VnTTrtu2QENlkTSpPNM\nkCRJkqROsREkSZIkqVNsBEmSJEnqFBtBkiRJkjrFRpAkSZKkTrERJEmSJKlTbARJkiRJ6hQbQZIk\nSZI6xUaQJEmSpE6xESRJkiSpUxaNuwCSJEnjtHjpWdNOu27ZAQ2WRFJTPBMkSZIkqVNsBEmSJEnq\nFBtBkiRJkjrFRpAkSZKkTrERJEmSJKlTbARJkiRJ6hQbQZIkSZI6xUaQJEmSpE6xESRJkiSpUxaN\nuwCSJElttHjpWdNOu27ZAQ2WRNKwPBMkSZIkqVNsBEmSJEnqFBtBkiRJkjrFRpAkSZKkTrERJEmS\nJKlTbARJkiRJ6hQbQZIkSZI6xUaQJEmSpE6xESRJkiSpU2ZsBEXERhFxSUR8MyK+HRHvLuN3joiv\nR8TKiPh8RGxYxj+svF5Zpi/uea+jy/jvRcT+PeOXlHErI2Lp6KspSZIkSZXZnAn6NfCHmbkb8FRg\nSUTsDXwQ+GhmPg64HTiizH8EcHsZ/9EyHxHxJOAQ4HeBJcDxEbF+RKwPHAc8H3gS8PIyryRJkiSN\n3KKZZsjMBO4uLzcoQwJ/CLyijD8ZeBfwCeDA8jvA6cA/RESU8csz89fAjyJiJfCMMt/KzPwhQEQs\nL/N+Zy4VkyRJmkSLl5417bTrlh3QYEmk7prVPUHljM2VwC3AecAPgDsy874yyypg+/L79sANAGX6\nncCWveOnZKYb368cR0bEZRFx2erVq2dTdEmSJElay6waQZl5f2Y+FdiB6uzNE+a1VNOX44TM3CMz\n99hqq63GUQRJkiRJLTfj5XC9MvOOiLgQeCawWUQsKmd7dgBuLLPdCOwIrIqIRcCmwK0949fozUw3\nXpLGpu4lK17qIknSZJtN73BbRcRm5feHA88FrgEuBF5WZjsMOKP8fmZ5TZl+Qbmv6EzgkNJ73M7A\nLsAlwKXALqW3uQ2pOk84cxSVkyRJkqSpZnMmaFvg5NKL23rAaZn57xHxHWB5RLwP+AZwYpn/ROAz\npeOD26gaNWTmtyPiNKoOD+4D3pCZ9wNExFHAOcD6wEmZ+e2R1VCSJEmSesymd7irgN37jP8hD/Xu\n1jv+HuCgad7r/cD7+4xfAayYRXklSZIkaU5m1TGCJEmSJC0UNoIkSZIkdcpQvcNJUhvZW5skSerl\nmSBJkiRJneKZIEmSpBbwrLY0Op4JkiRJktQpNoIkSZIkdYqNIEmSJEmdYiNIkiRJUqfYCJIkSZLU\nKfYOJ6k17BlJkiSNgmeCJEmSJHWKjSBJkiRJnWIjSJIkSVKn2AiSJEmS1Cl2jCBJkrSA2amMtC7P\nBEmSJEnqFBtBkiRJkjrFRpAkSZKkTrERJEmSJKlTbARJkiRJ6hR7h5PUOHsqkiRJ4+SZIEmSJEmd\nYiNIkiRJUqfYCJIkSZLUKd4TJEmSpHV4/6YWMs8ESZIkSeoUG0GSJEmSOsVGkCRJkqROsREkSZIk\nqVPsGEFSbd40K0mS2sgzQZIkSZI6xTNBkiRJGhmvElAbeCZIkiRJUqfYCJIkSZLUKTaCJEmSJHWK\njSBJkiRJnWIjSJIkSVKn2AiSJEmS1Cl2kS1JkqSxs2ttNckzQZIkSZI6xUaQJEmSpE6Z8XK4iNgR\nOAXYGkjghMw8NiK2AD4PLAauA/4kM2+PiACOBV4A/BI4PDOvKO91GPCO8tbvy8yTy/inA58GHg6s\nAN6UmTmiOkqagZcgSJKkLpnNmaD7gL/MzCcBewNviIgnAUuB8zNzF+D88hrg+cAuZTgS+ARAaTS9\nE9gLeAbwzojYvGQ+AfzvntySuVdNkiRJktY1YyMoM29acyYnM+8CrgG2Bw4ETi6znQy8uPx+IHBK\nVi4GNouIbYH9gfMy87bMvB04D1hSpm2SmReXsz+n9LyXJEmSJI3UUPcERcRiYHfg68DWmXlTmXQz\n1eVyUDWQbuiJrSrjBo1f1We8JEmSJI3crBtBEfEo4F+AN2fmz3unlTM4834PT0QcGRGXRcRlq1ev\nnu/FSZIkSVqAZtUIiogNqBpA/5yZ/1pG/7Rcykb5eUsZfyOwY098hzJu0Pgd+oxfR2aekJl7ZOYe\nW2211WyKLkmSJElrmU3vcAGcCFyTmR/pmXQmcBiwrPw8o2f8URGxnKoThDsz86aIOAf4QE9nCM8D\njs7M2yLi5xGxN9VldocCHx9B3SRJkrTA2cOp6pixEQT8HvBq4OqIuLKM+2uqxs9pEXEEcD3wJ2Xa\nCqrusVdSdZH9pwClsfNe4NIy33sy87by++t5qIvss8sgSZIkSSM3YyMoM78KxDST9+szfwJvmOa9\nTgJO6jP+MuDJM5VFkiRJkuZqqN7hJEmSJKntbARJkiRJ6pTZ3BMkqSW8OVSSJGlmngmSJEmS1Cme\nCZIkSVLnePVEt3kmSJIkSVKn2AiSJEmS1Ck2giRJkiR1io0gSZIkSZ1iI0iSJElSp9g7nCRJkjRL\n9iq3MHgmSJIkSVK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B7h6QWx0Rr6La+P6Y6tQpEREMvufs+oh4G3ByZv60ZLam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\nJEmSNCl/AxvPkiorRZ7mAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 1008x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5BW_bjno92G5",
        "colab_type": "text"
      },
      "source": [
        "On 23 January 2020, the central government of China imposed a lockdown in Wuhan and other cities in Hubei province\n",
        "\n",
        "On 2 February 2020, China implemented a seven-day lockdown in which only one person per household was allowed to exit once each two days, and most of the highway exits were closed\n",
        "\n",
        "On 13 March 2020, Huangshi and Qianjiang became the first Hubei cities to remove strict travel restrictions within part or all of their administrative confines"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2IFKsTpkxCcr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 462
        },
        "outputId": "1fc03fd9-339a-45ae-b191-5d647bc1e127"
      },
      "source": [
        "combined_df[['Date','Country','State','Active']].groupby(['Date','Country']).sum().query(\"Country=='China'\").reset_index().plot(x='Date',y='Active',kind='bar',title=\"Corona Trend for Country China\")"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fcd183adac8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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JkqR5ouk/Yk1syAC8613vevr1qaeeus60E044gRNOOOHp93fcccc602+55Zb1Pu/EE098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\nRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1008x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M9fgfjCTBrEn",
        "colab_type": "text"
      },
      "source": [
        "South Korea did not lockdown\n",
        "\n",
        "South Korea tests anyone who had been in contact with a confirmed case, and tracks down by credit card activity, surveillance camera footage and mobile phone tracking those who are potentially exposed, a measure that has proved effective but has raised questions about privacy\n",
        "\n",
        "South Korea had conducted almost 250,000 tests\n",
        "\n",
        "Source - Forbes"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BCUtcnnS0h5i",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 462
        },
        "outputId": "6f7427ea-0560-4dd5-90cf-724a8e34b550"
      },
      "source": [
        "combined_df[['Date','Country','State','Active']].query(\"Country=='Korea, South'\").plot(x='Date',y='Active',kind='bar',title=\"Corona Trend for Country South Korea\")"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fcd1805cf98>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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bM/MO4CJg5VBrI0mSJEnFnK7ZiYjlwD7A14CdM/OmMutmYOdyfzfgxp7YujJt\nuumTX+P4iLg8Ii7fsGHDXIonSZIkSQ+adWcnIh4JfAo4MTN/2jsvMxPIYRQoMz+amSsyc8XSpUuH\n8ZSSJEmSFqFZdXYiYguqjs4/ZeY/l8m3lNPTKH9vLdPXA7v3xJeVadNNlyRJkqShm81obAGcClyT\nme/tmXUuMDGi2jHAOT3Tjy6jsh0A/KSc7nYBcHBEbF8GJji4TJMkSZKkoVsyi2V+G3g58M2IuKpM\n+9/AauCsiDgOuAF4cZl3PvBc4DrgbuAVAJm5MSLeDlxWlntbZm4cSi0kSZIkaZIZOzuZ+RUgppl9\nUJ/lEzhhmuc6DThtLgWUJEmSpDrmNBqbJEmSJLWFnR1JkiRJnWRnR5IkSVIn2dmRJEmS1EmzGY1N\nkiRp6JavOm/aeWtXH9pgSSR1lUd2JEmSJHWSnR1JkiRJnWRnR5IkSVIn2dmRJEmS1El2diRJkiR1\nkp0dSZIkSZ1kZ0eSJElSJ9nZkSRJktRJdnYkSZIkdZKdHUmSJEmdZGdHkiRJUifZ2ZEkSZLUSXZ2\nJEmSJHWSnR1JkiRJnWRnR5IkSVIn2dmRJEmS1ElLRl0ASZLUbstXnTftvLWrD22wJJK0KY/sSJIk\nSeokOzuSJEmSOsnOjiRJkqROmrGzExGnRcStEfGtnmk7RMRFEXFt+bt9mR4R8YGIuC4iro6Ip/Vk\njinLXxsRxyxMdSRJkiSpMpsjO38PrJw0bRVwcWbuBVxcHgM8B9ir3I4HPgxV5wg4Cdgf2A84aaKD\nJEmSJEkLYcbOTmZ+Gdg4afLhwOnl/unAET3Tz8jKJcB2EbErcAhwUWZuzMw7gIuY2oGSJEmSpKGp\ne83Ozpl5U7l/M7Bzub8bcGPPcuvKtOmmS5IkSdKCmPcABZmZQA6hLABExPERcXlEXL5hw4ZhPa0k\nSZKkRaZuZ+eWcnoa5e+tZfp6YPee5ZaVadNNnyIzP5qZKzJzxdKlS2sWT5IkSdJit6Rm7lzgGGB1\n+XtOz/RXR8QaqsEIfpKZN0XEBcBf9gxKcDDwpvrFliRJw7Z81XnTzlu7+tAGSyJJwzFjZycizgSe\nBewUEeuoRlVbDZwVEccBNwAvLoufDzwXuA64G3gFQGZujIi3A5eV5d6WmZMHPZAkSZKkoZmxs5OZ\nL5lm1kF9lk3ghGme5zTgtDmVTpIkSZJqmvcABZIkSZI0jupesyNJksaU195IUsUjO5IkSZI6yc6O\nJEmSpE6ysyNJkiSpk+zsSJIkSeokOzuSJEmSOsnOjiRJkqROsrMjSZIkqZP8nR1JksaUv5cjSfPj\nkR1JkiRJnWRnR5IkSVIn2YFhm0MAACAASURBVNmRJEmS1El2diRJkiR1kp0dSZIkSZ3kaGySJC0w\nR1WTpNHwyI4kSZKkTrKzI0mSJKmT7OxIkiRJ6iQ7O5IkSZI6yc6OJEmSpE6ysyNJkiSpkxx6WpKk\nWXIIaUlqF4/sSJIkSeokOzuSJEmSOqnxzk5ErIyI70XEdRGxqunXlyRJkrQ4NHrNTkRsDnwIeDaw\nDrgsIs7NzO80WQ5J0uLmtTeStDg0fWRnP+C6zPxhZv4KWAMc3nAZJEmSJC0CTY/GthtwY8/jdcD+\nDZdBLVL3v6/mzJkbv9w4lVGStDhEZjb3YhEvBFZm5n8vj18O7J+Zr+5Z5njg+PLw8cD3pnm6nYDb\nahTDnDlzo8m1oYzmzJlrX64NZTRnztzC534tM5dOmZqZjd2ApwMX9Dx+E/Cmms91uTlz5tqTa0MZ\nzZkz175cG8pozpy50eWavmbnMmCviNgzIrYEjgLObbgMkiRJkhaBRq/Zycz7IuLVwAXA5sBpmfnt\nJssgSZIkaXFoeoACMvN84PwhPNVHzZkz16pcG8pozpy59uXaUEZz5syNKNfoAAWSJEmS1JSmr9mR\nJEmSpEbY2ZEkSZLUSY1fs1NHRASwH9WPkgKsBy7NWZyDFxE79+Yy85ZZvuacc3XL2XSuZBurX09+\nB4DM3Dib5euWcxS5kp1z/ermGl6vG22TLn9m65azbs5t0ni8nuvL0HOt2H/NJ9e0puvXdLt0df88\nn3LWzbXpe9mDzzHu1+xExMHAKcC1VBsngGXAY4FXZeaF0+SeCvwtsO2k3J0ld+WQc3XL2XSu6frt\nAbwbOKi8RgCPAj4PrMrMtUMuZ9O5uvUb+3YZQZt0/TPb9fq1ZZvUlvZ0fRl93Zrevm9L9fuDRwCP\nBhK4FTgHWJ2Zdw4513T9mm6Xzu6fF0n9auX6yho/6NPkDbgGWN5n+p7ANQNyVwH795l+APCNBcjV\nLWfTuabr91XgSGDznmmbU/3G0iVj9P7VzdWt39i3ywjapOuf2a7Xry3bpLa0p+vL6OvW9Pb9AuCN\nwC4903Yp0y5cgFzT9Wu6XTq7f14k9auV6/tcc1l4FDeq/8Qs6TN9S+C6QbkB8xYkV7ecTeeart8C\nzBur932M5g21fmPWJmO1TrtNGo92acvrub4MJzdm68pCzPtew/PGaf+1EO0yTnXw+0dDuX63Nlyz\ncxpwWUSsAW4s03an6oGeOiD32Yg4DzhjUu5o4HMLkKtbzqZzTdfviog4BTh9Uu4Y4OsLUM6mc3Xr\n14Z2abpNuv6Z7Xr92rJNakt7ur5M1Zb9V93cDRHxBuD0LNcllOsVju15nmHmmq5f0+3S5f3zfMrZ\nlvrVzU0x9tfsAETEk4DD2PTCwnMz8zsz5J4DHN4nN/BHTeeRq1vOpnON1S8itgSO6/d6wKmZ+csF\nKGdjubr1a0u7NNkmTdet5Nry2WtL/cZ+mzSi13N9GVKuDfuveeS2B1aV3M5l8s0l966c5iLyeeSa\nrl/T7dL1/XOn6zef3JTnaUNnZ0I4qslQc3W15X2QZqstn7263CaNx+u5vgw3V0db3gNJQzSXc95G\ncQP2ANZQjbxxLXBdub+GPhcc9uS2BVZTXZi4Ebi93F8NbLcAubrlbDrXdP2WAK8EPgtcXW6fBf4Y\n2GKM3r+6ubr1G/t2GUGbdP0z2/X6tWWb1Jb2dH0Zfd0a3b6X7CHAh6n+y35uub9yUKZurun6Nd0u\nI6if3z/GINf3ueay8ChuDH/0jlXUG9VkplzbRzVZqPqdSbVROoBqyMBl5f6HgU+M0ftXN1e3fmPf\nLiNok65/Zrtev7Zsk9rSnq4vo69b09v3k4HzS7meUW5HlWnvX4Bc0/Vrul06u39eJPWrlev7XHNZ\neBQ3HNVkXNql7ut9v+a8pt+/uvPq1m/s22UEbdL1z2zX69eWbVJb2tP1ZfR1a3r73nce1e+gzLmc\ndXNtmTeG9fP7xxjM63fbjPF3RUScEhH7R8Rjym3/MpLEjKN3lBE7gGr0joh4I7MY1aRGrm45m841\nXb+NEfGiiHhwXYuIzSLiSOCOBShn07m69WtDuzTdJl3/zHa9fm3ZJrWlPV1fRl+3prfv90TEvn2m\n7wvcswC5puvXdLt0ef+8GOpXNzfVXHpGo7hRjYP/J1TDzH2z3D4LvArYakBue+BdwHep3ryNVOf6\nvQvYYQFydcvZdK7p+i0HPgFsAL5fbreWaXuO0ftXN1e3fmPfLiNok65/Zrtev7Zsk9rSnq4vo6/b\ncprdvj8N+BrwHeDCcrsGuAT4rQXINV2/ptul6XL6/WMMcv1urRqNTe0WETsCZObtoy7LQqhbvy63\nS5frJmnxaHL7HhG70DPUbmbevJC5uuVsOtem+jWt6/WbrzacxvagiPj9QY8H5J426PEC5OqWs+lc\no/WjGuXjwQ9U2XDN5vWafv9q5ahZv7q5JuvXdJssgs9s1+vXim1Si9rT9WVqphX7rzq5zLw5M68o\nt1l/oa+bq1vOpnNtqp/fP8YjN6FVnR2q8zMHPZ7On8zweNi5uuVsOtd0/Sb/UvWgX67u1fT7VzdX\nt35taJem26Trn9mu168t26S2tKfry1Rt2X/VykXElYMeDzvXp1wLvf9qtF3qvt48cn7/GI8cgKex\nSZIkSeqmJaMuwGxExBOAw+k5VxM4NzOvmSG3LbByUu6CzLxzgXJ1y9l0run6BbDfpNylOUNPewTv\nX91c3fqNfbuMoE26/pntev3ask1qS3u6vkzNtGX/VStXsjv35jLzlpkydXMj2H812i5d3j/Ps5xt\nqV+t3GRjfxpbVEPMrQECuLTcAjgzIlYNyB0NXAk8C3h4uR1INQTl0QuQq1vOpnNN1+9gql+sfgvw\n3HJ7K3BtmTfscjadq1u/sW+XEbRJ1z+zXa9fW7ZJbWlP15fR163p7ftTI+IS4IvAu8vtSxFxSQy4\nRmEeuabr13S7dHb/vEjqVyvX11yGbhvFjWpIvC36TN+SGX6QDdiuz/TtmeFHk2rm6paz6VzT9bsG\nWN5n+p7ANWP0/tXN1a3f2LfLCNqk65/ZrtevLduktrSn68vo69b09v0qYP8+0w8AvrEAuabr13S7\ndHb/vEjqVyvX7zb2R3aAB4DH9Jm+a5k3nQD6HY57oMwbdq5uOZvONV2/JcC6PtPXA1sMyDX9/tXN\n1a1fG9ql6Tbp+me26/VryzapLe3p+jJVW/ZfdXOPyMyvTZ6YmZcAj1iAXNP1a7pdurx/hu7Xr25u\nijZcs3MicHFEXMtDv5i6B/BY4NUDcu8EroyICyflng28fQFydcvZdK7p+p0GXBYRa3pyuwNHMXjU\nj6bfv7q5uvVrQ7s03SZd/8x2vX5t2Sa1pT1dX6Zqy/6rbu6zEXEecMak3NFUP4g67FzT9Wu6Xbq8\nf55POdtSv7q5KVoxGltEbMbUC6kuy8z7Z8htDxzC1Aub7ligXN1yNp1run5PAg5j6oWh31mgcjad\nq1u/sW+XEbRJ1z+zXa9fW7ZJbWlP15epmbbsv+rmnkP/gRTOX6Bc0/Vrul06u3+eZznbUr9auSnP\n05LOTuCoJkPLlWxj9evJ7wCQmRtns3zdco4iV7Jzrl/dXMPrdaNt0uXPbN1y1s25TRqP13N9GXqu\nFfuv+eSa1nT9mm6Xru6f51POurk2fS978DnGvbMT1cgQp1CNHLG+TF5Gdfj5VZl54TS5pwJ/C2xL\ndW5ilNydJdf3h6jmkatbzqZzTddvD6qRU34X+El5vUcBnwdWZebaIZez6Vzd+o19u4ygTbr+me16\n/dqyTWpLe7q+jL5uTW/ftwXeRHUEY2eq6xVuBc4BVuc0w+3OI9d0/Zpul87unxdJ/Wrl+so5jGYw\nihuOajIu7VL39b4KHAls3jNtc6pzQy8Zo/evbq5u/ca+XUbQJl3/zHa9fm3ZJrWlPV1fRl+3prfv\nFwBvBHbpmbYLsAq4cAFyTdev6Xbp7P55kdSvVq7vc81l4VHcqP4Ts6TP9C2B6wblBsxbkFzdcjad\na7p+CzBvrN73MZo31PqNWZuM1TrtNmk82qUtr+f6MpzcmK0rCzHvew3PG6f910K0yzjVwe8fDeX6\n3dowGpujmgw313T9roiIU4DTJ+WOAb6+AOVsOle3fm1ol6bbpOuf2a7Xry3bpLa0p+vLVG3Zf9XN\n3RARbwBOz3JdQrle4die5xlmrun6Nd0uXd4/z6ecbalf3dwUY3/NDkBEPJH+o3CM26gmdcvZdK6x\n+kXElsBxk3LrgH8BTs3MXy5AORvL1a1fW9qlyTZpum4l15bPXlvqN/bbpBG9nuvLkHJt2H/NI7c9\n1alZvdem3AKcC7wrp7mIfB65puvXdLt0ff/c6frNJzfledrQ2ZEkSZKkudps1AWYi4h4y6DHA3LH\nD3q8ALm3DHo8Rrmm6/f7gx4PyDX9/tXN1a3f2LfLCNqk65/Zrtevbq7p+rWlPV1fpmbasv+qm3va\noMcLkGu6fk23S2f3z/3K1cH61cpNaFVnB7hihsfTiRkeDztXt5xN55qu374zPJ5O0+9f3Vzd+rWh\nXZpuk65/Zrtev7Zsk9rSnq4vU7Vl/1U39yczPB52run6Nd0uXd4/Q/frVzdXLexpbJIkSZK6aOxH\nY4uIJVQXUj0feEyZvJ7qx6ROzcx7B2QPAY5g0wubzsnMgaM41MnVLWfTuabrV7JPoP8FZtdMl6lb\nzhHl6tZv7NtlBG3S2c9s1+vXsm3S2Ldn3XK2pX7zyLVl/1U3ty2wclLugpzmhzOHkGu6fk23S2f3\nz/MsZ1vqVys35XnG/chORJxJ9Wupp1ONFgHVL6geA+yQmUdOkzsZeBzVkHW9uaOpxu5+3ZBzdcvZ\ndK7p+r0ReAmwZlLuKGBNZq4ecjmbztWt39i3ywjapOuf2a7Xry3bpLa0p+vL6OvW9Pb9aOAk4EKq\nL3UTuWcDb83MM4aca7p+TbdLZ/fPi6R+tXJ95Rx+lGcUN+D7w5xHdZ7foB8qGmquLfMWsn7AFn2m\nb9nk+7CQubr1G/d2GUWbjMN73vS8rtev6XZpy+u5vtSqe2v2XzVz3wO26zN9+xnKWTfX+P6r4Xbp\n7P55sdSvTq7frQ0DFGyMiBdFxINljYjNIuJI4I4BuXsiot+FVvsC9yxArm45m841Xb8HeOi0gV67\nlnnDLmfTubr1a0O7NN0mXf/Mdr1+bdkmtaU9XV+masv+q24uqH5Dpt/zDbogu26u6fo13S5d3j9D\n9+tXNzfF2F+zQ3VY7V3AKRFxB9WKvR3w+TJvOscCH46IbXjo8NfuwE/KvGHn6paz6dyxNFu/E4GL\nI+JaHvoF3D2AxwKvXoByNp2rW782tEvd12pD3aA9n726ObdJ4/F6ri/DyzVdt6a37+8EroyICyfl\nng28fQFyTdev6Xbp8v55PuVsS/3q5qYY+2t2ekXEjgCZefscMrvQc2FTZt68kLm65Ww612T9yn/T\n9mPTC8wuy8z7F6qcTebq1q8t7dJkm9R9vfnkSnbsP3ttqV/dXNP1a0t7ur70XX7s91/zyG0PHDIp\nd0FmDjoCNZ9c0/Vrul26vn/udP3mk+vVhiM7xKRRIyJiYjSG786Q2xZ4Jj2NFBGzHdWkTq5uOZvO\nNVo/qsPPE7eJx4MOlc63nI3mqFm/urkm69d0myyCz2zX69eKbVKL2tP1ZcR1o+Hte2beERFfoKd+\nM32hn0+ubjmbzrWlfn7/GI/cZGN/zU5Uo0asoTrsfGm5BbAmIlYNyB0NXAk8C3h4uR0IXFHmDTtX\nt5xN55qu38HAtcBbgOeW21uBa8u8YZez6Vzd+o19u4ygTbr+me16/dqyTWpLe7q+jL5uTW/fnxoR\nlwBfpDrt7t3AlyLikoh42gLkmq5f0+3S2f3zIqlfrVxfcxnNYBQ3HNVkXNql7utdAyzvM31P4Jox\nev/q5urWb+zbZQRt0vXPbNfr15ZtUlva0/Vl9HVrevt+FbB/n+kHAN9YgFzT9Wu6XTq7f14k9auV\n63cb+yM7OKrJsHNN128JD11Y1ms9sMWAXNPvX91c3fq1oV2abpOuf2a7Xr+2bJPa0p6uL1O1Zf9V\nN/eIzPza5ImZeQnwiAXINV2/ptuly/tn6H796uamaMM1O45qMtxc0/U7DbgsItb05HanGgHn1AUo\nZ9O5uvVrQ7s03SZd/8x2vX5t2Sa1pT1dX6Zqy/6rbu6zEXEe1Y8o9uaOBj63ALmm69d0u3R5/zyf\ncralfnVzU7RiNLZwVJNh55qu3xPpuTC05M7NzO8sUDmbztWt39i3ywjapOuf2a7Xry3bpLa0p+vL\n1Exb9l91c8+ZJnf+AuWarl/T7dLZ/fM8y9mW+tXKTXmeNnR2JEmSJGmu2nDNjiRJkiTNmZ0dSZIk\nSZ1kZ2cWYsA47gvwWo+KiN8q5yk29Zo7zXH57SPiUQtVnsUqIh7d8Ovt2OTrNSEidoiIHUZdjoXm\nNmnK8rW2Sa4vC/Jaja4v7r+k+ev894+5jFM9ihvVCBFrgH8H/jc9Y+UDnxmQewLwWeA84NeBvwfu\npPoxsScOyD1t0u23qIbo2wd42oDcH/XcXwZcXF7vP4HHDcj9I7BTuX8I8CPg34AbgBcNyG0E/g44\niHLt1Szb8znA9cBXSp2+Dfyg1PGgAbnHUI2E8hPg/lLOH1H9KNWU3y/oyW0LrAa+W8p8O9UY76vp\nM376LOvw2WHngEcBfwX8A/DSSfNOGZDbBfgw8CFgx9Ie3wTOAnYdkNth0m1HYC3V+PE7DMitnNS2\npwJXAx8Hdh6QW92znq0AfghcV9azZ06TuRJ4M/Drc2znFcAXyrq9O3BRWW8uA/YZkHsk8LayTv4E\n2ABcAhw7w+vtQbWN2ED1Q2nXAbeWactrrivfHDDPbVL/XFu2Sa4vLV1fml5XZijLRwfM2xx4JdWI\nUb89ad6bB+QeDrwB+F/A1sCxwLlUP6L5yDmWb8bfIQGe0nN/C6rt/bnAXwIPH5B7dc97/ljgy2Vd\n+Rrw5AG5fwZeVqMu/4VqBLF3UO0n/h/wLeCTgz6zVP/Q/6PyGfoG1T5tDfCsGV6v0e8t+P1julyt\n7yB9n2u+T7DQN6ovSn8MPBX4INWGd8cy7+sDcl8Gnge8pDToUVTjcj8PuHhA7oHyGl/ouf2i/P38\noDel5/5ZwPHlg/b8GV7vmz33/3PigwvsxOAfy/oe1QbnP6hGp3g/cMAs2vMq4InA08sH+IAy/Ym9\ndeiT+/zEBgL4A+B9VOPbv4PBG/0LgDcCu/RM26VMu3BAbvIOu3fHfdMC5D5VPpBHUG3sPwVsNfm9\n7ZP7HPAaYFX50L+R6svNa4BzZljPrp90u7f8/eEs17O/K+3/a8D/ZPAXp9717AvAvuX+44DLp8lc\nD7yH6kvBpeU1HjOLdexSqi8lL6EaLvKFZfpBwFcH5M6h2rkvA/4U+HNgL+B04C8H5L4KHAls3jNt\nc6rP/CUDcn8wze0FwIYBObdJ/XNt2Sa5vrR0fRnBujL5S2Hvl8N1A3J/R/UF8ETgCuC9/dq5T+4s\n4G+AU6g6m/8X+G/AXwP/MCB3F/DTcrur3O6fmD7L9/xvqDrUzyztc8aA3Ld77p8HPL/cfxbwHwNy\n64GzqToQZ5V1a8tB73nPZ+FPqPaz3wJeT7WfPW6GdfpjVB2AZwAnU/0z7dlUnfHXDMg1/b3F7x/9\nc7W+g/R9rjqhJm/AVZMev4zqvzm/PsNK8PWe+9dN94b1yb0A+BLwnN4Gn0U5e1eCyWUetEP7NvCo\ncv8rwGa982b5entQ/TfoSqoe86Avhr25Gwe19aR535j0+Iqe+98dkPtezXn3U+2gvtDn9osFyE1+\nz/4P1U54xzmsZz+aQ3u+nmpD9eSeafNdzwa93jXAknL/kknz+v5XetJr/TeqHfDNpS2Pr9kmgz4L\nk9exy8rfzWZYxwb9AvqgefdS7dw/1ud21xzWFbdJU19vnLdJri8tXV9GsK7cX8pzfc9t4vGvBuSu\n7rm/BPgo1VGNrWZoy6vK36Da1kbP46sH5D5AdeRq555ps3nPe9exqyhHuWbxet/ruX/ZdHWf7vWo\njmS8HDif6gjrx4CDZ1nOuexTrp70+JLydyvgmtnUb47z/P7RPzfn7x99Xm/W30H63drwo6JbRMTW\nmXkPQGb+Y0TcTNXzHvTLuZv33H/vpHlbThfKzE9FxAXA2yPij6hWipxFOZdFxAeoNhJLI2KLzLx3\nog4Dcm8FvhARH6JauT8ZEecCBzL4x7Ie/PXYzPwR1WHud0fEE6j+azmdOyPilVQbmzsi4n9S/Yfl\n94CfDchtiIiXUa1kf0B1yJOICAZf+3VDRLwBOD0zbymZnan+g3/jgNw1wCsz89rJMyJiIXJbRcRm\nmfkAQGa+MyLWU/1H6ZEDcr11P2PAvE1k5t9ExCeA95VyncTs1rNHR8SfUr3/j4qIyLIlGPR6VBuJ\n8yNiNfC5iHg/1c73d6l2cgNl5r8D/x4Rr6H6z9iRVDvwfu6JiIOpDnNnRByRmZ+JiGdS7Qym8/OI\neEZmfiUiDqP67x+Z+UBZz6ZzRUScQnUEqPcH0o4Bvj4gdzXwnsz81uQZEfF7A3Juk/pryzbJ9aW/\nNqwvTa8rP6Q6Pe5HUwo/eH/y4PuTmfcBx0fEX1B9ER60P5nIZEScP7FtL4+nfQ8z87UR8VvAmRHx\nGaojQrN5z7eNiOdTtcFWE+/3TK8HnB0Rf091pOTTEXEi8Gmq/cmUtuotann+n1KdsvUP5dqNF1Ed\nnbhwmtwDEfE4qn3KwyNiRWZeHhGPZdPPyWT3RsSvZ+YPynVrvyqv/8sZ6tf09xa/f8xgjt9B+j7B\nWN+oDls9s8/0fYCLBuReSZ/zQqnOLz15lq+9D9XG8dZZLHvMpNv2ZfouDPivZk+Z3kW1sfgXqnMw\nD5kh897Z1KFPbnfgI+U1dint+y2qQ9GDzgPfg2qn8i2qc7R3LdN3BF4wILd9qdvEua8bqTYI72Lw\nuaEvBB4/zbwjFiD3buD3+kxfyeD/9r5twHp29izfk8Oprk25eRbLnjTptrRnPZv2tIOyzIHAJ6i+\n0H2T6nqAVzLNOevAmprr2N5UX+Q+S3Xdwfupzuf+NvBfB+SeQnWo+k6q/xA/vkxfCrx2QG5LqlMc\nPlfqNVG3V1FOBZgm99+APaaZt2JAzm1S/0xbtkmuLy1dX0awrpwA7D3NvEGnQf0jPdc39Ez/78C9\nA3J/N817/uvAV2bRPpsBr6W6PuzHs1j+Y5NuO/e859OeuliWOZbqGp3bqE6X+w7VtT7bDsh8ea7v\neckdRHXa4zVUp6R9ioeutTt8QG6i83Ud1dG4/cv0pcC7B+Sa/t4yyu8fh9HM949nsen3j/OpTpUd\ndH1lre8g/W7+qOgMyn9+tsnqPxHSgoiIh1FdhDflv8ZSL7dJmgvXl26Y9F/0mZbdlWowmPMXuFgj\nU0bhuyMzB50tMLH+75iZtzVTsvZZDN8/WtHZiYhDKKPDZObanul/lJmndSj3b5l5Q5fKWTY0L6I6\nRHo21X9aDqf6j8nfZjlsO6xcTzmPAHYrk9ZTXaw36JQKcwv/Wp/JzAuGXcYZnu8vMvNt5sxNmt+2\nbW5bcgveniMo4xOo9j2926RzM/Oa6TLmGsudk5nfXYjXG/B8r8jMj5lrV27sOzsR8VfAb1NduPg8\nqsP3HyzzrszMvr8fMI/cX1IdJvX1hpM7BXg01akjP6W6MPBc4FDglsx83ZBzJ1ON8HEG1XCkUO3g\njqY6HGxulrk2lHEmEfGjzNzDnLmeeU1vA92HDen1RvDevZFqNLw1bLpNOorqFJvV5rqXG2SctmXm\n5pDLIZ0Pt1A3qnP7JkZx2I7qPL/3lceDRuEwNya58ncLqqFCtyyPlzB41Ja6ub6/LQAEg899NdfC\nMpb5P53mdhdwnzlzk3Kt2XaaG3kZv0+fawqo/gk3cFtmrtW5q6e5fRP4pbnxzvW7tWE0tiVZjWZC\nZt4ZEc8DPhoRn2TAiDTmxiY3kbk3Ii7LzInRUO6LiGlPRZtH7p6I2DczL5s0fV/gHnNzyrWhjFAN\naLBvllFzesXgEXDMLc5cW7ad5kZfxgeofpD0hknTdy3zzHUztzPVD+TeMWl6UP2WlLnxzk3Rhs7O\nDyLimZn5JYCsLkY7LiLeQfV7AubGO3dzRDwyM3+WmSsnJkbELpRhIIecOxb4cERsw0OHrXen+uXs\nY83NKdeGMkJ12tuvAVO+9FL9sJ85c73asu00N/oynghcHBHX8tCQw3tQjXb1anOdzf0r1ShnU4ZF\njogvmhv73BRtuGbnYQCZ+Ys+83bLzPXmxjc3nYh4BPCIzLx1IXKlU/TgBYmZefMsn99cC8sozVZb\ntp3mpuZGsf+KiM2A/dj0AvfLcuZRwMy1OKeOyTmc8zYuN+At5syZaz7XhjKaM2du8eRGUMY5/XK7\nOXPmRp8b9Iun4+wwc+bMjSTXhjKaM2du8eSaLuMfmzNnrl25tnZ2wpw5cyPJtaGM5syZWzy5NpTR\nnDlzI8yN/TU7/UTEZjngRyXNmTO3MLk2lNGcOXOLJzeCMi7LzHUzL2nOnLlxybXyyM7EBioi/sKc\nucki4pCIOC4ilk/K/ZG5ueXaUEZz5swtnlzDrxUR8eKIeFG5fxDwhoh4VVQXvpszZ27Mcn2fq41H\ndibEuP9iq7nGc9H8L2x3NteGMpozZ27x5EZQxlOAR1P9Fs9Pga2Ac4FDgVsy83XmzJkbr1xfWWNU\ngyZvtOdXuc2NR27sfwW8Lbk2lNGcOXOLJzeKMpa/WwC3A1uWx0uAq82ZMzd+uX63NpzGdiewV2Y+\natJtG+Amc+Ym2eSXsqn+i/eomOMvbJtrTRnNmTO3eHJNl3Eicy/Vb7P8qjy+Dxh0vY85c+ZGl5ui\nDZ2diV/J7mc2v65tbnHlfhARz5x4kJn3Z+ZxwPeAJ5qbU64NZTRnztziyTVdxpsj4pEls3JiYlQ/\nfPwrc+bMjWVuilZf9ZaMSQAABztJREFUsyNNFi34FfC25NpQRnPmzC2eXNNlnE5EPAJ4RGbeas6c\nufHPtaKzExHbAiuB3cqk9cAFWR2ONmfO3ALl2lBGc+bMLZ5cG8pozpy50eYmG/vT2CLiaKoRVJ4F\nPLzcDgSuKPPMmTO3ALk2lNGcOXOLJ9eGMpozZ260ub5yDqMZjOJGdU7tdn2mbw9835w5cwuTa0MZ\nzZkzt3hybSijOXPmRpvrdxv7IztAAP3OtXugzDNnztzC5NpQRnPmzC2eXBvKaM6cudHmplgyl4VH\n5J3AlRFxIXBjmbYH8Gzg7ebMmVuwXBvKaM6cucWTa0MZzZkzN9rcFG0ZoGB74BCmXqB0hzlz5hYu\n14YymjNnbvHk2lBGc+bMjTY3xVzOeRuXG/D75syZaz7XhjKaM2du8eTaUEZz5syNOFcnNOobcKU5\nc+aaz7WhjObMmVs8uTaU0Zw5c6PNtWGAgn7mdGGSOXPmhpZrQxnNmTO3eHJtKKM5c+ZGmavTQxr1\nDdjPnDlzzefaUEZz5swtnlwbymjOnLnR5towGhsR8QTgcHouUIqIuzLzGnPmzC1crg1lNGfO3OLJ\ntaGM5syZG21usrE/jS0i3gisoTp0dWm5BXBmRKwyZ87cwuTaUEZz5swtnlwbymjOnLnR5vqqczio\nyRvwfWCLPtO3BK41Z87cwuTaUEZz5swtnlwbymjOnLnR5vrdxv7IDtUvpT6mz/Rdyzxz5swtTK4N\nZTRnztziybWhjObMmRttboo2XLPz/9u7m1CpyjiO49+fKRIa17SyRaRUWhGZiwihjRGFgVCLXkCo\nZRDYCyS1bNPCVVBY9LIpWgRR0DIXQUGFZKi3kBIXGi7CQg00ctH1aTFHuDpzk1mce+aZ8/3AAec8\n53v4z0oeZubcl4Cvkhzl0r+gehuw087OrrWuhhnt7Oz609Uwo52dXbfdkDQfCU20JEuA+7j0L6ju\nL6XM2dnZtdfVMKOdnV1/uhpmtLOz67Ybuk8Nmx1JkiRJGtfE/2YnyaYk+5KcSPJ+kmvnrf1gZ2fX\nTlfDjHZ2dv3papjRzs6u226kcZ5m0MUBfAtsA1YBu4DDwK3N2kE7O7t2uhpmtLOz609Xw4x2dnbd\ndiPvNc7FXRzA7GWvHwCOAluAA3Z2du10NcxoZ2fXn66GGe3s7LrtRt5rnIu7OIBZYOayc5uaN3zK\nzs6una6GGe3s7PrT1TCjnZ1dt93Ie41zcRcHsAPYMuL8zcAHdnZ27XQ1zGhnZ9efroYZ7ezsuu1G\nHT6NTZIkSdJ0Gmdn1MUBzAC7gV+B08Ap4Jfm3Co7O7t2uhpmtLOz609Xw4x2dnbddqOOiX/0NPAp\ncAbYWkpZXUpZw+BHSmeaNTs7u3a6Gma0s7PrT1fDjHZ2dt12w8bZGXVxAEdcc821xV+blDlcc801\n1yZpDtdcc21y10YdNXyy81uSV5KsvXgiydokrwIn7OzsWutqmNHOzq4/XQ0z2tnZddsNqWGz8xSw\nBvgmyekkp4GvgdXAk3Z2dq11NcxoZ2fXn66GGe3s7Lrthvg0NkmSJElTqYZPdkhyR5IHk6y47Pw2\nOzu79roaZrSzs+tPV8OMdnZ23XZDxvmBTxcH8AJwBPgCOA48Om/tgJ2dXTtdDTPa2dn1p6thRjs7\nu267kfca5+IuDuBnYGXz7/XAj8CLzeuDdnZ27XQ1zGhnZ9efroYZ7ezsuu1GHUuZfEtKKecASinH\nk2wFPkuyDoidnV1rXQ0z2tnZ9aerYUY7O7tuu+EbjXNxR04m2XzxRfPGtwPXAXfb2dm11tUwo52d\nXX+6Gma0s7PrthtWxvgYqIsDuAm4cYG1++3s7NrpapjRzs6uP10NM9rZ2XXbjTp89LQkSZKkqVTD\n19gkSZIkaWxudiRJkiRNJTc7kqTOJZlLcijJ4SSzSV5O8r//RyVZn2THYs0oSaqPmx1J0iT4p5Sy\nuZRyF/AQ8Ajw2hWa9YCbHUnSgnxAgSSpc0nOlVJWznt9C7CfwWNG1wEfAyua5Z2llO+T7APuBI4B\nHwFvAbuBrcBy4O1SynuL9iYkSRPHzY4kqXOXb3aac38BtwNngQullPNJNgCflFLubf7I3K5Syvbm\n+meBG0oprydZDnwHPFFKObaob0aSNDGWdj2AJElXsAzY0/yBuTlg4wLXPQxsSvJ483oG2MDgkx9J\nUg+52ZEkTZzma2xzwB8MfrtzEriHwW9Nzy+UAc+XUvYuypCSpInnAwokSRMlyfXAu8CeMviu9Qzw\neynlAvA0cFVz6VngmnnpXuC5JMua+2xMsgJJUm/5yY4kaRJcneQQg6+s/cvggQRvNGvvAJ8neQb4\nEvi7Of8TMJdkFvgQeJPBE9oOJAnwJ/DYYr0BSdLk8QEFkiRJkqaSX2OTJEmSNJXc7EiSJEmaSm52\nJEmSJE0lNzuSJEmSppKbHUmSJElTyc2OJEmSpKnkZkeSJEnSVHKzI0mSJGkq/QepnfAAFmWK+gAA\nAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1008x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fV-B00Tw15ox",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 462
        },
        "outputId": "7061d773-9dd7-48ae-dde8-74389b19a26b"
      },
      "source": [
        "combined_df[['Date','Country','State','Active']].query(\"Country=='Italy'\").plot(x='Date',y='Active',kind='bar', title=\"Corona Trend for Country Italy\")"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fcd18746208>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        },
        {
          "output_type": "display_data",
          "data": {
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pdbxZgiRJ0hB50wNpNHhESJIkSVLreERIkiSpAY/sSGXziJAkSZKk1rEjJEmS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\nhiMIAQAAw/kFkgzHMqAOJekAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 1008x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dfUpysi-4JxS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 462
        },
        "outputId": "307b2a52-2ee7-4195-97b4-139b1c561871"
      },
      "source": [
        "combined_df[['Date','Country','State','Active']].query(\"Country=='Iran'\").plot(x='Date',y='Active',kind='bar', title=\"Corona Trend for Country Iran\")"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fcd184a5a58>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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up8PtCdycmT8BiIilwCHAjSOtSpIkVT49zdPaJI2rcekE7Qjc1vF4JbDXiGqR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\nrkJggg==\n",
            "text/plain": [
              "<Figure size 1008x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cjU0zuec2GKv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 446
        },
        "outputId": "ffb59508-1486-49f5-994b-020c9f501dac"
      },
      "source": [
        "combined_df[['Date','Country','State','Active']].query(\"Country=='US' & State=='Washington'\").plot(x='Date',y='Active',kind='bar')"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fcd182154e0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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UUj9tkCRJktpsyfLLJ122YcWRNbYkP/1+snMW8GHgifL6QuDBlNJj5fWNwO7l5d2BuwDK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\nAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 1008x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7kANzLgW2hYN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 446
        },
        "outputId": "6a9397e4-9daa-441c-a17b-c56425b6d832"
      },
      "source": [
        "combined_df[['Date','Country','State','Active']].query(\"Country=='US' & State=='New York'\").plot(x='Date',y='Active',kind='bar')"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fcd16b70f60>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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a1m3YjtF3U0ScGxEvi4hnl8PLyp4kJuy9o+yxAyh674iIM5lEryYVclXLWXeu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\nYII=\n",
            "text/plain": [
              "<Figure size 1008x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SKsqp1tb3R6X",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 462
        },
        "outputId": "1b05c988-1ff1-4e03-91e7-f412552dbf47"
      },
      "source": [
        "combined_df[['Date','Country','State','Active']].groupby(['Date','Country']).sum().query(\"Country=='US'\").reset_index().plot(x='Date',y='Active',kind='bar',title=\"Corona Trend for Country USA\")"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fcd169d5080>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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kSZIkTSI7O5IkSZJayc6OJEmSpFaysyNJkiSplezsSJIkSWolOzuSJEmSWsnO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\n",
            "text/plain": [
              "<Figure size 1008x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q6WqmevfC6rk",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 446
        },
        "outputId": "3b5d8f81-0c64-493b-c550-8bc6f0cb4f5a"
      },
      "source": [
        "combined_df[['Date','Country','State','Active']].query(\"Country=='India'\").plot(x='Date',y='Active',kind='bar')"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fcd16899908>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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X2cj9+jXNNW1fk9y498m4v2fHvX2lbJNK6c+c64vrSns5t+8Al0TERcCXpuSO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\nUtH+H39N7+TV79shAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 1008x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O-DRefPWXZdT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "ab5d03ec-c222-422d-921c-6b23ca732294"
      },
      "source": [
        "combined_df.query(\"Country=='India'\")"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>State</th>\n",
              "      <th>Country</th>\n",
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              "  <tbody>\n",
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              "      <td>NaN</td>\n",
              "      <td>India</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>India</td>\n",
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              "      <td>2020-01-26</td>\n",
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              "    <tr>\n",
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              "      <td>2020-01-28</td>\n",
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              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>3235</th>\n",
              "      <td>NaN</td>\n",
              "      <td>India</td>\n",
              "      <td>21.0</td>\n",
              "      <td>78.0</td>\n",
              "      <td>2020-01-29</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
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              "      <td>2020-01-30</td>\n",
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              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>4155</th>\n",
              "      <td>NaN</td>\n",
              "      <td>India</td>\n",
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              "      <td>2020-01-31</td>\n",
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              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "      <td>2020-02-01</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "      <td>2</td>\n",
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              "      <td>0</td>\n",
              "      <td>3</td>\n",
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              "    <tr>\n",
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              "      <td>3</td>\n",
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              "      <td>0</td>\n",
              "      <td>3</td>\n",
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              "    <tr>\n",
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              "      <td>3</td>\n",
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              "    <tr>\n",
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              "      <td>India</td>\n",
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              "      <td>2020-02-06</td>\n",
              "      <td>3</td>\n",
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              "      <td>0</td>\n",
              "      <td>3</td>\n",
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              "    <tr>\n",
              "      <th>7375</th>\n",
              "      <td>NaN</td>\n",
              "      <td>India</td>\n",
              "      <td>21.0</td>\n",
              "      <td>78.0</td>\n",
              "      <td>2020-02-07</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
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              "    <tr>\n",
              "      <th>7835</th>\n",
              "      <td>NaN</td>\n",
              "      <td>India</td>\n",
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              "      <td>2020-02-08</td>\n",
              "      <td>3</td>\n",
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              "      <td>0</td>\n",
              "      <td>3</td>\n",
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              "      <th>8295</th>\n",
              "      <td>NaN</td>\n",
              "      <td>India</td>\n",
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              "      <td>2020-02-09</td>\n",
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              "      <td>3</td>\n",
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              "    <tr>\n",
              "      <th>9215</th>\n",
              "      <td>NaN</td>\n",
              "      <td>India</td>\n",
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              "      <td>78.0</td>\n",
              "      <td>2020-02-11</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
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              "      <td>India</td>\n",
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              "      <td>2020-02-12</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
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              "    <tr>\n",
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              "      <td>India</td>\n",
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              "      <td>78.0</td>\n",
              "      <td>2020-02-13</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10595</th>\n",
              "      <td>NaN</td>\n",
              "      <td>India</td>\n",
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              "      <td>2020-02-14</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
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              "      <td>NaN</td>\n",
              "      <td>India</td>\n",
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              "      <td>78.0</td>\n",
              "      <td>2020-02-15</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
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              "    <tr>\n",
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              "      <td>NaN</td>\n",
              "      <td>India</td>\n",
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              "      <td>2020-02-16</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11975</th>\n",
              "      <td>NaN</td>\n",
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              "      <td>2020-02-17</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
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              "      <td>NaN</td>\n",
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              "      <td>78.0</td>\n",
              "      <td>2020-02-18</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12895</th>\n",
              "      <td>NaN</td>\n",
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              "      <td>2020-02-19</td>\n",
              "      <td>3</td>\n",
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              "      <td>3</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>13355</th>\n",
              "      <td>NaN</td>\n",
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              "      <td>2020-02-20</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
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              "      <td>2020-02-21</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
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              "      <td>NaN</td>\n",
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              "      <td>2020-02-22</td>\n",
              "      <td>3</td>\n",
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              "      <td>2020-02-23</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
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              "      <td>NaN</td>\n",
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              "      <td>2020-02-24</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15655</th>\n",
              "      <td>NaN</td>\n",
              "      <td>India</td>\n",
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              "      <td>2020-02-25</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16115</th>\n",
              "      <td>NaN</td>\n",
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              "      <td>2020-02-26</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
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              "      <td>NaN</td>\n",
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              "      <td>2020-02-27</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17035</th>\n",
              "      <td>NaN</td>\n",
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              "      <td>2020-02-28</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
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              "      <td>2020-02-29</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
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              "      <td>3</td>\n",
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              "      <td>3</td>\n",
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              "    <tr>\n",
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              "      <td>NaN</td>\n",
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              "      <td>78.0</td>\n",
              "      <td>2020-03-02</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
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              "      <td>2</td>\n",
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              "    <tr>\n",
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              "      <td>5</td>\n",
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              "      <td>3</td>\n",
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              "    <tr>\n",
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              "    <tr>\n",
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              "      <td>30</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
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              "      <td>78.0</td>\n",
              "      <td>2020-03-06</td>\n",
              "      <td>31</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>28</td>\n",
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              "    <tr>\n",
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              "      <td>India</td>\n",
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              "      <td>78.0</td>\n",
              "      <td>2020-03-07</td>\n",
              "      <td>34</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21175</th>\n",
              "      <td>NaN</td>\n",
              "      <td>India</td>\n",
              "      <td>21.0</td>\n",
              "      <td>78.0</td>\n",
              "      <td>2020-03-08</td>\n",
              "      <td>39</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>36</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21635</th>\n",
              "      <td>NaN</td>\n",
              "      <td>India</td>\n",
              "      <td>21.0</td>\n",
              "      <td>78.0</td>\n",
              "      <td>2020-03-09</td>\n",
              "      <td>43</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22095</th>\n",
              "      <td>NaN</td>\n",
              "      <td>India</td>\n",
              "      <td>21.0</td>\n",
              "      <td>78.0</td>\n",
              "      <td>2020-03-10</td>\n",
              "      <td>56</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>52</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22555</th>\n",
              "      <td>NaN</td>\n",
              "      <td>India</td>\n",
              "      <td>21.0</td>\n",
              "      <td>78.0</td>\n",
              "      <td>2020-03-11</td>\n",
              "      <td>62</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>57</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23015</th>\n",
              "      <td>NaN</td>\n",
              "      <td>India</td>\n",
              "      <td>21.0</td>\n",
              "      <td>78.0</td>\n",
              "      <td>2020-03-12</td>\n",
              "      <td>73</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>68</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23475</th>\n",
              "      <td>NaN</td>\n",
              "      <td>India</td>\n",
              "      <td>21.0</td>\n",
              "      <td>78.0</td>\n",
              "      <td>2020-03-13</td>\n",
              "      <td>82</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>76</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23935</th>\n",
              "      <td>NaN</td>\n",
              "      <td>India</td>\n",
              "      <td>21.0</td>\n",
              "      <td>78.0</td>\n",
              "      <td>2020-03-14</td>\n",
              "      <td>102</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>96</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24395</th>\n",
              "      <td>NaN</td>\n",
              "      <td>India</td>\n",
              "      <td>21.0</td>\n",
              "      <td>78.0</td>\n",
              "      <td>2020-03-15</td>\n",
              "      <td>113</td>\n",
              "      <td>2</td>\n",
              "      <td>13</td>\n",
              "      <td>98</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24855</th>\n",
              "      <td>NaN</td>\n",
              "      <td>India</td>\n",
              "      <td>21.0</td>\n",
              "      <td>78.0</td>\n",
              "      <td>2020-03-16</td>\n",
              "      <td>119</td>\n",
              "      <td>2</td>\n",
              "      <td>13</td>\n",
              "      <td>104</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25315</th>\n",
              "      <td>NaN</td>\n",
              "      <td>India</td>\n",
              "      <td>21.0</td>\n",
              "      <td>78.0</td>\n",
              "      <td>2020-03-17</td>\n",
              "      <td>142</td>\n",
              "      <td>3</td>\n",
              "      <td>14</td>\n",
              "      <td>125</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      State Country   Lat  Long       Date  Confirmed  Death  Recovered  Active\n",
              "15      NaN   India  21.0  78.0 2020-01-22          0      0          0       0\n",
              "475     NaN   India  21.0  78.0 2020-01-23          0      0          0       0\n",
              "935     NaN   India  21.0  78.0 2020-01-24          0      0          0       0\n",
              "1395    NaN   India  21.0  78.0 2020-01-25          0      0          0       0\n",
              "1855    NaN   India  21.0  78.0 2020-01-26          0      0          0       0\n",
              "2315    NaN   India  21.0  78.0 2020-01-27          0      0          0       0\n",
              "2775    NaN   India  21.0  78.0 2020-01-28          0      0          0       0\n",
              "3235    NaN   India  21.0  78.0 2020-01-29          0      0          0       0\n",
              "3695    NaN   India  21.0  78.0 2020-01-30          1      0          0       1\n",
              "4155    NaN   India  21.0  78.0 2020-01-31          1      0          0       1\n",
              "4615    NaN   India  21.0  78.0 2020-02-01          1      0          0       1\n",
              "5075    NaN   India  21.0  78.0 2020-02-02          2      0          0       2\n",
              "5535    NaN   India  21.0  78.0 2020-02-03          3      0          0       3\n",
              "5995    NaN   India  21.0  78.0 2020-02-04          3      0          0       3\n",
              "6455    NaN   India  21.0  78.0 2020-02-05          3      0          0       3\n",
              "6915    NaN   India  21.0  78.0 2020-02-06          3      0          0       3\n",
              "7375    NaN   India  21.0  78.0 2020-02-07          3      0          0       3\n",
              "7835    NaN   India  21.0  78.0 2020-02-08          3      0          0       3\n",
              "8295    NaN   India  21.0  78.0 2020-02-09          3      0          0       3\n",
              "8755    NaN   India  21.0  78.0 2020-02-10          3      0          0       3\n",
              "9215    NaN   India  21.0  78.0 2020-02-11          3      0          0       3\n",
              "9675    NaN   India  21.0  78.0 2020-02-12          3      0          0       3\n",
              "10135   NaN   India  21.0  78.0 2020-02-13          3      0          0       3\n",
              "10595   NaN   India  21.0  78.0 2020-02-14          3      0          0       3\n",
              "11055   NaN   India  21.0  78.0 2020-02-15          3      0          0       3\n",
              "11515   NaN   India  21.0  78.0 2020-02-16          3      0          3       0\n",
              "11975   NaN   India  21.0  78.0 2020-02-17          3      0          3       0\n",
              "12435   NaN   India  21.0  78.0 2020-02-18          3      0          3       0\n",
              "12895   NaN   India  21.0  78.0 2020-02-19          3      0          3       0\n",
              "13355   NaN   India  21.0  78.0 2020-02-20          3      0          3       0\n",
              "13815   NaN   India  21.0  78.0 2020-02-21          3      0          3       0\n",
              "14275   NaN   India  21.0  78.0 2020-02-22          3      0          3       0\n",
              "14735   NaN   India  21.0  78.0 2020-02-23          3      0          3       0\n",
              "15195   NaN   India  21.0  78.0 2020-02-24          3      0          3       0\n",
              "15655   NaN   India  21.0  78.0 2020-02-25          3      0          3       0\n",
              "16115   NaN   India  21.0  78.0 2020-02-26          3      0          3       0\n",
              "16575   NaN   India  21.0  78.0 2020-02-27          3      0          3       0\n",
              "17035   NaN   India  21.0  78.0 2020-02-28          3      0          3       0\n",
              "17495   NaN   India  21.0  78.0 2020-02-29          3      0          3       0\n",
              "17955   NaN   India  21.0  78.0 2020-03-01          3      0          3       0\n",
              "18415   NaN   India  21.0  78.0 2020-03-02          5      0          3       2\n",
              "18875   NaN   India  21.0  78.0 2020-03-03          5      0          3       2\n",
              "19335   NaN   India  21.0  78.0 2020-03-04         28      0          3      25\n",
              "19795   NaN   India  21.0  78.0 2020-03-05         30      0          3      27\n",
              "20255   NaN   India  21.0  78.0 2020-03-06         31      0          3      28\n",
              "20715   NaN   India  21.0  78.0 2020-03-07         34      0          3      31\n",
              "21175   NaN   India  21.0  78.0 2020-03-08         39      0          3      36\n",
              "21635   NaN   India  21.0  78.0 2020-03-09         43      0          3      40\n",
              "22095   NaN   India  21.0  78.0 2020-03-10         56      0          4      52\n",
              "22555   NaN   India  21.0  78.0 2020-03-11         62      1          4      57\n",
              "23015   NaN   India  21.0  78.0 2020-03-12         73      1          4      68\n",
              "23475   NaN   India  21.0  78.0 2020-03-13         82      2          4      76\n",
              "23935   NaN   India  21.0  78.0 2020-03-14        102      2          4      96\n",
              "24395   NaN   India  21.0  78.0 2020-03-15        113      2         13      98\n",
              "24855   NaN   India  21.0  78.0 2020-03-16        119      2         13     104\n",
              "25315   NaN   India  21.0  78.0 2020-03-17        142      3         14     125"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n0xpiEV5cXRP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 446
        },
        "outputId": "18eb0779-f78f-4539-a028-71579fceca0e"
      },
      "source": [
        "combined_df[['Date','Country','State','Active']].query(\"Country=='US' & State=='Arizona'\").plot(x='Date',y='Active',kind='bar')"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fcd1672a0f0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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NEXHymNwJwGXAYcCD6+lwquEiT5hWZsI6ls6Vbt8RVL8a/Xbg6Hp6B3BdvWza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\n",
            "text/plain": [
              "<Figure size 1008x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0TEC3e6PcXFM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 446
        },
        "outputId": "2eb14fc1-fe25-4fc4-f78e-fec33bad45a1"
      },
      "source": [
        "combined_df[['Date','Country','State','Active']].query(\"Country=='Brazil'\").plot(x='Date',y='Active',kind='bar')"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fcd165fdef0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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f2WcPuf0HgQ/OpyhJkiRJmquuBwqQJEmSpIngoEaSJElS1RzUSJIkSaqagxpJkiRJVXNQ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\nJEmqmoMaSZIkSVX7/wHUJWWiSNO9hwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1008x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p5943O8HcXCB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}