Recovered_of_covid19_data_analysis_notebook.ipynb

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      "name": "Recovered of covid19 data analysis notebook.ipynb",
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cj_Hn32NjvmI",
        "colab_type": "text"
      },
      "source": [
        "# Welcome to Covid19 Data Analysis Notebook\n",
        "------------------------------------------"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KjBIE7hgjvmJ",
        "colab_type": "text"
      },
      "source": [
        "### Let's Import the modules "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xA6BGaKljvmK",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "8bddfa72-e2bc-431e-88d4-5d10252d7d3d"
      },
      "source": [
        "import pandas as pd \n",
        "import numpy as np \n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt \n",
        "print('Modules are imported.')"
      ],
      "execution_count": 104,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Modules are imported.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "osD2o4yejvmP",
        "colab_type": "text"
      },
      "source": [
        "## Task 2 "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IUUI26cQjvmP",
        "colab_type": "text"
      },
      "source": [
        "### Task 2.1: importing covid19 dataset\n",
        "importing \"Covid19_Confirmed_dataset.csv\" from \"./Dataset\" folder. \n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0PlAuOzTjvmQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 457
        },
        "outputId": "91277d0b-c45b-4015-a376-12c936a2139b"
      },
      "source": [
        "corona_dataset_csv= pd.read_csv(\"sample_data/time_series_covid19_recovered_global.csv\")\n",
        "corona_dataset_csv.head(10)"
      ],
      "execution_count": 105,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <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>...</td>\n",
              "      <td>17</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>18</td>\n",
              "      <td>18</td>\n",
              "      <td>18</td>\n",
              "      <td>18</td>\n",
              "      <td>18</td>\n",
              "      <td>21</td>\n",
              "      <td>24</td>\n",
              "      <td>24</td>\n",
              "      <td>38</td>\n",
              "      <td>38</td>\n",
              "      <td>40</td>\n",
              "      <td>41</td>\n",
              "      <td>42</td>\n",
              "      <td>61</td>\n",
              "      <td>61</td>\n",
              "      <td>64</td>\n",
              "      <td>64</td>\n",
              "      <td>64</td>\n",
              "      <td>64</td>\n",
              "      <td>66</td>\n",
              "      <td>66</td>\n",
              "      <td>77</td>\n",
              "      <td>77</td>\n",
              "      <td>77</td>\n",
              "      <td>77</td>\n",
              "      <td>81</td>\n",
              "      <td>81</td>\n",
              "      <td>81</td>\n",
              "      <td>81</td>\n",
              "      <td>93</td>\n",
              "      <td>93</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Antigua and Barbuda</td>\n",
              "      <td>17.0608</td>\n",
              "      <td>-61.7964</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>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>...</td>\n",
              "      <td>19</td>\n",
              "      <td>19</td>\n",
              "      <td>19</td>\n",
              "      <td>19</td>\n",
              "      <td>19</td>\n",
              "      <td>19</td>\n",
              "      <td>19</td>\n",
              "      <td>19</td>\n",
              "      <td>19</td>\n",
              "      <td>19</td>\n",
              "      <td>19</td>\n",
              "      <td>20</td>\n",
              "      <td>20</td>\n",
              "      <td>20</td>\n",
              "      <td>20</td>\n",
              "      <td>20</td>\n",
              "      <td>20</td>\n",
              "      <td>20</td>\n",
              "      <td>20</td>\n",
              "      <td>20</td>\n",
              "      <td>20</td>\n",
              "      <td>20</td>\n",
              "      <td>20</td>\n",
              "      <td>20</td>\n",
              "      <td>20</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>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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Argentina</td>\n",
              "      <td>-38.4161</td>\n",
              "      <td>-63.6167</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>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>...</td>\n",
              "      <td>3062</td>\n",
              "      <td>3530</td>\n",
              "      <td>3732</td>\n",
              "      <td>3999</td>\n",
              "      <td>4167</td>\n",
              "      <td>4349</td>\n",
              "      <td>4617</td>\n",
              "      <td>4788</td>\n",
              "      <td>4788</td>\n",
              "      <td>5336</td>\n",
              "      <td>5521</td>\n",
              "      <td>5709</td>\n",
              "      <td>5896</td>\n",
              "      <td>5993</td>\n",
              "      <td>6088</td>\n",
              "      <td>6180</td>\n",
              "      <td>6909</td>\n",
              "      <td>7305</td>\n",
              "      <td>7568</td>\n",
              "      <td>7991</td>\n",
              "      <td>8332</td>\n",
              "      <td>8743</td>\n",
              "      <td>9083</td>\n",
              "      <td>9564</td>\n",
              "      <td>9891</td>\n",
              "      <td>10174</td>\n",
              "      <td>10512</td>\n",
              "      <td>10721</td>\n",
              "      <td>11851</td>\n",
              "      <td>12206</td>\n",
              "      <td>12728</td>\n",
              "      <td>13153</td>\n",
              "      <td>13576</td>\n",
              "      <td>13816</td>\n",
              "      <td>14788</td>\n",
              "      <td>18416</td>\n",
              "      <td>19143</td>\n",
              "      <td>20134</td>\n",
              "      <td>21138</td>\n",
              "      <td>22028</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Armenia</td>\n",
              "      <td>40.0691</td>\n",
              "      <td>45.0382</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>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>...</td>\n",
              "      <td>2874</td>\n",
              "      <td>2936</td>\n",
              "      <td>3064</td>\n",
              "      <td>3145</td>\n",
              "      <td>3220</td>\n",
              "      <td>3255</td>\n",
              "      <td>3287</td>\n",
              "      <td>3297</td>\n",
              "      <td>3317</td>\n",
              "      <td>3386</td>\n",
              "      <td>3402</td>\n",
              "      <td>3427</td>\n",
              "      <td>3454</td>\n",
              "      <td>3468</td>\n",
              "      <td>3513</td>\n",
              "      <td>3720</td>\n",
              "      <td>4014</td>\n",
              "      <td>4099</td>\n",
              "      <td>4451</td>\n",
              "      <td>5226</td>\n",
              "      <td>5466</td>\n",
              "      <td>5639</td>\n",
              "      <td>6081</td>\n",
              "      <td>6214</td>\n",
              "      <td>6276</td>\n",
              "      <td>6571</td>\n",
              "      <td>6814</td>\n",
              "      <td>7560</td>\n",
              "      <td>8266</td>\n",
              "      <td>8854</td>\n",
              "      <td>9002</td>\n",
              "      <td>9131</td>\n",
              "      <td>10144</td>\n",
              "      <td>10797</td>\n",
              "      <td>11335</td>\n",
              "      <td>12149</td>\n",
              "      <td>12911</td>\n",
              "      <td>13116</td>\n",
              "      <td>13297</td>\n",
              "      <td>14048</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Australian Capital Territory</td>\n",
              "      <td>Australia</td>\n",
              "      <td>-35.4735</td>\n",
              "      <td>149.0124</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>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>...</td>\n",
              "      <td>104</td>\n",
              "      <td>104</td>\n",
              "      <td>104</td>\n",
              "      <td>104</td>\n",
              "      <td>104</td>\n",
              "      <td>104</td>\n",
              "      <td>104</td>\n",
              "      <td>104</td>\n",
              "      <td>104</td>\n",
              "      <td>104</td>\n",
              "      <td>104</td>\n",
              "      <td>104</td>\n",
              "      <td>104</td>\n",
              "      <td>104</td>\n",
              "      <td>104</td>\n",
              "      <td>104</td>\n",
              "      <td>104</td>\n",
              "      <td>104</td>\n",
              "      <td>104</td>\n",
              "      <td>104</td>\n",
              "      <td>104</td>\n",
              "      <td>104</td>\n",
              "      <td>104</td>\n",
              "      <td>104</td>\n",
              "      <td>104</td>\n",
              "      <td>105</td>\n",
              "      <td>105</td>\n",
              "      <td>105</td>\n",
              "      <td>105</td>\n",
              "      <td>105</td>\n",
              "      <td>105</td>\n",
              "      <td>105</td>\n",
              "      <td>105</td>\n",
              "      <td>105</td>\n",
              "      <td>105</td>\n",
              "      <td>105</td>\n",
              "      <td>105</td>\n",
              "      <td>105</td>\n",
              "      <td>105</td>\n",
              "      <td>105</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>New South Wales</td>\n",
              "      <td>Australia</td>\n",
              "      <td>-33.8688</td>\n",
              "      <td>151.2093</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>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>...</td>\n",
              "      <td>2653</td>\n",
              "      <td>2653</td>\n",
              "      <td>2656</td>\n",
              "      <td>2661</td>\n",
              "      <td>2663</td>\n",
              "      <td>2670</td>\n",
              "      <td>2666</td>\n",
              "      <td>2683</td>\n",
              "      <td>2685</td>\n",
              "      <td>2685</td>\n",
              "      <td>2687</td>\n",
              "      <td>2691</td>\n",
              "      <td>2700</td>\n",
              "      <td>2714</td>\n",
              "      <td>2719</td>\n",
              "      <td>2723</td>\n",
              "      <td>2724</td>\n",
              "      <td>2729</td>\n",
              "      <td>2735</td>\n",
              "      <td>2738</td>\n",
              "      <td>2743</td>\n",
              "      <td>2763</td>\n",
              "      <td>2766</td>\n",
              "      <td>2766</td>\n",
              "      <td>2768</td>\n",
              "      <td>2770</td>\n",
              "      <td>2770</td>\n",
              "      <td>2770</td>\n",
              "      <td>2771</td>\n",
              "      <td>2771</td>\n",
              "      <td>2771</td>\n",
              "      <td>2771</td>\n",
              "      <td>2771</td>\n",
              "      <td>2776</td>\n",
              "      <td>2782</td>\n",
              "      <td>2784</td>\n",
              "      <td>2784</td>\n",
              "      <td>2784</td>\n",
              "      <td>2784</td>\n",
              "      <td>2787</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>10 rows × 165 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                 Province/State       Country/Region  ...  6/29/20  6/30/20\n",
              "0                           NaN          Afghanistan  ...    13934    14131\n",
              "1                           NaN              Albania  ...     1438     1459\n",
              "2                           NaN              Algeria  ...     9674     9897\n",
              "3                           NaN              Andorra  ...      799      799\n",
              "4                           NaN               Angola  ...       93       93\n",
              "5                           NaN  Antigua and Barbuda  ...       22       22\n",
              "6                           NaN            Argentina  ...    21138    22028\n",
              "7                           NaN              Armenia  ...    13297    14048\n",
              "8  Australian Capital Territory            Australia  ...      105      105\n",
              "9               New South Wales            Australia  ...     2784     2787\n",
              "\n",
              "[10 rows x 165 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 105
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4yKBcAOsjvmT",
        "colab_type": "text"
      },
      "source": [
        "#### Let's check the shape of the dataframe"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bkO5au3NjvmU",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "ef6e188c-1597-42b0-8dc1-b530d781e17f"
      },
      "source": [
        "corona_dataset_csv.shape"
      ],
      "execution_count": 106,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(253, 165)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 106
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NR_J5rKWjvmZ",
        "colab_type": "text"
      },
      "source": [
        "### Task 2.2: Delete the useless columns"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WkL0U4Lgjvma",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "corona_dataset_csv.drop([\"Lat\",\"Long\"],axis=1,inplace=True)"
      ],
      "execution_count": 107,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SrdYJWKWjvmc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 251
        },
        "outputId": "d893b5ec-fa32-412c-aa85-452d9e0cb877"
      },
      "source": [
        "\n",
        "corona_dataset_csv.head()"
      ],
      "execution_count": 108,
      "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>Province/State</th>\n",
              "      <th>Country/Region</th>\n",
              "      <th>1/22/20</th>\n",
              "      <th>1/23/20</th>\n",
              "      <th>1/24/20</th>\n",
              "      <th>1/25/20</th>\n",
              "      <th>1/26/20</th>\n",
              "      <th>1/27/20</th>\n",
              "      <th>1/28/20</th>\n",
              "      <th>1/29/20</th>\n",
              "      <th>1/30/20</th>\n",
              "      <th>1/31/20</th>\n",
              "      <th>2/1/20</th>\n",
              "      <th>2/2/20</th>\n",
              "      <th>2/3/20</th>\n",
              "      <th>2/4/20</th>\n",
              "      <th>2/5/20</th>\n",
              "      <th>2/6/20</th>\n",
              "      <th>2/7/20</th>\n",
              "      <th>2/8/20</th>\n",
              "      <th>2/9/20</th>\n",
              "      <th>2/10/20</th>\n",
              "      <th>2/11/20</th>\n",
              "      <th>2/12/20</th>\n",
              "      <th>2/13/20</th>\n",
              "      <th>2/14/20</th>\n",
              "      <th>2/15/20</th>\n",
              "      <th>2/16/20</th>\n",
              "      <th>2/17/20</th>\n",
              "      <th>2/18/20</th>\n",
              "      <th>2/19/20</th>\n",
              "      <th>2/20/20</th>\n",
              "      <th>2/21/20</th>\n",
              "      <th>2/22/20</th>\n",
              "      <th>2/23/20</th>\n",
              "      <th>2/24/20</th>\n",
              "      <th>2/25/20</th>\n",
              "      <th>2/26/20</th>\n",
              "      <th>2/27/20</th>\n",
              "      <th>2/28/20</th>\n",
              "      <th>...</th>\n",
              "      <th>5/22/20</th>\n",
              "      <th>5/23/20</th>\n",
              "      <th>5/24/20</th>\n",
              "      <th>5/25/20</th>\n",
              "      <th>5/26/20</th>\n",
              "      <th>5/27/20</th>\n",
              "      <th>5/28/20</th>\n",
              "      <th>5/29/20</th>\n",
              "      <th>5/30/20</th>\n",
              "      <th>5/31/20</th>\n",
              "      <th>6/1/20</th>\n",
              "      <th>6/2/20</th>\n",
              "      <th>6/3/20</th>\n",
              "      <th>6/4/20</th>\n",
              "      <th>6/5/20</th>\n",
              "      <th>6/6/20</th>\n",
              "      <th>6/7/20</th>\n",
              "      <th>6/8/20</th>\n",
              "      <th>6/9/20</th>\n",
              "      <th>6/10/20</th>\n",
              "      <th>6/11/20</th>\n",
              "      <th>6/12/20</th>\n",
              "      <th>6/13/20</th>\n",
              "      <th>6/14/20</th>\n",
              "      <th>6/15/20</th>\n",
              "      <th>6/16/20</th>\n",
              "      <th>6/17/20</th>\n",
              "      <th>6/18/20</th>\n",
              "      <th>6/19/20</th>\n",
              "      <th>6/20/20</th>\n",
              "      <th>6/21/20</th>\n",
              "      <th>6/22/20</th>\n",
              "      <th>6/23/20</th>\n",
              "      <th>6/24/20</th>\n",
              "      <th>6/25/20</th>\n",
              "      <th>6/26/20</th>\n",
              "      <th>6/27/20</th>\n",
              "      <th>6/28/20</th>\n",
              "      <th>6/29/20</th>\n",
              "      <th>6/30/20</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Afghanistan</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>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>...</td>\n",
              "      <td>996</td>\n",
              "      <td>1040</td>\n",
              "      <td>1075</td>\n",
              "      <td>1097</td>\n",
              "      <td>1128</td>\n",
              "      <td>1138</td>\n",
              "      <td>1209</td>\n",
              "      <td>1259</td>\n",
              "      <td>1303</td>\n",
              "      <td>1328</td>\n",
              "      <td>1428</td>\n",
              "      <td>1450</td>\n",
              "      <td>1522</td>\n",
              "      <td>1585</td>\n",
              "      <td>1762</td>\n",
              "      <td>1830</td>\n",
              "      <td>1875</td>\n",
              "      <td>2171</td>\n",
              "      <td>2651</td>\n",
              "      <td>3013</td>\n",
              "      <td>3326</td>\n",
              "      <td>3928</td>\n",
              "      <td>4201</td>\n",
              "      <td>4725</td>\n",
              "      <td>5164</td>\n",
              "      <td>5508</td>\n",
              "      <td>6158</td>\n",
              "      <td>7660</td>\n",
              "      <td>7962</td>\n",
              "      <td>8292</td>\n",
              "      <td>8764</td>\n",
              "      <td>8841</td>\n",
              "      <td>9260</td>\n",
              "      <td>9869</td>\n",
              "      <td>10174</td>\n",
              "      <td>10306</td>\n",
              "      <td>10674</td>\n",
              "      <td>12604</td>\n",
              "      <td>13934</td>\n",
              "      <td>14131</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Albania</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>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>...</td>\n",
              "      <td>777</td>\n",
              "      <td>783</td>\n",
              "      <td>789</td>\n",
              "      <td>795</td>\n",
              "      <td>803</td>\n",
              "      <td>812</td>\n",
              "      <td>823</td>\n",
              "      <td>851</td>\n",
              "      <td>857</td>\n",
              "      <td>872</td>\n",
              "      <td>877</td>\n",
              "      <td>891</td>\n",
              "      <td>898</td>\n",
              "      <td>898</td>\n",
              "      <td>910</td>\n",
              "      <td>925</td>\n",
              "      <td>938</td>\n",
              "      <td>945</td>\n",
              "      <td>960</td>\n",
              "      <td>980</td>\n",
              "      <td>1001</td>\n",
              "      <td>1034</td>\n",
              "      <td>1039</td>\n",
              "      <td>1044</td>\n",
              "      <td>1055</td>\n",
              "      <td>1064</td>\n",
              "      <td>1077</td>\n",
              "      <td>1086</td>\n",
              "      <td>1114</td>\n",
              "      <td>1126</td>\n",
              "      <td>1134</td>\n",
              "      <td>1159</td>\n",
              "      <td>1195</td>\n",
              "      <td>1217</td>\n",
              "      <td>1250</td>\n",
              "      <td>1298</td>\n",
              "      <td>1346</td>\n",
              "      <td>1384</td>\n",
              "      <td>1438</td>\n",
              "      <td>1459</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Algeria</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>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>...</td>\n",
              "      <td>4256</td>\n",
              "      <td>4426</td>\n",
              "      <td>4784</td>\n",
              "      <td>4747</td>\n",
              "      <td>4918</td>\n",
              "      <td>5129</td>\n",
              "      <td>5277</td>\n",
              "      <td>5422</td>\n",
              "      <td>5549</td>\n",
              "      <td>5748</td>\n",
              "      <td>5894</td>\n",
              "      <td>6067</td>\n",
              "      <td>6218</td>\n",
              "      <td>6297</td>\n",
              "      <td>6453</td>\n",
              "      <td>6631</td>\n",
              "      <td>6717</td>\n",
              "      <td>6799</td>\n",
              "      <td>6951</td>\n",
              "      <td>7074</td>\n",
              "      <td>7255</td>\n",
              "      <td>7322</td>\n",
              "      <td>7420</td>\n",
              "      <td>7606</td>\n",
              "      <td>7735</td>\n",
              "      <td>7842</td>\n",
              "      <td>7943</td>\n",
              "      <td>8078</td>\n",
              "      <td>8196</td>\n",
              "      <td>8324</td>\n",
              "      <td>8422</td>\n",
              "      <td>8559</td>\n",
              "      <td>8674</td>\n",
              "      <td>8792</td>\n",
              "      <td>8920</td>\n",
              "      <td>9066</td>\n",
              "      <td>9202</td>\n",
              "      <td>9371</td>\n",
              "      <td>9674</td>\n",
              "      <td>9897</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Andorra</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>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>...</td>\n",
              "      <td>652</td>\n",
              "      <td>653</td>\n",
              "      <td>653</td>\n",
              "      <td>663</td>\n",
              "      <td>676</td>\n",
              "      <td>676</td>\n",
              "      <td>681</td>\n",
              "      <td>684</td>\n",
              "      <td>692</td>\n",
              "      <td>694</td>\n",
              "      <td>698</td>\n",
              "      <td>733</td>\n",
              "      <td>735</td>\n",
              "      <td>738</td>\n",
              "      <td>741</td>\n",
              "      <td>741</td>\n",
              "      <td>744</td>\n",
              "      <td>751</td>\n",
              "      <td>757</td>\n",
              "      <td>759</td>\n",
              "      <td>780</td>\n",
              "      <td>781</td>\n",
              "      <td>781</td>\n",
              "      <td>781</td>\n",
              "      <td>789</td>\n",
              "      <td>789</td>\n",
              "      <td>791</td>\n",
              "      <td>792</td>\n",
              "      <td>792</td>\n",
              "      <td>792</td>\n",
              "      <td>792</td>\n",
              "      <td>796</td>\n",
              "      <td>797</td>\n",
              "      <td>797</td>\n",
              "      <td>797</td>\n",
              "      <td>799</td>\n",
              "      <td>799</td>\n",
              "      <td>799</td>\n",
              "      <td>799</td>\n",
              "      <td>799</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Angola</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>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>...</td>\n",
              "      <td>17</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>18</td>\n",
              "      <td>18</td>\n",
              "      <td>18</td>\n",
              "      <td>18</td>\n",
              "      <td>18</td>\n",
              "      <td>21</td>\n",
              "      <td>24</td>\n",
              "      <td>24</td>\n",
              "      <td>38</td>\n",
              "      <td>38</td>\n",
              "      <td>40</td>\n",
              "      <td>41</td>\n",
              "      <td>42</td>\n",
              "      <td>61</td>\n",
              "      <td>61</td>\n",
              "      <td>64</td>\n",
              "      <td>64</td>\n",
              "      <td>64</td>\n",
              "      <td>64</td>\n",
              "      <td>66</td>\n",
              "      <td>66</td>\n",
              "      <td>77</td>\n",
              "      <td>77</td>\n",
              "      <td>77</td>\n",
              "      <td>77</td>\n",
              "      <td>81</td>\n",
              "      <td>81</td>\n",
              "      <td>81</td>\n",
              "      <td>81</td>\n",
              "      <td>93</td>\n",
              "      <td>93</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 163 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "  Province/State Country/Region  1/22/20  ...  6/28/20  6/29/20  6/30/20\n",
              "0            NaN    Afghanistan        0  ...    12604    13934    14131\n",
              "1            NaN        Albania        0  ...     1384     1438     1459\n",
              "2            NaN        Algeria        0  ...     9371     9674     9897\n",
              "3            NaN        Andorra        0  ...      799      799      799\n",
              "4            NaN         Angola        0  ...       81       93       93\n",
              "\n",
              "[5 rows x 163 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 108
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vFtgrHxpl3GY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "countires= list(corona_dataset_csv['Country/Region'])"
      ],
      "execution_count": 109,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P3R97YZYjvmf",
        "colab_type": "text"
      },
      "source": [
        "### Task 2.3: Aggregating the rows by the country"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mFdcMXQkjvmg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "corona_dataset_aggregated=corona_dataset_csv.groupby(\"Country/Region\").sum()"
      ],
      "execution_count": 110,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f3qZakqwjvmj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "63e8c71f-2baf-4797-de2d-601df3e5acb3"
      },
      "source": [
        "corona_dataset_aggregated.head()"
      ],
      "execution_count": 111,
      "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>1/22/20</th>\n",
              "      <th>1/23/20</th>\n",
              "      <th>1/24/20</th>\n",
              "      <th>1/25/20</th>\n",
              "      <th>1/26/20</th>\n",
              "      <th>1/27/20</th>\n",
              "      <th>1/28/20</th>\n",
              "      <th>1/29/20</th>\n",
              "      <th>1/30/20</th>\n",
              "      <th>1/31/20</th>\n",
              "      <th>2/1/20</th>\n",
              "      <th>2/2/20</th>\n",
              "      <th>2/3/20</th>\n",
              "      <th>2/4/20</th>\n",
              "      <th>2/5/20</th>\n",
              "      <th>2/6/20</th>\n",
              "      <th>2/7/20</th>\n",
              "      <th>2/8/20</th>\n",
              "      <th>2/9/20</th>\n",
              "      <th>2/10/20</th>\n",
              "      <th>2/11/20</th>\n",
              "      <th>2/12/20</th>\n",
              "      <th>2/13/20</th>\n",
              "      <th>2/14/20</th>\n",
              "      <th>2/15/20</th>\n",
              "      <th>2/16/20</th>\n",
              "      <th>2/17/20</th>\n",
              "      <th>2/18/20</th>\n",
              "      <th>2/19/20</th>\n",
              "      <th>2/20/20</th>\n",
              "      <th>2/21/20</th>\n",
              "      <th>2/22/20</th>\n",
              "      <th>2/23/20</th>\n",
              "      <th>2/24/20</th>\n",
              "      <th>2/25/20</th>\n",
              "      <th>2/26/20</th>\n",
              "      <th>2/27/20</th>\n",
              "      <th>2/28/20</th>\n",
              "      <th>2/29/20</th>\n",
              "      <th>3/1/20</th>\n",
              "      <th>...</th>\n",
              "      <th>5/22/20</th>\n",
              "      <th>5/23/20</th>\n",
              "      <th>5/24/20</th>\n",
              "      <th>5/25/20</th>\n",
              "      <th>5/26/20</th>\n",
              "      <th>5/27/20</th>\n",
              "      <th>5/28/20</th>\n",
              "      <th>5/29/20</th>\n",
              "      <th>5/30/20</th>\n",
              "      <th>5/31/20</th>\n",
              "      <th>6/1/20</th>\n",
              "      <th>6/2/20</th>\n",
              "      <th>6/3/20</th>\n",
              "      <th>6/4/20</th>\n",
              "      <th>6/5/20</th>\n",
              "      <th>6/6/20</th>\n",
              "      <th>6/7/20</th>\n",
              "      <th>6/8/20</th>\n",
              "      <th>6/9/20</th>\n",
              "      <th>6/10/20</th>\n",
              "      <th>6/11/20</th>\n",
              "      <th>6/12/20</th>\n",
              "      <th>6/13/20</th>\n",
              "      <th>6/14/20</th>\n",
              "      <th>6/15/20</th>\n",
              "      <th>6/16/20</th>\n",
              "      <th>6/17/20</th>\n",
              "      <th>6/18/20</th>\n",
              "      <th>6/19/20</th>\n",
              "      <th>6/20/20</th>\n",
              "      <th>6/21/20</th>\n",
              "      <th>6/22/20</th>\n",
              "      <th>6/23/20</th>\n",
              "      <th>6/24/20</th>\n",
              "      <th>6/25/20</th>\n",
              "      <th>6/26/20</th>\n",
              "      <th>6/27/20</th>\n",
              "      <th>6/28/20</th>\n",
              "      <th>6/29/20</th>\n",
              "      <th>6/30/20</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Country/Region</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Afghanistan</th>\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>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>...</td>\n",
              "      <td>996</td>\n",
              "      <td>1040</td>\n",
              "      <td>1075</td>\n",
              "      <td>1097</td>\n",
              "      <td>1128</td>\n",
              "      <td>1138</td>\n",
              "      <td>1209</td>\n",
              "      <td>1259</td>\n",
              "      <td>1303</td>\n",
              "      <td>1328</td>\n",
              "      <td>1428</td>\n",
              "      <td>1450</td>\n",
              "      <td>1522</td>\n",
              "      <td>1585</td>\n",
              "      <td>1762</td>\n",
              "      <td>1830</td>\n",
              "      <td>1875</td>\n",
              "      <td>2171</td>\n",
              "      <td>2651</td>\n",
              "      <td>3013</td>\n",
              "      <td>3326</td>\n",
              "      <td>3928</td>\n",
              "      <td>4201</td>\n",
              "      <td>4725</td>\n",
              "      <td>5164</td>\n",
              "      <td>5508</td>\n",
              "      <td>6158</td>\n",
              "      <td>7660</td>\n",
              "      <td>7962</td>\n",
              "      <td>8292</td>\n",
              "      <td>8764</td>\n",
              "      <td>8841</td>\n",
              "      <td>9260</td>\n",
              "      <td>9869</td>\n",
              "      <td>10174</td>\n",
              "      <td>10306</td>\n",
              "      <td>10674</td>\n",
              "      <td>12604</td>\n",
              "      <td>13934</td>\n",
              "      <td>14131</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Albania</th>\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>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>...</td>\n",
              "      <td>777</td>\n",
              "      <td>783</td>\n",
              "      <td>789</td>\n",
              "      <td>795</td>\n",
              "      <td>803</td>\n",
              "      <td>812</td>\n",
              "      <td>823</td>\n",
              "      <td>851</td>\n",
              "      <td>857</td>\n",
              "      <td>872</td>\n",
              "      <td>877</td>\n",
              "      <td>891</td>\n",
              "      <td>898</td>\n",
              "      <td>898</td>\n",
              "      <td>910</td>\n",
              "      <td>925</td>\n",
              "      <td>938</td>\n",
              "      <td>945</td>\n",
              "      <td>960</td>\n",
              "      <td>980</td>\n",
              "      <td>1001</td>\n",
              "      <td>1034</td>\n",
              "      <td>1039</td>\n",
              "      <td>1044</td>\n",
              "      <td>1055</td>\n",
              "      <td>1064</td>\n",
              "      <td>1077</td>\n",
              "      <td>1086</td>\n",
              "      <td>1114</td>\n",
              "      <td>1126</td>\n",
              "      <td>1134</td>\n",
              "      <td>1159</td>\n",
              "      <td>1195</td>\n",
              "      <td>1217</td>\n",
              "      <td>1250</td>\n",
              "      <td>1298</td>\n",
              "      <td>1346</td>\n",
              "      <td>1384</td>\n",
              "      <td>1438</td>\n",
              "      <td>1459</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Algeria</th>\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>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>...</td>\n",
              "      <td>4256</td>\n",
              "      <td>4426</td>\n",
              "      <td>4784</td>\n",
              "      <td>4747</td>\n",
              "      <td>4918</td>\n",
              "      <td>5129</td>\n",
              "      <td>5277</td>\n",
              "      <td>5422</td>\n",
              "      <td>5549</td>\n",
              "      <td>5748</td>\n",
              "      <td>5894</td>\n",
              "      <td>6067</td>\n",
              "      <td>6218</td>\n",
              "      <td>6297</td>\n",
              "      <td>6453</td>\n",
              "      <td>6631</td>\n",
              "      <td>6717</td>\n",
              "      <td>6799</td>\n",
              "      <td>6951</td>\n",
              "      <td>7074</td>\n",
              "      <td>7255</td>\n",
              "      <td>7322</td>\n",
              "      <td>7420</td>\n",
              "      <td>7606</td>\n",
              "      <td>7735</td>\n",
              "      <td>7842</td>\n",
              "      <td>7943</td>\n",
              "      <td>8078</td>\n",
              "      <td>8196</td>\n",
              "      <td>8324</td>\n",
              "      <td>8422</td>\n",
              "      <td>8559</td>\n",
              "      <td>8674</td>\n",
              "      <td>8792</td>\n",
              "      <td>8920</td>\n",
              "      <td>9066</td>\n",
              "      <td>9202</td>\n",
              "      <td>9371</td>\n",
              "      <td>9674</td>\n",
              "      <td>9897</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Andorra</th>\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>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>...</td>\n",
              "      <td>652</td>\n",
              "      <td>653</td>\n",
              "      <td>653</td>\n",
              "      <td>663</td>\n",
              "      <td>676</td>\n",
              "      <td>676</td>\n",
              "      <td>681</td>\n",
              "      <td>684</td>\n",
              "      <td>692</td>\n",
              "      <td>694</td>\n",
              "      <td>698</td>\n",
              "      <td>733</td>\n",
              "      <td>735</td>\n",
              "      <td>738</td>\n",
              "      <td>741</td>\n",
              "      <td>741</td>\n",
              "      <td>744</td>\n",
              "      <td>751</td>\n",
              "      <td>757</td>\n",
              "      <td>759</td>\n",
              "      <td>780</td>\n",
              "      <td>781</td>\n",
              "      <td>781</td>\n",
              "      <td>781</td>\n",
              "      <td>789</td>\n",
              "      <td>789</td>\n",
              "      <td>791</td>\n",
              "      <td>792</td>\n",
              "      <td>792</td>\n",
              "      <td>792</td>\n",
              "      <td>792</td>\n",
              "      <td>796</td>\n",
              "      <td>797</td>\n",
              "      <td>797</td>\n",
              "      <td>797</td>\n",
              "      <td>799</td>\n",
              "      <td>799</td>\n",
              "      <td>799</td>\n",
              "      <td>799</td>\n",
              "      <td>799</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Angola</th>\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>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>...</td>\n",
              "      <td>17</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>18</td>\n",
              "      <td>18</td>\n",
              "      <td>18</td>\n",
              "      <td>18</td>\n",
              "      <td>18</td>\n",
              "      <td>21</td>\n",
              "      <td>24</td>\n",
              "      <td>24</td>\n",
              "      <td>38</td>\n",
              "      <td>38</td>\n",
              "      <td>40</td>\n",
              "      <td>41</td>\n",
              "      <td>42</td>\n",
              "      <td>61</td>\n",
              "      <td>61</td>\n",
              "      <td>64</td>\n",
              "      <td>64</td>\n",
              "      <td>64</td>\n",
              "      <td>64</td>\n",
              "      <td>66</td>\n",
              "      <td>66</td>\n",
              "      <td>77</td>\n",
              "      <td>77</td>\n",
              "      <td>77</td>\n",
              "      <td>77</td>\n",
              "      <td>81</td>\n",
              "      <td>81</td>\n",
              "      <td>81</td>\n",
              "      <td>81</td>\n",
              "      <td>93</td>\n",
              "      <td>93</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 161 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                1/22/20  1/23/20  1/24/20  ...  6/28/20  6/29/20  6/30/20\n",
              "Country/Region                             ...                           \n",
              "Afghanistan           0        0        0  ...    12604    13934    14131\n",
              "Albania               0        0        0  ...     1384     1438     1459\n",
              "Algeria               0        0        0  ...     9371     9674     9897\n",
              "Andorra               0        0        0  ...      799      799      799\n",
              "Angola                0        0        0  ...       81       93       93\n",
              "\n",
              "[5 rows x 161 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 111
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kyS-FnT1l1Yt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "corona_dataset_aggregated.to_csv(\"Aggregated_Recovered.csv\")"
      ],
      "execution_count": 112,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TkVkKdkljvmm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "b960563b-c0a4-4c5a-cec0-a58af60f4e63"
      },
      "source": [
        "corona_dataset_aggregated.shape"
      ],
      "execution_count": 113,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(188, 161)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 113
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JmTzvmiqjvmq",
        "colab_type": "text"
      },
      "source": [
        "### Task 2.4: Visualizing data related to a country for example China\n",
        "visualization always helps for better understanding of our data."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iuj6gxb3jvmq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "d72f095e-7324-4e68-e38a-6b95c2abacb1"
      },
      "source": [
        "corona_dataset_aggregated.loc[\"China\"].plot()\n",
        "corona_dataset_aggregated.loc[\"Italy\"].plot()\n",
        "corona_dataset_aggregated.loc[\"Spain\"].plot()\n",
        "corona_dataset_aggregated.loc['India'].plot()\n",
        "plt.legend()\n",
        "plt.xlabel(\"Date\")\n",
        "plt.ylabel(\"Number of Cases\")\n"
      ],
      "execution_count": 114,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Number of Cases')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 114
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_Wg9LY_tpBKV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "86562c68-88a4-40f1-e4fe-4039f70065f5"
      },
      "source": [
        "\n",
        "\n",
        "corona_dataset_aggregated.loc[\"China\"].plot()\n",
        "corona_dataset_aggregated.loc[\"Italy\"].plot()\n",
        "corona_dataset_aggregated.loc[\"Spain\"].plot()\n",
        "corona_dataset_aggregated.loc['India'].plot()\n",
        "corona_dataset_aggregated.loc['Brazil'].plot()\n",
        "plt.legend()\n",
        "plt.xlabel(\"Date\")\n",
        "plt.ylabel(\"Number of Cases\")"
      ],
      "execution_count": 115,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Number of Cases')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 115
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Zj4+vF9HDO97DnPXcmmxEJExE3hWRJBHZIyLTRSRCRFaKSLL9Hm7XFRF5XkRSRGSHiExqcJ4Fdv1kEVnQoHyyiHxvH/O8vTw0zbWhlFJdjTGGQzvziBkZjo9vx3u+pp67ezbPAZ8aY0YCE4A9wEJglTFmGLDK/gwwFxhmv24HXgIrcQCPAFOBKcAjDZLHS8BtDY6bY5c310anExwcfEL1165dy8UXXwzo0gJKdQdF2RWU5FV16Eto4MZkIyI9gbOAfwEYY2qMMUXAJUD94+1LgEvt7UuApcayAQgTkSjgAmClMabAGFMIrATm2PtCjTEb7CWllzY6V1NtdCvz5s1j4cJOm2eVUi5I3ZEPwMAx3TTZAHFALvCaiGwVkVdEpAfQ1xiTadfJAvra2/2BtAbHp9tlLZWnN1FOC20cQ0RuF5FEEUnMzc09me/YbtauXcvMmTO54oorGDlyJNddd93Rec0+/fRTRo4cyaRJk3jvvfeOHtNwaYHmlh9QSnVetdV1bFt1mH6DQzvckgKNufM5Gx9gEnC3MWajiDxHo8tZxhgjIm6dwqClNowxi4BFYM0g0NJ5sv70J6r3tO0SA/6jRtLvoYdcrr9161Z27dpFdHQ0Z5xxBl9//TUJCQncdtttrF69mqFDhzJ//vwmj3V1+QGlVOex7YvDVBTXMOf2cZ4OpVXu7NmkA+nGmPp1jN/FSj7Z9iUw7Pcce38GMKDB8TF2WUvlMU2U00IbndqUKVOIiYnBy8uL+Ph4UlNTSUpKIi4ujmHDhiEiXH/99U0em56ezgUXXMC4ceP461//yq5du9o5eqVUWyovrmbL54cZMqk3UR101oCG3NazMcZkiUiaiIwwxuwFzgN2268FwJP2+4f2IcuBu0RkGdZggGJjTKaIfAb8qcGggNnAg8aYAhEpEZFpwEbgRuCFBudqqo2TdiI9EHdpuDSAt7c3DofD5WNdXX5AKdU5fPV2Mk6Hk+k/HuLpUFzi7ulq7gb+LSJ+wAHgZqze1NsicgtwCLjKrvsxcCGQAlTYdbGTyh+ATXa9x40xBfb2z4HFQCDwif0CK8k01UaXM3LkSFJTU9m/fz9Dhgw5OgN0Y7r8gFJdR8rmHFI25zB13mB69g7ydDgucWuyMcZsA5qaDfS8Juoa4M5mzvMq8GoT5YnA2CbK85tqoysKCAhg0aJFXHTRRQQFBTFjxgxKS0uPq6fLDyjVNVSW1bBu2V56Dwxh0gUDPR2Oy3SJAVtnWmKgvXT3769UR7Tls0N8+/5+5j88hciYE3sOzx10iQGllOqCUnfk0XtgSIdINCdCk41SSnUSlaU1ZB4oJnZcx36AsymabJRSqpM4tDMfDMSOd+8qve6gyUYppTqJ1B159OjpR++BIZ4O5YRpslFKqU6grtbJ4d0FxI6PxJ7gvlNpNdmIyFMiEioiviKySkRyRaTpx9SVUkq5xaFd+dRW13XKS2jgWs9mtjGmBLgYSAWGAr9xZ1DqB97e3sTHxzNhwgQmTZrEN9980ybnvfXWW9m9ezcAsbGx5OXltcl5lVLusWN1GsHh/gzowEs/t8SVhzrr61wEvGOMKe6MXbjOKjAwkG3btgHw2Wef8eCDD/Lll18eU8fhcODjc2LP577yyittFqNSyr1yD5eSsa+I0y8bird357z74UrUK0QkCZgMrBKR3kCVe8NSTSkpKSE83Joibu3atcyYMYN58+YxevRoAC699FImT57MmDFjWLRoEWAtoBYfH098fDwjRowgLi4OgJkzZ9L4IValVMe0fVUavv7ejD4zytOhnLRW/zlsjFkoIk9hTYxZJyIVWIuTdSvr395HXlpZm54zckAwM64a3mKdyspK4uPjqaqqIjMzk9WrVx/dt2XLFnbu3Hk0gbz66qtERERQWVnJaaedxuWXX868efOYN28eAFdddRVnn312m34HpZR7lRZUkbwpm7Fn98c/yNfT4Zw0VwYIBGFNePmSXRRN0/OdKTeov4yWlJTEp59+yo033nh00bQpU6YcTTQAzz//PBMmTGDatGmkpaWRnJx8dN9TTz1FYGAgd97Z5PRzSqkOasOH+xEvIf78zjMPWlNcudD/GrAZON3+nAG8A6xwV1AdUWs9kPYwffp08vLyqF9VtEePHkf3rV27li+++IJvv/2WoKAgZs6cSVWVdbXziy++4J133mHdunUeiVspdXJyDpWwb2M2k+YMIiQiwNPhnBJX7tkMMcY8BdQCGGMqAB0h4AFJSUnU1dXRq9fxU1UUFxcTHh5OUFAQSUlJbNiwAYBDhw5x55138s477xAY2LGXjVVK/cAYw1fvJBMY4svkCwZ5OpxT5krPpkZEAgEDICJDgGq3RqWOqr9nA9b/fEuWLMHb2/u4enPmzOF//ud/GDVqFCNGjGDatGkALF68mPz8fC699FIAoqOj+fjjj9vvCyilTkpRdgWZKcWcccVQ/ALdvfSY+7W6xICInA88DIwGPgfOAG4yxqx1e3TtSJcYOF53//5KedKu9Rms/fderntsGmF9O+4CaW22xIAxZiVwGXAT8CaQ4GqiEZFUEfleRLaJSKJdFiEiK0Uk2X4Pt8tFRJ4XkRQR2SEikxqcZ4FdP1lEFjQon2yfP8U+VlpqQymlOouMfUUEhfrRs0/XuPztymi0M4AqY8x/gTDgIRE5kQuI5xhj4htkvoXAKmPMMGCV/RlgLjDMft2OPfpNRCKAR4CpwBTgkQbJ4yXgtgbHzWmlDaWU6vCMMRxJLiJ6eFinnAetKa4MEHgJqBCRCcC9wH5g6Sm0eQmwxN5eAlzaoHypsWwAwkQkCrgAWGmMKTDGFAIrgTn2vlBjzAZ7Semljc7VVBsnrLuuZNpdv7dSHUFJXhXlRdVEDw3zdChtxpVk47B/zC8B/mGM+Qfg6vzWBvhcRDaLyO12WV9jTKa9nQX0tbf7A2kNjk23y1oqT2+ivKU2TkhAQAD5+fnd7ofXGEN+fj4BAZ17qKVSndWR5EIAood3nWTjyhCHUhF5ELgeOEtEvABXH2M90xiTISJ9gJX2tDdHGWOMiLj1l7ylNuwEeDvAwIHHPzAVExNDenr60edaupOAgABiYmI8HYZS3dKR5CICevgS0a9H65U7CVeSzXzgWuAWY0yWiAwE/urKyY0xGfZ7joi8j3XPJVtEoowxmfalsBy7egYwoMHhMXZZBjCzUflauzymifq00Ebj+BYBi8AajdZ4v6+v7zFP6CullLsZpyFjXxHRw8IQr65xvwZcG42WZYz5mzFmvf35sDGm1Xs2ItJDRELqt4HZwE5gOVA/omwB8KG9vRy40R6VNg1rLrZM4DNgtoiE2wMDZgOf2ftKRGSaPQrtxkbnaqoNpZTq0PZ8m0lpfhWDJ/b2dChtqtWejf3D/wIwCvADvIEyY0zPVg7tC7xvj6TwAd4wxnwqIpuAt0XkFuAQcJVd/2PgQiAFqABuBjDGFIjIH4BNdr3HjTEF9vbPgcVAIPCJ/QJ4spk2lFKqw6osreGb91KIHhbG8Ckndau5w3LlMtqLwNVY86ElYPUgWp0ozBhzAJjQRHk+cF4T5QZocpZIY8yrwKtNlCcCY11tQymlOipjDF+9m0xtZR1nXzOiywx5rufSKjzGmBTA2xhTZ4x5jR+eZ1FKKdUGtn5+2Jp0c+4gIqK7zsCAeq70bCpExA/YZq9rk4mLSUoppVTr9m3K4tv39zMsoQ9TLuqag5JcSRo32PXuAsqxRoxd7s6glFKqu8g9XMrqJUlEDwvjvAWju9QItIaa7dnYyz/3NsbstouqgMdEZAxQ3B7BKaVUV1ZVXssn//s9gSG+zLl9LN6+XfeiUUvf7AUgsonyCOA594SjlFLdx5dv7KW8uJo5t48jMMTP0+G4VUvJZqgx5rilHe3nbca7LySllOr6slNLSNmcw+QLBtE3LtTT4bhdS8mmpfnPXJ2uRimlVBM2fLCfgGBf4s8/fqqsrqilZJMiIhc2LhSRucAB94WklFJdW1pSAelJhSTMjcUvoPOvwumKlr7lL4H/ishVwGa7LAGYDlzs7sCUUqorMsbw3fKDBIf7M+asaE+H026a7dkYY5KBccCXQKz9+hIYb4zZ1x7BKaVUV5Oxr4isA8VMumAQPr7eng6n3bTYfzPGVAOvtVMsSinV5SV+fJCgUD9GnRHl6VDaVdcd1K2UUh1MZkoRGXuLmDh7YLfq1YAmG6WUajeJn6QSEOzLmBn9W6/cxTSbbERklf3+l/YLRymluqacQyUc3lVA/KwB+Pp3r14NtHzPJkpETgfmicgy4JgJe4wxW9wamVJKdSGJH6fiH+TDuLO753LrLSWb3wO/w1pu+W+N9hngXHcFpZRSXUleehkHt+dx2kWx+AV2j+dqGmtp6PO7xpi5wFPGmHMavVxONCLiLSJbRWSF/TlORDaKSIqIvGUvX4CI+NufU+z9sQ3O8aBdvldELmhQPscuSxGRhQ3Km2xDKaU8YdsXh/Hx92b8uQM8HYrHtDpAwBjzBxGZJyJP268TfaDzF8CeBp//AjxrjBkKFAK32OW3AIV2+bN2PURkNNZKoWOwFm37p53AvIF/AHOB0cA1dt2W2lBKqXZVXlxN8qZsRk2PIqBH953pq9VkIyJ/xkoYu+3XL0TkT66cXERigIuAV+zPgnX57V27yhLgUnv7Evsz9v7z7PqXAMuMMdXGmINACjDFfqUYYw4YY2qAZcAlrbShlFLtaueXGTidhvHnds97NfVcuXh4ERBvjHECiMgSYCvwkAvH/h24nx8m9ewFFBljHPbndKB+DGB/IA3AGOMQkWK7fn9gQ4NzNjwmrVH51FbaUEqpduOoqWPnugxix0US1ifI0+F4lKvP2YQ12O7pygH25bYcY8zmVit7iIjcLiKJIpKYm5vr6XCUUl3Mvk3ZVJXVEn9e971XU8+Vns2fga0isgZr+PNZwMKWDwHgDKxh0xcCAUAo1qJrYSLiY/c8YoAMu34G1pLT6SLig5XU8huU12t4TFPl+S20cQxjzCJgEUBCQoJx4TsppZTLdq3LICK6B9HDw1qv3MW5MkDgTWAa8B7wH2C6MeYtF4570BgTY4yJxbrBv9oYcx2wBrjCrrYA+NDeXm5/xt6/2hhj7PKr7dFqccAw4DtgEzDMHnnmZ7ex3D6muTaUUqpd5BwqIedQKWNm9Me6ldy9uTTg2xiTifWj3xYeAJaJyB+x7v38yy7/F/C6iKQABVjJA2PMLhF5G2twggO40xhTByAidwGfAd7Aq8aYXa20oZRS7WLnlxn4+HkxYlo/T4fSIYjVEVAJCQkmMTHR02EopbqA6opaFj/wNcOn9uOc60d6Ohy3EpHNxpiE1urpRJxKKdXG8o+U46h1Mji+t6dD6TBaTDb2w5NJ7RWMUkp1BVWltQAEherkJfVaTDb2vZG9IjKwneJRSqlOr7KsBoCA4O47Y0BjrgwQCAd2ich3QHl9oTFmntuiUkqpTqyyzOrZBGqyOcqVZPM7t0ehlFJdSFVZLT7+3vj4db91a5rTarIxxnwpIoOAYcaYL0QkCGuosVJKqSZUltVor6YRVybivA1rUsv/tYv6Ax+4MyillOrMqkprO0WyMU4nNYcPt0tbrgx9vhNr6pkSAGNMMtDHnUEppVRnVllWS0Bwxx+JVrJiBfsvvIjKHTvc3pYryabansIfAHveMn0SVCmlmtEZLqM5y8vJefoZAkaNImDsWLe350qy+VJEHgICReR84B3gI/eGpZRSnVdVWS0BIR072eQtehlHTg59H3oQ8XL/8/2utLAQyAW+B34KfAw87M6glFKqs6qtqcNR4+zQPZuatDQKXnuN0Hk/ImjixHZp05XRaE57wbSNWJfP9hqdUE0ppZpUWWrddQjswPdscp76K/j40Oe++9qtTVdGo10E7AeeB14EUkRkrrsDU0qpzqjKfqCzo84eUL5hA6UrVxJ5++349u3bbu268lDnM8A5xpgUABEZAvwX+MSdgSmlVGfUkWcPMA4H2U/8Cd+YGCJuvqld23Yl2ZTWJxrbAaDUTfEopVSnVt+zCQzpeJfRCpa+TnVyMv1feB4vf/92bbvZZCMil9mbiSLyMfA21j2bK7FWyVRKKdVI/T2bjnYZrfg/sJEAACAASURBVCY9g9wXXiD4nHMImTWr3dtvqWfzowbb2cDZ9nYuEOi2iJRSqhOrLKtFvAT/QJcWQm4XxhiyHn8MEaHf73/nkWWqm/3TMMbcfConFpEAYB3gb7fzrjHmERGJA5YBvYDNwA3GmBoR8QeWApOBfGC+MSbVPteDwC1AHXCPMeYzu3wO8BzWXG2vGGOetMubbONUvo9SSrmiqqyWgGBfxKv9f9CbU/bll5SvW0+fhQ/gGxXlkRhcGY0WJyJ/E5H3RGR5/cuFc1cD5xpjJgDxwBwRmQb8BXjWGDMUKMRKItjvhXb5s3Y9RGQ0cDUwBpgD/NNe1M0b+AcwFxgNXGPXpYU2lFLKrSpLO9bsAcbhIOfpp/EdNJCIa6/1WByuPNT5AZAKvIA1Mq3+1SJjKbM/+tovA5yLNbEnwBLgUnv7Evsz9v7zxOrrXQIsM8ZUG2MOAinAFPuVYow5YPdalgGX2Mc014ZSSrlVVXnHmoSz6P33qUnZT59770P8PDdowZWLilXGmOdP5uR272MzMBSrF7IfKDLGOOwq6VizSGO/pwEYYxwiUox1Gaw/sKHBaRsek9aofKp9THNtNI7vduB2gIEDdTFSpdSpqyytpVf/YE+HAYCjoIDc554ncMIEQmaf79FYXEk2z4nII8DnWJfGADDGbGntQHtZ6XgRCQPeB0aebKDuYIxZBCwCSEhI0FkRlFKnrKNMwmmcTo4sXIizpIR+Ly/yyKCAhlxJNuOAG7AuTTntsvrLYS4xxhSJyBpgOhAmIj52zyMGyLCrZQADgHR7ZumeWAMF6svrNTymqfL8FtpQSim3cdY5qa5wdIhJOAteW0z5uvX0/f3vCBg1ytPhuHTP5kpgsDHmbGPMOfar1UQjIr3tHg0iEgicD+wB1gBX2NUWAB/a28vtz9j7V9tzsC0HrhYRf3uU2TDgO6xnfYbZAxj8sAYRLLePaa4NpZRym0O7CsBAkIcf6KzNzCT3+ecJOX8W4ddc49FY6rnSs9kJhAE5J3juKGCJfd/GC3jbGLNCRHYDy0Tkj8BW4F92/X8Br4tIClCAlTwwxuwSkbeB3YADuNO+PIeI3AV8hjX0+VVjzC77XA8004ZSSrlF0oZM1ixNInJAMMMS2m/OsabkvvAiOJ30XbjQ45fP6klrEziLyFpgPFZPouE9m3lujaydJSQkmMTERE+HoZTqhNKTCvjwuW30Hx7OhXeMw8+DD3RW7d3HwUsvJeKmm+j7wP1ub09ENhtjElqr58qfyCNtEI9SSnVJFSU1rHx1N+F9g7jwZ+PwC/DszAE5Tz+NV0gIkT+93aNxNObKejZftkcgSinV2RhjWLV4N9WVDn50T7zHE03p2rWUr19Pn/vvxzsszKOxNNbqn4yIlGKNPgPww3o4s9wYE+rOwJRSqqPbuyGLw7sLOOvq4UTGePbZGlNTQ86fn8QvLo6I66/zaCxNcaVnE1K/3eCJ/mnuDEoppTq6ytIavno3mX6DezL2rCafG29X+UuWUHPoEANeXuTRmQKa48rQ56PsKWg+AC5wUzxKKdUpfPOfFGqr6ph5/QiPT7pZtXcvec+/QMj5swieMcOjsTTHlctolzX46AUkAFVui0gppTq44twK9m7MYsJ5A+gV7dnLZ87qao78+jd49exJv8ce82gsLXHlblbDdW0cWJNyXuKWaJRSqhPYtjIN8Rbiz/f8nIq5f/sb1cnJDFj0v/hERHg6nGa5cs/mlNa1UUqprqSipIY932Yycmo/evRs36WVGyv76msKliwl/LrrCD7rLI/G0pqWloX+fQvHGWPMH9wQj1JKdWg71qRR53AycfYgj8bhKCwk88EH8Rs6hD6/+bVHY3FFSz2b8ibKemAtRNYL0GSjlOpWqspr+X5NOkPiexPWN8hjcZi6Oo785n4cRUXELfpfvAICPBaLq1paFvroAmkiEgL8ArgZa5GyVhdPU0qprmbbF4epqarjtIvjPBpH7t+fo/yrr+j32GMdYkZnV7R4z0ZEIoB7geuwVrycZIwpbI/AlFKqI6kqq2XH6nSGTu7j0cXRytatI//llwm76irC51/lsThOVEv3bP4KXIa1uNi4Bks8K6VUt7Pls0PU1tRx2kWe69UYh4PsvzyFX2wsfR/+rcfiOBktPdR5HxANPAwcEZES+1UqIiXtE55SSnleQWY521elMXJaPyKie3gsjqL33qNm/35633cvXh1wloCWtHTP5oRmF1BKqa7IGMO6ZXvxDfBm+o+HeiwOZ0UFeS+8SODEiYTMmuWxOE6WJhSllGpGXZ2TTf9NJWNvEdMuGUxQqGd6E8YYjvz2tzjy8ujzm990mAXRToTbko2IDBCRNSKyW0R2icgv7PIIEVkpIsn2e7hdLiLyvIikiMgOEZnU4FwL7PrJIrKgQflkEfnePuZ5e6LQZttQSilXZR0s5q0/fMemFQcZPLE3o2d4brLNvH/8k9JPPqXPr+8jaNJEj8VxKtzZs3EA9xljRmPNEn2niIwGFgKrjDHDgFX2Z4C5wDD7dTvwEhwdEfcIMBWYAjzSIHm8BNzW4Lg5dnlzbSilVIuMMWz74jDv/3ULtTV1XPjz8cy5fSxeHppss3TtWvJefJGel15KxE9+4pEY2oLbko0xJtMYs8XeLgX2AP2x5lVbYldbAlxqb18CLLVnlt4AhIlIFNYM0yuNMQX2sOuVwBx7X6gxZoOx1rZe2uhcTbWhlFIt2rEmna/fTWHQuF7M/+0U4sZHeuyyVW12DpkPPoT/yJH0e+zRTnn5rF67LCsnIrHARGAj0NcYk2nvygL62tv9gbQGh6XbZS2VpzdRTgttNI7rdqxeFAMHen5CPaWUZxXnVrLhg/0MGtuLuXeM8+iPu6mp4cj99+OsqqL/3/6Gl79n52E7VW5PNiISDPwH+KUxpqThfzxjjBER0+zBbaClNowxi7CeIyIhIcGtcSilOjZjDGv/nYR4CWdfO8KjicZZWUn6L35BxcaNRD35Z/wHn+KzPU4nlGVDdSnUlkNtpbVdeAgKDsA5D0JAz7YJvhluTTYi4ouVaP5tjHnPLs4WkShjTKZ9KSzHLs8ABjQ4PMYuywBmNipfa5fHNFG/pTaUUqpJe77OJD2pkLOvHUFIhOfmGqsrKyPtjjuo3LyFfn94nLBLXbwL4Ki2kkfhQSuBFBz8YbvoMNTVNH2cbxBMurHzJht7ZNi/gD3GmL812LUcWAA8ab9/2KD8LhFZhjUYoNhOFp8Bf2owKGA28KAxpsB+yHQa1uW5G4EXWmlDKaWOU1ZYzdfvJhM9LIwxZ0Z7LA5HYSFpt91OVVIS/Z95mtALL7R6JaVHIGcP5O6FygKoLIKqoh/eS7OhJANocIHGLwQi4qDvGBh5EYQNhIAwK7n4BYFvD6ssuA+0Qy/OnT2bM4AbgO9FZJtd9hBWAnhbRG4BDgH1k/t8DFwIpAAVWJN+YieVPwCb7HqPG2MK7O2fA4uBQOAT+0ULbSil1DGMMXz55l6cdYZzbhjpmSWe6xw48w5z+Cc/p+ZwBjE3JxBSuBhefMTqrdRV/1BXvCEwzEoc9e+9hlmJJTwOIgZb20G92iWJuEqsgVwqISHBJCYmejoMpVQ7S9qQyarFezj98qFMbK+VN8tyIO07SP8O0jbBka1kfuNH0YEeDDgrn+CBXhA5FMJjf3j1GQ29R0JgeMdKIiKbjTEJrdVrl9FoSinVEZUWVLF+2T6ihvZkwnkDWj/gRBlj3TPJ3gU5uyF7J2Rut+6hAHj5QtQEimUWRQc20+uycwj+5d0QORy8vNs+Hg/SZKOU6pbq6pysWrwbY2DWTaNP7aHNmgrI22fdNynNsl5Fh+HgOut+CwACvYZA9CSY8lOIOQ2iJlC2cTOZL/ycwEmT6P3Yc+Dr2ybfr6PRZKOU6naM07Bq8R4y9hVx3oJRhEYGunZgbRUUp0NxmvU6shVSv7YSTcOb8wgE94WB02Dw2RAVb10C8zt2dc/yb78l/c478Rs8mAH//AfSRRMNaLJRSnUztTV1rFu2j+RN2Uy7dDAjp0cdX8kYyN9vJZOSdMhLhsMboGD/sfX8Q62EMubH0GcUhA+C4H7Qozd4t/zzWv7dd6T97Of4DRzIwNdexTssrA2/ZcejyUYp1S0YYzi0M5/1b+2jJK+KhAtjmTwntmEFK7nseAt2fwilmT/sC4qEAVNgwtXWcOGeMdYrNKbVpNKUis2bSbvjZ/j278/Axa/hE9715wrW0Wg2HY2mVOeRU5HDe8nvUVFb4VJ9yQnCZ2M0XtnBOHtW4TgzDRNVbu2sLLBu2hccgOoy8PKyhhCHDYLQKKv34t12SwtE7khj0t9XUtkrmO8eupjq8KDWD3KzBWMW0Cuw10kdq6PRlFKdnjGGnIocduXvYk/BHspryymtKeWTg59QU1eDv3fr84X1LxjBOUk3UOlTwY4h75HcNxFnsQMK68DUgbMOEAj0hqAIexRYKRTttF5taOx+B/e+VUV6by/+Or+WkuwPIbtNmzgpPx7245NONq7SZKOU6lAqaiv4+sjXfJ76OYnZieRV5gHgJV4E+gTihRcXxF7AHRPuYEBIy8OV92/J4fN/7aJX/x786LoBBBZVwt4iSF4JjioIiYJJC2DK7dDDvT+2dWVlHLjoYryHxHD+v//NnNBQt7bX0WiyUUp5TK2zluzybPYX7Wdz9mY2Z29md/5uHMZBREAEZ0SfwZjIMYzpNYYRESMI9HFx1JjTyd5PN7Lqowr69jjCxeZx/BfbUyQG97USzJgfw4Cp1mWzdpD79+dw5OQQ89zf8e5miQY02Sil2lleZR6rD69m5aGVJGYl4jAOAHy9fBkXOY6bx97MtKhpTOo7CR+vE/iJqiiA/ash+XP2bi3ji9yf0N9vJxcOXoHfoHnW8y39J7XrA5M1qankL1mCIzeXslWrCb/2WgLj49ul7Y5Gk41Syu2yyrNYdXgVn6d+ztacrRgMg0IHcd2o6xgcNphBoYMY02sMAT4nONtyVQmkrIRtb8L+VWCcZHslsDpvIf3713Hx3dfjE/ZL93ypFhhjKHr3XbL//CQYg19MDCGzZ9P7V+0fS0ehyUYp5RZppWl8cegLvjj0BTvydgAwNGwod0y4g1mDZjEsbNiJrxljDGTtsO657F8NaRvB6YDQ/nDGLymPuZBPFlfTI8KLOb86DZ/g9n9I0lFYSObvfkfZF6sImjaN6Cf/jG+/fu0eR0ejyUYp1WYOFB84mmD2FOwBYFTEKO6ZeA+zBs0irudJLAJmDGRsht0fWM+/1M8r1m8cnH43DJ0FA6dTZ4TPnt1KdbmDyx+YTIAHEk3Z+q848tCDOIuK6XP//UTctABpp3tCHZ0mG6XUSasfObY9ZztfZXzF/mLrCfvxvcdz3+T7mDVoFjEhMa2cpQVp38HyeyB3jzVp5eCZcNZvYNgFEHLsau/fvLWPzJRizr9lNJExISff5klwVlWR88zfKHz9dfyGDmHgyy8TMHJku8bQ0WmyUUqdlO8yv+Phrx8mszwTXy9fJvSewMIRCzlv4Hn063EKl42cTjj0NWz7N2xfZj2pf8k/rAXAApt+0n7vxix2rElnwnkDGH5a+16yqkpK4shvfkN1cgrh119Pn1/fh1eA51b67Kg02SilXLItZxvL9i7jm4xvqDN1lNSUEBsay6LzFzG572T8TvUp+5oK+P5t+OYFyE8Bv2CY9jM45yHwb76nknu4lDX/l0T/4WGcftmQU4vhBNSVlZP3z39SsHQp3mFhDHh5EcEzZrRb+52NO5eFfhW4GMgxxoy1yyKAt4BYIBW4yhhTaC8h/RzWSp0VwE3GmC32MQuAh+3T/tEYs8Qun8wPq3R+DPzCGGOaa8Nd31Oprs7hdPDUpqd4M+lNgn2DOXfguQT7BtM7qDfXjbrO9WdfGqutsu7FZH0Ph7+xbvrXVlgzJF/2Moy8+LhZkhsrL6rmk//9nsBgX2bfOhYvb/ffHzHGUPrpp2Q/+Rcc2dn0vOJy+tx3X7eY3+xUuLNnsxh4EVjaoGwhsMoY86SILLQ/PwDMBYbZr6nAS8BUO3E8AiRgzd+9WUSW28njJeA2YCNWspmDtSx0c20opU5Adnk2W3O38p99/2FD5gZuGH0Dd8XfRZDvKc7ldXA9fPuitdZL/dxmIVEw4RoYexkMOsOllShzDpXw8T93UFNVxyW/mkhQaNvNX9ac8o3fkffCC1QkJuI/ehT9//4sQRMnur3drsBtycYYs05EYhsVXwLMtLeXAGuxEsElwFJjzQq6QUTCRCTKrrvSGFMAICIrgTkishYINcZssMuXApdiJZvm2lBKtSK7PJvPD33Opwc/PTpcOdAnkEemP8IVw684+RPX1Vo9l+8WwYE11jT8E6+HIedC9ETrqf4TGAadnJjN6iV7CAzx4/L74+nVP/jkY3NB9YGDZD/xBOVff41P7970/f3vCJ8/H/HuWqtpulN737Ppa4ypn7c7C6gfTtIfSGtQL90ua6k8vYnylto4jojcDtwOMHBgO609rlQHU+esY236Wt7c8ybfZX2HwTAyYiS/mPQLpkdNZ3jEcHy9TnIYcV4KbFkM29+C8hzo0QfO/wNMuQ18T/zym7POSeLHqWz6byr9Bvdk7h3j3NqjceTlkf/yyxS88SZe/v70eeABwq+5WgcAnASPDRCw76+4dX2D1towxiwCFoG1xIA7Y1GqoymtKeX95Pd5I+kNMsoyiOoRxc/if8bc2LnE9ow99QYOroP/u8KaWXn4HJh4g/VMzEms/wJwJLmIdcv2kp9Rzohp/TjnupF4+7rnHo2prSX/tcXkvfQSpqaGnj++lD6//CU+kZFuaa87aO9kky0iUcaYTPsymT0zHhlAw+lbY+yyDH64JFZfvtYuj2mifkttKKWAQyWH+Peef/NhyodUOCqY1GcS9yXcxzkDzjmxuciaYwykfgVvXA0Rg+GG9611YU5SRUkN37yXwt4NWQRH+DP3jnHETYg88dkHXFS5bRuZjz9O9e49hJw/i9733ot/3Ek8jKqO0d7JZjmwAHjSfv+wQfldIrIMa4BAsZ0sPgP+JCL1wzxmAw8aYwpEpEREpmENELgReKGVNpTqtrLKs9iUtYlPUz9lXfo6fL18mRs3l+tGXcfoXqNP7eSOamvxscMbrOlj0jZCea61ANmNH0DIyT/3cmBbLqtf30NtVR2T5wxi8txYfP3dc5/EWVlJ5u8foeSjj/Dp3Zv+zz9H6OzZbmmrO3LbSp0i8iZWryQSa3mgR4APgLeBgcAhrGHJBfbQ5xexRpRVADcbYxLt8/wEeMg+7RPGmNfs8gR+GPr8CXC3fdmsV1NttBavrtSp2pIxBofTUONwWq86673aUUe1w0md09pf1+DlcBqcR8ud1DnB4XQeX8cYHHV2mflhX3iQL31DA/D19sJpDLV1tews+pb12R9woMy62R/sE86UXhdxWq8LCfaJwGkMTmPFaww4jcFAk+X1n6mrpW/RFvrnb6BfyTb6lOzGx9QAUBQQQ0bIBDJCxpMccQ7lPj2t47DP4zTHfm7i/L6FtQRlVeNTXkdQYS1Vwd5kjupBdZD3sXE2Oq6puI/5b4Jp9N/o2P3nr3mTaVs+Z93UH/HVaXOp9Qtotu5xv5on2Fbj4xv/Dh+/v7X2T/T4Ywteum4yAyJObpShqyt16rLQNk02yhhDRU0dRZW1lNiv0ioHpdX2e5WDkipru6zKQWnVD+Vl1Q6qHU5qHHXU1DmpdjiP+wvuLuJdim/4BsSn7Icyryp8gvci3lU4a8KpLZqCo2w4zuoo4OTuc/ji4DbvFfzUZwU9pYIa480uE0eicziJzuFscQ4nlzCrfQEvEbwEBDn2s/zwuXG9caUwsVhwCpT6CEeCYHcE4CXHHsexxzc8D0fPx3GX2hpfeKvfHZO5n1vf+jOJ42fy33NvaLJyc8f+sF9a3n+C9Rtz9bs0v7/54//447FE9Ty556U02ZwgTTZdV2lVLVnFVRwpriKzqJLM4ioyiyvJKa2mqMJKKsX2y+Fs+e+Dn7cXIQE+9suXYH9rOzjAB38fb/x9vPDz8bLevRts+3jjZ++zygUvEXy8vPD2kqMvn0bbXvXvIvh42/vs4/Krc3k3+U3e2beMGmcNoX5hOO34vb18GBs+men9zmNi5FS8vbyb/LE/Jhl4/fD5aD0MPsWp+O//DL/tr+NdkIxj2Bzqxl+LM+4cxL/H8UnjJO6llBVWs/r1PaTtLmBoQh/OuX4kfgHtc5W/ctcujvzmfpyVlQz+aDnewe4dRt3VuJpsdLoa1SWUVtWyL7uU3ZmlJGWWkFZYSWZRJVnFVZRWO46pKwK9g/3p1zOAnoG+xIQH0jPQ97hXaKDv0aQSEuBDsL8PAb6efa6iylHF+ox1fHLwE9YcXkOdqWNO3BzujL+TQaGD2qaR4gzrwcuD66z3YnuW5b7j4Nq38Rl+QZv8cBhjyEsrY//WHHZ+mUGdw8nZ1wxnzFn93Xbz/2jbdXWUrl5N4ZKlVCQm4hUURMyLL2iicSNNNqpTqXMaDhdUkJRZwp7MEvZklZKUVUJaQeXROiH+PsRG9iAusgdnDI2kX88AonoGEB0WSFTPAPqEBODn03mmfTfG8G3mt3yQ8gFr09ZS6agkIiCCq0dezbWjrmVAyIDWT9JyA3BkK2x/E1JWQYE1czOB4RB7JpxxDww7H8JjT/m7ANRUOti/NYcda9LJSytDvIQBo8KZcdVwwvqe4uwErXAUFFD0n/9QtOwtajMy8I2Ops8DDxB2+WXdcqnm9qTJRnVYxhgO5VewPb2I7WnF7EgvYteREipr6wDwEhjcO5gJMWFcfdpARvYLYUS/EPqHBbr9X8btoaauho8PfszS3UtJLkymp39PLoy7kDlxc0jom3Dqw5TLcmHHW9bsyjm7wSfAmsL/tFsg7izoMwbacC2WI8lFbPnsEGl7CnDWGSKie3D2tSMYMqk3gcHuezDTGEPl1m0UvvkmpZ9+iqmtJWjqVPrcfz8h552L+OjPYHvQP2XVYWSXVLE9rYjt6UXsSC9mR3oxxZW1AAT4ejEmuifzTxvA6OhQRvULZVjfYI9f1nKH4upi3t77Nm8kvUFeZR5Dw4by+OmPc9Hgi059ZuX6aWO2/Rv2fWqtctl/Mlz8LIy5DALD2uZLNJB7uJQNHx7g8K58gkL9GH/uAAZPiKTfkJ5u/UeBs7yc4hX/pfDNN6lOSsIrOJiw+fMJv3o+/kOHuq1d1TRNNspj6pyGzYcK+XRnFp/vziK90LoU5u0ljOgbwoXj+jE+JowJMWEM7xuMTzvM6OtJpTWlvLbzNf5vz/9R6ajk9OjTeeKMJ5gePf3UfpRrKqz7L8mfwZ4VP0wbM+1nEH899Gn7Rb7q6pyk7Spg99dHOLg9D/8ePky/bAjjZsbg6+fefyBU799P4ZvLKP7gA5xlZfiPHEm/xx6j58UX4dWjh1vbVs3TZKPalTFWgnl/awaf7coir6wGP28vZgyL5CdnxDFhQE9GR/Uk0M0/SB2JMYbl+5fzTOIzFFYXMjduLreOu5Xh4cNP/qRFh2HfZ5D8uZVoHFXg2wOGngfx19rTxrTtssn1N/z3bshi36YsKktrCQj2JeGiWOJnDcQ/0H0/N6a2ltJVqyh8400qvvsO8fUlZM4cwq+5hsCJ8V3ismpnp8lGtYuUnFI+2HqED7ZlkF5YSYCvF+eN6sucMf04Z2Qfgv273/+KFbUVrEtfx9v73mZT1ibie8fz0tSXGNNrzImfzBjI3glJ/7V6L9nfW+URg2HyzTB8tjV1v49/m34Hp9OQk1pC2p4C9m/JIT+jHC8fIW5cJCOm9WPg2F54u7FHWnP4MMUffEDRO+/iyM3FNzqa3vfdS9jll+MTEeG2dtWJ635/w1W7Ka928N7WDN7adJidGSV4CZwxNJJ7zx/O7DH9umWCcRon69LXseLACtalr6PSUUlkYCQPT32YK0dciZe4+MNcUWCtZlmYai2hnLLaHqIsMHAazP4jDJ8LkW1/b8I4DXnp1pDlpG+zKC+qBoG+saGcfc1whib0JaBH2/aaGnLk51PyyaeUfPQRldu3gwg9zppBv2seJ3jGDJ32v4Pqfn/bldvtzy3j9W8P8Z/N6ZRWOxgdFcrvLh7NjyZE0Seke07N7jROVh9ezUvbX2Jf4T4iAiL40eAfMSduDpP6TMLbq4UfyIoCa+6xI1utV+Y26zJZPb8QiJsBZ/0aRsyF4D5tGrujto7cw2VkHSgm+0AxR/YXU1lSgwgMGN2L0y8bwoDREe4dUeZ0Uvr55xS99x7lX38DdXX4jxhBn1/fR+hFF+EbdfITfar2oclGtYmckio+2pHJ8u1H2J5WhK+3cNG4KG48PZaJA8K67TVzYwyrD6/mn9v/yb7CfcSGxvKnM//E3Li5zQ9dri6DQ99Yi4ztXwO5e37YFx5njR477VboPRLCBkGvIW1y/6WmykFhVgWFWeUUZlVQZG8X51QenZkgNDKAASPDGTAqggGjI+jRs20vyzXmyMuj/OuvyX/lX1QnJ+MTHUWvn/yE0B9dTMDwU7inpdqdJht10grLa/h0VxbLtx1hw8F8jIHRUaEsnDuSyyfF0DvEvT9EHZkxhq8yvuLFbS+yO383saGx/HnGn5kbO/f4Xkydw+qxHFhrJZi078BZC97+MGg6jL8SoifB/7d37sFxVfcd//x2Ja31sB42En4jiRgb7NgyD9sEptg0GBsMOK8JFJpAkjIhE6aZhGnDkGnIjDMlzSQDDOkA9UDSlAaSpqQeSuNA6umEAMaAZWFsy8i2jCXL+KW3LGkfp3+cI/vqane1q921jPT7zOzs3fO633vP79zfveecPXfmUijKfBxisD/C8UPdtB/t5dTRPtrbrHPp7Rg4kyYQEMqqCqmYUUxNXSUXVpdyYU1p7p1Lezt927fTt+0tere9j5zifgAAELNJREFUyWCT/YNpQXU1s3/6E6auXYtk8b8/yrlDnY2SEtGY4eX32tjxYQcdpwfZ02b/uW8M1FxQzP3Xz+fWpbP4RNXkXu7DGMMbR97gyYYn2XFsB7NLZrPxmo3cXHszeeF+OLYHOg9Dx2HoOARHG+BIPQx02QJmLoWrvwG1q+3YyxjeZjlEJBzlYP0JwoNRouEYPR0DnDjcTUtjO7GIfVLJDwWpmFHEnAUVlM8oYtqMYipmFlFaWZjTgX2w5yr84Yf07djB6R31nN6xg4EPPgBjkMJCiq64gvINGyhasZIpl12qYzEfc3QhTocuxDmcSDTG7rYu9rZ1c7xngM31R2j8qJvigiBlhfnUVBazvHo61y+sYvHs0knbTTZEc2czWw9vZfP+zTR1NFEVmsa9F17DZ00h+cf2wkfvQ/vB4ZmCIbjwMpi1zC4LU7MKiqdnRU80GuP3T+2iueHEmTAJCGWVhVz0yenMvXQa02YWU1IROid1F+3uZmDfPvobGxnYt4+Bxn0M7NtHrLcXgEBJCYV1dRRevozilSspXLwYKcjdGJCSPXQhTiUtBiMx3mvt4M0Dp3jr4CneOdROj2cBy9rKYh6/YxnrPzmTQGByOxaAaCRMw6E/srV5C1uPvUPzYDsAl0UDbGzvYF33hxTsrQcJwLSL7RNL3Z12fKV8HpTNheLKjJeDOdnaw8nWnhHhB+qP09xwgmu/MJ/aZZUEgkLh1IKc150Jhxk8dMg5ln0MNDbSv6+RyJG2M2kCpaWELplP2W23EVqwgMK6OkKfuFifXCY46mwmIcYYmk/20eDWHNvZ0sGu1k4GIjEA5leVsGHZLJbXTKduTjlVpaEJuSzMqMSidtbXqQNw6gDm5AGaTu3hd32HeCk4wKlgkDxjuLK/nztOh1lVUMmsiothyUKoutQO4FcuyKgrLBknWnr4zSPbz3SJ+Vm5oZalf5nhIp0ejDFEOzqIHDs27BM+0sZgy2HCh1sIt7VB1K5dRzBIqLaGomWXE/riJYQWXMKUBQvImzFj0j8JT0YmrLMRkbXAY0AQ2GSMeWScJZ1TjDH0DET4qGuAY139HO3qp/Gjbt5r6WRXaydd/fapZUp+gMWzyrhzxUUsr6ngquppTC+ZJAP7kQE4usv+AbLjQzvFGKDvJJxswpzczxGJsKuggPopIf5UVMSh/DzyCuC60BxurFzGtXNXMbVqEZTOhmTTl7NMNBzj1Wd3EyrK55b7l45YAiavIEhJReJ6NMYQ6+0l2tFBtKPTfnd2uO9OYp2dZ+IiHe1Ejh8ncvwEhMMjygpWVJA/by6FS5dSuv5mCqqrmbJwIQW1tQS0K0xxTEhnIyJB4GfADUALsF1ENhtjdo+vsvSIxQwD7lXC/eGz36fDUdr7BmnvHeSU53Oy14ad6BngWPcAfYPRYeXlB4WFM0pZv3QWS2aXseR8XnPMGLtIZCxiF4+MReyTRizsCY+c3Q6fhoFuGOiE/i474D7QTXiwl+5wD4NdrYQ7WxgMnyYcGyQcG6Q/0k+3GLoCAbqCQfoL7LpZJwpCNJZM4YOS2XQb65QLAvlceeFV3DVvNWuq1zBtSu7+nW6MgXAYE41iolGIRDCRyJnt7lP9vP1/JzjZ2ssNt5RTfLyJWG8vsb6+M9/9fX30ecN6eqzzaG8n0tFOtKMzruMYIlBcTLCsjGB5OcHyMkLV1eRVVZFXWWW/hz6VFxAITZKbEyUjJuQEARG5GnjYGHOj+/0ggDHmHxPlGesEgWfu3kiUmuH7HyWP8aRItTPBpJE23n5S3deQNaS3r7F0iaSfJ5GlStL4MWgT7yt7/S/vTZIpU4z/zfDxGQiVgwSYe/hV5u9/MXHCYJBAcTGBoiLrPCrKyauocA5k6LucYEX5WcdSVkawtFQH55WUmewTBGYDhz2/W4AV/kQici9wL8C8efPGtCMJnSZw+qi7QJ3Fe9Hwh6dzWUrWtS0jNsbIiPxjczlp7y/ejU7SF7cLw7QNS+reUU/Ajge4jxAgIAEXByIBl82mD0iAIAECgQBCwONo0qwpv5xhB5os2B2P02jfqxxw34JIAPfOZbsdEEqKw9TONUwtvQ6CqwlMKbROpbhomHORggIdG1HOGyaqs0kJY8zTwNNgn2zGUsY9T/0wq5oURVEmIudhZ31WaAW803DmuDBFURRlHJiozmY7MF9EakSkALgd2DzOmhRFUSYtE7IbzRgTEZFvAluwU5+fMca8P86yFEVRJi0T0tkAGGNeBl4ebx2KoijKxO1GUxRFUc4j1NkoiqIoOUedjaIoipJz1NkoiqIoOWdCLlczFkTkOHBojNkvAE6Mmurco7rSQ3Wlh+pKj4mq6yJjTOVoidTZZAEReTuVtYHONaorPVRXeqiu9JjsurQbTVEURck56mwURVGUnKPOJjs8Pd4CEqC60kN1pYfqSo9JrUvHbBRFUZSco082iqIoSs5RZ6MoiqLkHmPMpP0AzwDHgF2+8JXAvwA3AO8A77nv6118EfDfwF7gfeARX/6ZwB+AOuANl6YB+KInTQ2wDWgCXgAKPHFzga3Abpf3b+Nom+7S9ABP+Pb/Q+ybSnviHHNG2jxppgBvATtdGT/wxN0OPATc6cp+D3gdWOor40ngGuDH7lw2AC8C5Z40DzodjcCNCepxzFqSnetUbMGlucKFNwGP47qnXVwQ2AG8FEfTQlcHA8ADqdhmNjTFKa/Zpa0H3k7H1ly677rz+213HhuAP2L/fzGU5svAB+7z5Qy0JDvmnNk9UA78B9ZO9wBXp9Eek9lHpvY1mq7l7lzWY9vHZzx512LbVRPwXZ/mUdtwsvxx6zaVi/JE/QB/AVzOSGfzA+BzwDJglgtbDLS67SJgtdsuAP4ErPPkvwf4DnAJMN+FzQLacBdS4NfA7W77SeA+X8O43G1PBfYBl/m0FQPXAl+PY9wrXRnxGl1G2jzlCFDitvOxjXSl+/0L10A+BVS4sHXANl8Z9diL8Rogz4X9CPiR277MNZAQ9kKwHwhmU0uyc52KLbjfb7lzLsD/+Gzh28C/M9zZDGmqAq7CXiT9ziaubWZDU5zymoELkuwnoa25dFuBSmA1UOTC7gNecNvTgAPuu8JtV4xRS7Jjzpnduzr7mqfNl6d6jkaxj0ztazRdRZxtWzOxNzB52Ha3H6h1+XYy3O5HazdJ88et22SRk+EDVDPS2bwGlPnCBDgFhOKU8RjwN57fL8Q78a5C5ruyTniM4GpgSxKN/wXcEE8bcLffuD1x8RpdVrW5NEXAu8AKl38nvjtp7EXG24AuBX4dp6zPAM+57QeBBz1xW3B3btnUkuhcp2ILrgHv9cTdATzltudg7/CvxzmbeJqAh/E5m0S2mammBMfcTPwL/Ki2BpQCf46Td9lQuH//wFPAHZloSdYms233QBlw0G9HqZyj0eoiQ/tKWZcLqwE+wjqbYe0aT1uLZ6P+dpMsf6KPjtn4EJELgLAxptMX9TngXWPMgC99OXAL9qKCiASBBcaY3b50y7F3APuxj9wdxpiIi24BZifQU41tuNuSaEv12LKtLSgi9di7pVeMMduc1p3GWaCHr2LvyoZYB/w+TrFf8aSbje0WGSJXWobKqHZ5trnfqdjCbKcrnsZHgb8DYp74RJpSIgua4mGAP4jIOyJy7yj78fNpnO378J7jlOsxTS1x26SfLNh9DXAceFZEdojIJhEpTvEcJayLLNRlSrpEZIWIvI/tCvu6O8ZkdZJKu0mnToEJ/PK0DFiD7ds9g4gswnbvrPGF5wG/Ah43xhxwwStwFytPupnAL7F91TERSUmIiJQAvwW+ZYzpEpG/8mtLk6xpAzDGRIE653BfFJHF2H7cYRdyEVmNNdRrPcE3Yrs2vOkeAiLAcymLyI6WEefaBadsC35EZD1wzBjzjois8kSN0JQmY9aUhGuNMa0iUgW8IiJ7sU9lqdjaWuBZn567gCuB63KlJc1jztTu87BdmvcbY7aJyGPYcao9fl1pkmldpqTL3XgtEpFLgV+IyGj2l3K7SQd9shnJsDtuEZmDHbT+kjFmvy/t08AHxphHk+QvxU4meMgY86YLPgmUO2cFtjG1egsWkXzsxe85Y8x/xis7C8c2Jm1+jDEd2H77tfgakIgsATYBtxljTrqwImzf8hFPuruB9cCdnjuqVuwA/hBZ1+LC451rSM0WWp0uv8ZrgFtFpBl4HrheRP7Nr2kMZKIpLsaYVvd9zJW13L+fJCzHjikM6fk0dmD5Vs8TR8r1mIqWUdpkPDK1+xagxV20wQ7IX+4vNwHJ6iLTukxLlzFmD3YCw2KS10kq7Sbttpmwf22yfPD0i+Prq8TO9NgJfDZOvo3YC1TAF/46MNVtF2C7GL4VJ/9vGD4Y+Q1PnAD/CjzqC4vXj3o3KY7ZZEObJ00lZwcjC7GTJG4DXvOkmYedqfIpX96b8czgwzqG3UClL90ihk8QOED8CQKZaBlxrsdgC/4B3Jt88auAl7B97K/Fyf8wKYzZZFOTJ12xxyaKnY2sS8XWXP087/m9DNslNd+Xbxp2bKHCfQ4C08aiJdkx59LunU0t8NTXj1M5R4nqIlt1OZoubLsZGoe6CDiCXeU5D9ueajg7wL8In42SuN3EzZ+oTowxk9vZYLvA2oAw9i7h74Gfe+K/B/RydupgPXYG0Rxs3/IeT/jXsBe9//Xkv8uV7c1f5+JqnRE1OSMPefJd68pv8OT7B682l64ZO5DY4/QPzVj7J/c75r4fzpY2T/4l2Cm9DcAup+/zwMOeNJuAdk/5b7vwJ4BVnnRN2P7foXRPeuIewl7AGkkwoypDLfHO9U3YbqBRbcHFXen2u98dm/8CtArrbPyaZrj66QI63HZpAtv8ajY1ecqoxV4ohqaNP+TfTyJbAx4A7vakeRU7AD2kZbMn7iuunpuAe8aqZZRjzpndY6dMv+3s5HfYWYSptscRdZGtuhxNF/DX7lzWYyfObPDE3YSdfbkf+5QHKbabRPmTfXS5Gg8i8j2gyRjz/Bjz3wXMMcY8kl1l57c2zz42AZvM2a6JROneBVYYY8LjrSVJ/ozO98dFUyb7EZFXsF0+beOtJUn+nNh9FnTlpC6zoCsjG01atjobRVEUJdfoBAFFURQl56izURRFUXKOOhtFURQl56izURRFUXKOOhtFGQdEJCoi9SLyvojsFJHviEjS9igi1W4VCUX52KHORlHGh9PGmDpjzCLs8vLrgO+PkqcaUGejfCzRqc+KMg6ISI8xpsTzuxbYjv1390XYdbuKXfQ3jTGvi8ib2NWyD2KXgH8ceAT7p9EQ8DNjzFPn7CAUJQ3U2SjKOOB3Ni6sA1gAdAMxY0y/iMwHfmWMudIt6PmAMWa9S38v9l/mG0UkBPwZ+IIx5uA5PRhFSQFd9VlRzj/ygSdEpA6IYl/4FY81wBIR+bz7XYZ9N4s6G+W8Q52NopwHuG60KPZ9PN/HrjG2FDuu2p8oG3Z5+S3nRKSiZIBOEFCUcUZEKrGrDD9hbL92GdBmjIlhF1IMuqTd2FdXD7EFuM+9IgERuUREilGU8xB9slGU8aHQvVk0H/vCuF8CP3Vx/wz8VkS+hH0vSa8LbwCiIrIT+Dn2deTVwLti3/51HNhwrg5AUdJBJwgoiqIoOUe70RRFUZSco85GURRFyTnqbBRFUZSco85GURRFyTnqbBRFUZSco85GURRFyTnqbBRFUZSc8/8Z6aMzKJTP3AAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QldLIptIrJcD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "countries=list(corona_dataset_aggregated.index)"
      ],
      "execution_count": 116,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l1rRf6j4riRf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "7974d45e-21a6-444b-979c-20080cbdb221"
      },
      "source": [
        "countries"
      ],
      "execution_count": 117,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Afghanistan',\n",
              " 'Albania',\n",
              " 'Algeria',\n",
              " 'Andorra',\n",
              " 'Angola',\n",
              " 'Antigua and Barbuda',\n",
              " 'Argentina',\n",
              " 'Armenia',\n",
              " 'Australia',\n",
              " 'Austria',\n",
              " 'Azerbaijan',\n",
              " 'Bahamas',\n",
              " 'Bahrain',\n",
              " 'Bangladesh',\n",
              " 'Barbados',\n",
              " 'Belarus',\n",
              " 'Belgium',\n",
              " 'Belize',\n",
              " 'Benin',\n",
              " 'Bhutan',\n",
              " 'Bolivia',\n",
              " 'Bosnia and Herzegovina',\n",
              " 'Botswana',\n",
              " 'Brazil',\n",
              " 'Brunei',\n",
              " 'Bulgaria',\n",
              " 'Burkina Faso',\n",
              " 'Burma',\n",
              " 'Burundi',\n",
              " 'Cabo Verde',\n",
              " 'Cambodia',\n",
              " 'Cameroon',\n",
              " 'Canada',\n",
              " 'Central African Republic',\n",
              " 'Chad',\n",
              " 'Chile',\n",
              " 'China',\n",
              " 'Colombia',\n",
              " 'Comoros',\n",
              " 'Congo (Brazzaville)',\n",
              " 'Congo (Kinshasa)',\n",
              " 'Costa Rica',\n",
              " \"Cote d'Ivoire\",\n",
              " 'Croatia',\n",
              " 'Cuba',\n",
              " 'Cyprus',\n",
              " 'Czechia',\n",
              " 'Denmark',\n",
              " 'Diamond Princess',\n",
              " 'Djibouti',\n",
              " 'Dominica',\n",
              " 'Dominican Republic',\n",
              " 'Ecuador',\n",
              " 'Egypt',\n",
              " 'El Salvador',\n",
              " 'Equatorial Guinea',\n",
              " 'Eritrea',\n",
              " 'Estonia',\n",
              " 'Eswatini',\n",
              " 'Ethiopia',\n",
              " 'Fiji',\n",
              " 'Finland',\n",
              " 'France',\n",
              " 'Gabon',\n",
              " 'Gambia',\n",
              " 'Georgia',\n",
              " 'Germany',\n",
              " 'Ghana',\n",
              " 'Greece',\n",
              " 'Grenada',\n",
              " 'Guatemala',\n",
              " 'Guinea',\n",
              " 'Guinea-Bissau',\n",
              " 'Guyana',\n",
              " 'Haiti',\n",
              " 'Holy See',\n",
              " 'Honduras',\n",
              " 'Hungary',\n",
              " 'Iceland',\n",
              " 'India',\n",
              " 'Indonesia',\n",
              " 'Iran',\n",
              " 'Iraq',\n",
              " 'Ireland',\n",
              " 'Israel',\n",
              " 'Italy',\n",
              " 'Jamaica',\n",
              " 'Japan',\n",
              " 'Jordan',\n",
              " 'Kazakhstan',\n",
              " 'Kenya',\n",
              " 'Korea, South',\n",
              " 'Kosovo',\n",
              " 'Kuwait',\n",
              " 'Kyrgyzstan',\n",
              " 'Laos',\n",
              " 'Latvia',\n",
              " 'Lebanon',\n",
              " 'Lesotho',\n",
              " 'Liberia',\n",
              " 'Libya',\n",
              " 'Liechtenstein',\n",
              " 'Lithuania',\n",
              " 'Luxembourg',\n",
              " 'MS Zaandam',\n",
              " 'Madagascar',\n",
              " 'Malawi',\n",
              " 'Malaysia',\n",
              " 'Maldives',\n",
              " 'Mali',\n",
              " 'Malta',\n",
              " 'Mauritania',\n",
              " 'Mauritius',\n",
              " 'Mexico',\n",
              " 'Moldova',\n",
              " 'Monaco',\n",
              " 'Mongolia',\n",
              " 'Montenegro',\n",
              " 'Morocco',\n",
              " 'Mozambique',\n",
              " 'Namibia',\n",
              " 'Nepal',\n",
              " 'Netherlands',\n",
              " 'New Zealand',\n",
              " 'Nicaragua',\n",
              " 'Niger',\n",
              " 'Nigeria',\n",
              " 'North Macedonia',\n",
              " 'Norway',\n",
              " 'Oman',\n",
              " 'Pakistan',\n",
              " 'Panama',\n",
              " 'Papua New Guinea',\n",
              " 'Paraguay',\n",
              " 'Peru',\n",
              " 'Philippines',\n",
              " 'Poland',\n",
              " 'Portugal',\n",
              " 'Qatar',\n",
              " 'Romania',\n",
              " 'Russia',\n",
              " 'Rwanda',\n",
              " 'Saint Kitts and Nevis',\n",
              " 'Saint Lucia',\n",
              " 'Saint Vincent and the Grenadines',\n",
              " 'San Marino',\n",
              " 'Sao Tome and Principe',\n",
              " 'Saudi Arabia',\n",
              " 'Senegal',\n",
              " 'Serbia',\n",
              " 'Seychelles',\n",
              " 'Sierra Leone',\n",
              " 'Singapore',\n",
              " 'Slovakia',\n",
              " 'Slovenia',\n",
              " 'Somalia',\n",
              " 'South Africa',\n",
              " 'South Sudan',\n",
              " 'Spain',\n",
              " 'Sri Lanka',\n",
              " 'Sudan',\n",
              " 'Suriname',\n",
              " 'Sweden',\n",
              " 'Switzerland',\n",
              " 'Syria',\n",
              " 'Taiwan*',\n",
              " 'Tajikistan',\n",
              " 'Tanzania',\n",
              " 'Thailand',\n",
              " 'Timor-Leste',\n",
              " 'Togo',\n",
              " 'Trinidad and Tobago',\n",
              " 'Tunisia',\n",
              " 'Turkey',\n",
              " 'US',\n",
              " 'Uganda',\n",
              " 'Ukraine',\n",
              " 'United Arab Emirates',\n",
              " 'United Kingdom',\n",
              " 'Uruguay',\n",
              " 'Uzbekistan',\n",
              " 'Venezuela',\n",
              " 'Vietnam',\n",
              " 'West Bank and Gaza',\n",
              " 'Western Sahara',\n",
              " 'Yemen',\n",
              " 'Zambia',\n",
              " 'Zimbabwe']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 117
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ppzM4PDJrqlR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 334
        },
        "outputId": "547ea1b9-271a-4ca9-a5d6-dbaaee7eb75f"
      },
      "source": [
        "print(countries)\n",
        "lis=['Afghanistan', 'Albania']\n",
        "corona_dataset_aggregated.loc[\"India\"].plot()\n",
        "plt.legend()\n",
        "plt.xlabel(\"Date\")\n",
        "plt.ylabel(\"Number of Cases\")"
      ],
      "execution_count": 118,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['Afghanistan', 'Albania', 'Algeria', 'Andorra', 'Angola', 'Antigua and Barbuda', 'Argentina', 'Armenia', 'Australia', 'Austria', 'Azerbaijan', 'Bahamas', 'Bahrain', 'Bangladesh', 'Barbados', 'Belarus', 'Belgium', 'Belize', 'Benin', 'Bhutan', 'Bolivia', 'Bosnia and Herzegovina', 'Botswana', 'Brazil', 'Brunei', 'Bulgaria', 'Burkina Faso', 'Burma', 'Burundi', 'Cabo Verde', 'Cambodia', 'Cameroon', 'Canada', 'Central African Republic', 'Chad', 'Chile', 'China', 'Colombia', 'Comoros', 'Congo (Brazzaville)', 'Congo (Kinshasa)', 'Costa Rica', \"Cote d'Ivoire\", 'Croatia', 'Cuba', 'Cyprus', 'Czechia', 'Denmark', 'Diamond Princess', 'Djibouti', 'Dominica', 'Dominican Republic', 'Ecuador', 'Egypt', 'El Salvador', 'Equatorial Guinea', 'Eritrea', 'Estonia', 'Eswatini', 'Ethiopia', 'Fiji', 'Finland', 'France', 'Gabon', 'Gambia', 'Georgia', 'Germany', 'Ghana', 'Greece', 'Grenada', 'Guatemala', 'Guinea', 'Guinea-Bissau', 'Guyana', 'Haiti', 'Holy See', 'Honduras', 'Hungary', 'Iceland', 'India', 'Indonesia', 'Iran', 'Iraq', 'Ireland', 'Israel', 'Italy', 'Jamaica', 'Japan', 'Jordan', 'Kazakhstan', 'Kenya', 'Korea, South', 'Kosovo', 'Kuwait', 'Kyrgyzstan', 'Laos', 'Latvia', 'Lebanon', 'Lesotho', 'Liberia', 'Libya', 'Liechtenstein', 'Lithuania', 'Luxembourg', 'MS Zaandam', 'Madagascar', 'Malawi', 'Malaysia', 'Maldives', 'Mali', 'Malta', 'Mauritania', 'Mauritius', 'Mexico', 'Moldova', 'Monaco', 'Mongolia', 'Montenegro', 'Morocco', 'Mozambique', 'Namibia', 'Nepal', 'Netherlands', 'New Zealand', 'Nicaragua', 'Niger', 'Nigeria', 'North Macedonia', 'Norway', 'Oman', 'Pakistan', 'Panama', 'Papua New Guinea', 'Paraguay', 'Peru', 'Philippines', 'Poland', 'Portugal', 'Qatar', 'Romania', 'Russia', 'Rwanda', 'Saint Kitts and Nevis', 'Saint Lucia', 'Saint Vincent and the Grenadines', 'San Marino', 'Sao Tome and Principe', 'Saudi Arabia', 'Senegal', 'Serbia', 'Seychelles', 'Sierra Leone', 'Singapore', 'Slovakia', 'Slovenia', 'Somalia', 'South Africa', 'South Sudan', 'Spain', 'Sri Lanka', 'Sudan', 'Suriname', 'Sweden', 'Switzerland', 'Syria', 'Taiwan*', 'Tajikistan', 'Tanzania', 'Thailand', 'Timor-Leste', 'Togo', 'Trinidad and Tobago', 'Tunisia', 'Turkey', 'US', 'Uganda', 'Ukraine', 'United Arab Emirates', 'United Kingdom', 'Uruguay', 'Uzbekistan', 'Venezuela', 'Vietnam', 'West Bank and Gaza', 'Western Sahara', 'Yemen', 'Zambia', 'Zimbabwe']\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Number of Cases')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 118
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OwYufLIRrqhx",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 441
        },
        "outputId": "34d2b11b-1d46-4087-edc0-f3cf8fb75a36"
      },
      "source": [
        "for c in countries:\n",
        "  corona_dataset_aggregated.loc[c].plot(figsize=(20,10))\n",
        "  plt.xlabel(\"Date\")\n",
        "  plt.ylabel(\"Number of Recovered Cases\")\n",
        "  "
      ],
      "execution_count": 156,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DeSN4PAcrqYL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 119,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wWxbBXo9rqUx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 119,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "52pUSpc5rqRo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 119,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4j-AUb7Pjvmt",
        "colab_type": "text"
      },
      "source": [
        "### Task3: Calculating a good measure \n",
        "we need to find a good measure reperestend as a number, describing the spread of the virus in a country. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tLz7sF6Ljvmu",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "outputId": "2820bdb3-bea8-46a6-f3e3-7a599c2b5e43"
      },
      "source": [
        "corona_dataset_aggregated.loc['India'].plot()\n",
        "plt.legend()"
      ],
      "execution_count": 120,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f5cb5d00710>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 120
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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O7p/qUJpMSUNEpJ29XXaA4ydrmTpKSUNERBqxclslAOfrTENERBqzYmslY/N7MLBn11SH0mRKGiIi7aim1lm5rTIj+zNASUNEpF2t2b6PQ8er0/62rvVR0hARaUfPrC4jLzeLK87KjKvaxlPSEBFpJyeqa3jurR3MnHgavTLs9xl1lDRERNrJyxv3cPB4Nf84uSDVoTSbkoaISDv53eoyBvXqykVjM7M/A5JIGmY23MxeMbMNZrbezL4cyvub2RIz2xz+9gvlZmb3m1mxma01s8kx65oT6m82szkx5VPM7O2wzP1mZg1tQ0Qk0+w/WsXSd/cwe9JQcrIz9//1ZCKvBr7m7hOA6cBtZjYBuAN4yd3HAy+F5wBXAePDYx7wAEQJALgLmAZMBe6KSQIPALfGLDcrlNe3DRGRjLJkw26qa52PZthVbeM1mjTcfae7rw7Th4CNwDBgNrAwVFsIXBumZwOPeGQZ0NfMhgAzgSXuXunu+4AlwKwwr7e7L3N3Bx6JW1eibYiIZJTF63cxrG83zhnWJ9WhtEiTzpHMbBRwHrAcGOzuO8OsXcDgMD0M2B6zWGkoa6i8NEE5DWwjPq55ZlZkZkXl5eVNeUkiIm3u8IlqXtu8l5kTTyO0vmespJOGmfUEfgd8xd0Pxs4LZwjeyrGdoqFtuPt8dy9098L8/Py2DENEpMlefmcPVdW1zMqg27rWJ6mkYWa5RAnjMXd/JhTvDk1LhL97QnkZMDxm8YJQ1lB5QYLyhrYhIpIxFq/bxcCeXZkyMvPH8iQzesqAh4CN7v6fMbMWAXUjoOYAz8aU3xRGUU0HDoQmpsXADDPrFzrAZwCLw7yDZjY9bOumuHUl2oaISEbYf7SKl9/Zw8yJg8nOyuymKYCcJOpcBNwIvG1ma0LZvwP3AE+Z2VzgPeATYd7zwNVAMXAUuAXA3SvN7DvAylDv2+5eGaa/CCwAugF/Cg8a2IaISEZ4bPn7HDtZw40XjEx1KK3Coq6CjqOwsNCLiopSHYaICCeqa7j43lc4a0hvHvns1FSH0yAzW+XuhY3Vy9xfmIiIpLln1+yg/NAJ5v3dmFSH0mqUNERE2kBtrfPL10o4a0hvLhqXuZcNiaekISLSBl7csJvNew7z+UvGZPxvM2IpaYiItDJ35yevbGb0wB585NzMvmxIPCUNEZFWtnRTOevKDvKFS8d2iGG2sZQ0RERa2S9fK2FY32587LxhjVfOMEoaIiKtqPzQCZaVVPBPUwrIzeBLoNen470iEZEUenHDLmodrj4n868zlYiShohIK/rT27sYM7AHZwzulepQ2oSShohIK6k8UsUbJRVcdU7mXwK9PkoaIiKtZMmGXdTUOledPSTVobQZJQ0RkVbyu1VljBzQnYlDe6c6lDajpCEi0grWbN/Pim2V3HTBqA7bNAVKGiIireKX/1NCr7wcrj9/eOOVM5iShohIC22vPMqf3t7Jp6aOoGfXZG5TlLmUNEREWmjB69vIMuPmi0alOpQ2p6QhItICR6uqeapoO7POPo0hfbqlOpw2p6QhItICi9bs4NDxam66YFSqQ2kXShoiIs3k7jzyxnucMbgX54/ql+pw2oWShohIM725fT8bdh7kxgtGduhhtrEaTRpm9rCZ7TGzdTFld5tZmZmtCY+rY+bdaWbFZvaumc2MKZ8VyorN7I6Y8tFmtjyUP2lmXUJ51/C8OMwf1VovWkSkNTz0P1vp1TWHazvgJdDrk8yZxgJgVoLy+9x9Ung8D2BmE4AbgIlhmZ+ZWbaZZQM/Ba4CJgCfDHUB7g3rGgfsA+aG8rnAvlB+X6gnIpIWSsoP8/y6ndx4wcgOP8w2VqNJw91fAyqTXN9s4Al3P+HuW4FiYGp4FLt7ibtXAU8Asy06n7sceDosvxC4NmZdC8P008AV1lnO/0Qk7c1/rYQu2VncctHoVIfSrlrSp3G7ma0NzVd1PUDDgO0xdUpDWX3lA4D97l4dV37KusL8A6H+B5jZPDMrMrOi8vLyFrwkEZHG7T54nGdWl/GJwuHk9+qa6nDaVXOTxgPAWGASsBP4YatF1AzuPt/dC929MD8/P5WhiEgn8OyaMqpqavnc33WuswxoZtJw993uXuPutcAviZqfAMqA2AuvFISy+sorgL5mlhNXfsq6wvw+ob6ISEq9tmkvpw/uycgBPVIdSrtrVtIws9iLxX8MqBtZtQi4IYx8Gg2MB1YAK4HxYaRUF6LO8kXu7sArwHVh+TnAszHrmhOmrwNeDvVFRFLmaFU1K7ZWcsnpnbNVo9EufzN7HLgUGGhmpcBdwKVmNglwYBvwzwDuvt7MngI2ANXAbe5eE9ZzO7AYyAYedvf1YRNfB54ws+8CbwIPhfKHgEfNrJioI/6GFr9aEZEWWl5SSVVNLZecPijVoaREo0nD3T+ZoPihBGV19b8HfC9B+fPA8wnKS/hb81Zs+XHg443FJyLSnl7dVE5ebhaFneQX4PH0i3ARkSZ4bVM5F4wZQF5udqpDSYnO84sUEZFmcndeeXcPK7fto2TvEW68YGSqQ0oZJQ0RkUb8/s0yvvrUW2QZnD64J7POPi3VIaWMkoaISAP2H63ie/+1kUnD+/LEvOmdtlmqjpKGiEgD7n3hXfYfO8mjHzun0ycMUEe4iEi9Vr1XyeMr3uezF41iwtDeqQ4nLShpiIgkcLKmlm/8fh1D++TxlStPT3U4aUPNUyIiCfzqL1t5Z9ch5t84hR6d6NLnjdGZhohInO2VR7lvyWauPGswMyZ23pFSiShpiIjEcHf+z7PryDL49uyJqQ4n7ShpiIjE+OPanSx9t5yvzTiDoX27pTqctKOkISISHDh6km89t4FzC/ow58JRqQ4nLal3R0QkuOeFjew7WsWCW84nO0t3l05EZxoiIsCKrZU8vmI7cy8ezdnD+qQ6nLSlpCEind7Rqmru+N1ahvXtxleuHJ/qcNKamqdEpNP7j+c3UrL3CL/53DS6d9HXYkN0piEindor7+zh18ve59a/G82F4wamOpy0p6QhIp3Wjv3H+OpTazjztF7868wzUh1ORlDSEJFOqaq6ltt/s5qq6lp++unJdM3RFWyT0WjSMLOHzWyPma2LKetvZkvMbHP42y+Um5ndb2bFZrbWzCbHLDMn1N9sZnNiyqeY2dthmfvNzBrahohIS7k733puPavf38+9153L2PyeqQ4pYyRzprEAmBVXdgfwkruPB14KzwGuAsaHxzzgAYgSAHAXMA2YCtwVkwQeAG6NWW5WI9sQEWmRn79awmPL3+efLxnDR84dmupwMkqjScPdXwMq44pnAwvD9ELg2pjyRzyyDOhrZkOAmcASd690933AEmBWmNfb3Ze5uwOPxK0r0TZERJrtj2t3cO8L73DNh4by9ZlnpjqcjNPcPo3B7r4zTO8CBofpYcD2mHqloayh8tIE5Q1t4wPMbJ6ZFZlZUXl5eTNejoh0Btv2HuHrT69l8oi+/ODj55KlX303WYs7wsMZgrdCLM3ehrvPd/dCdy/Mz89vy1BEJEOdqK7h9sdXk5Odxf/7lDq+m6u5SWN3aFoi/N0TysuA4TH1CkJZQ+UFCcob2oaISJO4O9/8/TrWlR3k+9edyzBdvbbZmps0FgF1I6DmAM/GlN8URlFNBw6EJqbFwAwz6xc6wGcAi8O8g2Y2PYyauiluXYm2ISLSJD9buoXfrirlf10xnpm6qVKLNPp7eTN7HLgUGGhmpUSjoO4BnjKzucB7wCdC9eeBq4Fi4ChwC4C7V5rZd4CVod633b2uc/2LRCO0ugF/Cg8a2IaISNJeeWcPP1j8LrMnDeVfdF2pFrOou6DjKCws9KKiolSHISJpYN+RKmb86DUG9OjCH267iLxc9WPUx8xWuXthY/V0ZS4R6bD+76L17DsS3R9DCaN16DIiItIh/bZoO8+9tYMvXzGeiUN1f4zWoqQhIh3O+h0H+OYf1nHh2AF84dKxqQ6nQ1HSEJEO5UR1DV/49Wr6de/C/Z88j5xsfc21JvVpiEiHsqykkvcrj/KLG6cwsGfXVIfT4SgFi0iHsvTdPXTNyeKS03V1iLagpCEiHcqr75YzfcwAjZZqI0oaItJhvF9xlJK9R7j0DJ1ltBUlDRHpMJZuii5Rd9kZg1IcScelpCEiHcbSd8sZNaA7owb2SHUoHZaShoh0CK8X7+XPxXu5VGcZbUpJQ0Qy3ksbd3PzgpWMHtCD2y4bl+pwOjT9TkNEMtor7+7h879exVlDerPwlqn069El1SF1aEoaIpKxlpVU8PlHV3H64F48OncafbrlpjqkDk/NUyKSkd58fx9zF6xkeP/uPPLZqUoY7URJQ0QyzsadB7n5VysZ0LMrj31uGgN0uZB2o6QhIhmlaFslN8xfRrfcbB773DQG985LdUidipKGiGSM/96wm08/uJz+Pbrw289fwPD+3VMdUqejjnARyQgvrNvF7b9ZzYShvfnVzeerSSpFWnSmYWbbzOxtM1tjZkWhrL+ZLTGzzeFvv1BuZna/mRWb2Vozmxyznjmh/mYzmxNTPiWsvzgsay2JV0Qy0+9WlXL7b1ZzTkEffq0+jJRqjeapy9x9UswNye8AXnL38cBL4TnAVcD48JgHPABRkgHuAqYBU4G76hJNqHNrzHKzWiFeEckQx0/WcOczb/O1375F4ah+PPLZqfTO0yipVGqLPo3ZwMIwvRC4Nqb8EY8sA/qa2RBgJrDE3SvdfR+wBJgV5vV292Xu7sAjMesSkQ7uWFUNtz5SxOMr3ucLl47l13On0UsJI+Va2qfhwItm5sAv3H0+MNjdd4b5u4DBYXoYsD1m2dJQ1lB5aYJyEengjpyoZu7ClSzfWskPrjuXjxcOT3VIErQ0aVzs7mVmNghYYmbvxM50dw8JpU2Z2TyiJi9GjBjR1psTkTa070gVtyxYydrS/dz3iUlce57+V0wnLWqecvey8HcP8HuiPondoWmJ8HdPqF4GxP67UBDKGiovSFCeKI757l7o7oX5+br5ikim2n3wONfPf4MNOw7ywGemKGGkoWYnDTPrYWa96qaBGcA6YBFQNwJqDvBsmF4E3BRGUU0HDoRmrMXADDPrFzrAZwCLw7yDZjY9jJq6KWZdItLBbNt7hH964HXK9h1jwS3nM3PiaakOSRJoSfPUYOD3YRRsDvAbd3/BzFYCT5nZXOA94BOh/vPA1UAxcBS4BcDdK83sO8DKUO/b7l4Zpr8ILAC6AX8KDxHpYDbuPMiND62gpraW39w6nQ8N75vqkKQeFg1M6jgKCwu9qKgo1WGISJKKtlVyy4KV9OiSw6NzpzJ+cK9Uh9QpmdmqmJ9O1Eu/CBeRlHB3nli5nbsXrWdo3248OncqBf10WZB0p6QhIu3u8IlqvvH7t3l2zQ4uHjeQH90wiYH6lXdGUNIQkXa1cedBbntsNdsqjvC1D5/OFy8bR3aWrhCUKZQ0RKTdlJQf5vpfvEFebja/uXU608cMSHVI0kRKGiLSLvYdqeKzC1aSm53F775woS5rnqF0Pw0RaXPv7jrEpx5czo79x5l/0xQljAymMw0RaTMVh0/w8F+28svXttK7Ww6/uGkKU0b2T3VY0gJKGiLS6g6fqOYnLxez4PWtnKiu5aPnDuWuj07QfTA6ACUNEWk1J2tqeWZ1Kf+5ZBO7D57gY+cN47bLxjFuUM9UhyatRElDRFqsuqaWZ9fs4P6XN/NexVE+VNCHn316ClNG9mt8YckoShoi0my1tc5za3fw45c2U1J+hAlDevPgTYVccdYgdHfmjklJQ0SabP/RKv7wZhmPLX+fzXsOc8bgXvz8M5OZMeE0svRDvQ5NSUNEklJb6/xly16eXLmdF9fvpqqmlnOG9eEnnzqPq88eomTRSShpiEiDSvcd5bdFpTy9qpSy/cfo2z2XT00bwScKhzNhaO9UhyftTElDRD7g+MkaXtywm6dWbucvW/YCcPG4gdx59Zl8eMJguuZkpzhCSRUlDRHB3dm69wivbirn1U3lLCup4PjJWob17caXrxjPdVMKdNlyAZQ0RDqtIyeqeWNLBa9uKmfppj1srzwGwJiBPbjh/BF8eMJgLhgzQH0VcgolDZFO4ODxk2zccZD1Ow6yYWf0d/PuQzlgeDkAAAuLSURBVFTXOt27ZHPh2AHM+/uxXDI+nxEDdEYh9VPSEOlAjlXVULL3MCXlRygpP8I7u6IE8X7l0b/WGdizKxOH9uayM/K5eNxApozqpz4KSZqShkiGcXd2HTxO2b5jvFdxlE17DrFp1yE27T5M2f5jf61nBiP7d+ecYX24/vxopNPEIb0Z1DsvhdFLpkv7pGFms4AfA9nAg+5+T4pDEmlzR6uq2XngOLsOHA9/j7HjwHG2Vx7l7bID7D968q91u2RnMSa/B1NG9uOG84czJr8nY/J7MHpgD/JydQYhrSutk4aZZQM/BT4MlAIrzWyRu29IbWQi0Y/dTtbWUl3jVNdE0zW1zsmaWo6frOVYVQ3HToZHVQ3HY6aPnQzPq/5WVnm0ip37j7PzwDEOHq/+wPYG9OjC0L7dmDXxNCYO68OI/t0p6NeNkf27k5OtW+NI+0jrpAFMBYrdvQTAzJ4AZgOtnjTuf2kzi97acUqZu3+g3gdLEhcmqhe/vsR1Eq0rQRyJ6iUMrvE42iSWpGJr3rqiei14DS34XGs8ShDVtbXUJvl+18cMuudm061LNnm52fTtnsuIAd2ZNqY/Q/p0Y0ifPE7rk8eQPnkM7p2nswZJC+meNIYB22OelwLT4iuZ2TxgHsCIESOataFBvbpyxuBeH5yRYLRhogGIiS7Olrhe660rcWwJlk36NbRgfUkE2C5xJFxfckNGk9lulkFOdhY5WUZOtpH71+kscrON7CwjNyuLvC7ZdMsNjy5Z5IXp7l1y6JabTV6XLLpkZ+mifpJx0j1pJMXd5wPzAQoLC5v1/98NU0dww9TmJRwRkc4i3RtCy4DhMc8LQpmIiKRAuieNlcB4MxttZl2AG4BFKY5JRKTTSuvmKXevNrPbgcVEQ24fdvf1KQ5LRKTTSuukAeDuzwPPpzoOERFJ/+YpERFJI0oaIiKSNCUNERFJmpKGiIgkzRJdUiGTmVk58F4zFx8I7G3FcFqL4moaxdU0iqtp0jUuaFlsI909v7FKHS5ptISZFbl7YarjiKe4mkZxNY3iapp0jQvaJzY1T4mISNKUNEREJGlKGqean+oA6qG4mkZxNY3iapp0jQvaITb1aYiISNJ0piEiIklT0hARkeS5e8Y/gIeBPcC6uPLpwC+J7jG+Cng7/L08zO8O/BfwDrAeuCdu+SHAi8Ak4I1QZy1wfUyd0cByoBh4EugSM2848ArR7WnXA19OENuAUOcw8JO47X+P6M6FhxO85hbFFlMnD1gBvBXW8a2YeTcA3wA+Hdb9NvA68KG4dfwcuAj4QXgv1wK/B/rG1LkzxPEuMLOez7HZsTT0XiezL4Q6U0J5MXA/ofk2Zn428CbwxwRxnRk+hxPAvyazf7ZWXDH1toV6a4Cipuxrod4d4f39angf1wIvEY3fr6szB9gcHnMaOCYbi6Wh19tm+z3QF3iaaD/dCFzQhOOx3s+hpZ9jEnFNDe/lGqLj42Mxy84iOq6KgTviYm70GG5o+YSfbTJfyun+AP4emMwHk8a3gH8CzgOGhrKzgbIw3R24LEx3Af4HuCpm+VuArwGnA+ND2VBgJ+ELEXgKuCFM/xz4QtwOPjlM9wI2ARPiYusBXAx8PsFOOj2sI9HB06LYYtZjQM8wnUt0sE0PzxeGHf1CoF8ouwpYHreONURfqDOAnFB2L3BvmJ4QdvSuRAf0FiC7NWNp6L1OZl8Iz1eE99yAP8XuC2H+V4HfcGrSqItrEHA+0RdefNJIuH+2Vlwx9bYBAxvYRr37Wqj3CpAPXAZ0D2VfAJ4M0/2BkvC3X5ju18xYGnq9bbbfh8/rczHHfN9k36OGPoeWfo5JxNWdvx1bQ4j+CckhOu62AGPCcm9x6n7f2HHT4PIJP9uGZmbSAxjFB5PGn4E+cWUGVAJdE6zjx8CtMc+fTPQGhjd2fFjX3pgP8wJgcQMxPgt8OFFswM3xO2nMvEQHT6vGFup0B1YT3Yfdwrri/9vuF3cgnAU8lWBdHwMeC9N3AnfGzFtM+E+qNWOp771OZl8IB+I7MfM+Cfwi5nkB0X/dlxOSRqK4gLuJSxr17Z+tEVfccttI/EXd6L4G9Ab+kmDZ8+rKE7wnvwA+2ZJYGjomW3u/B/oAW+P3o2TeoyT2j2Z/jk2JK5SNBnYTJY1TjmtijrVE+2f8cdPQ8vU9OmyfhpkNBE66+4G4Wf8ErHb3E3H1+wIfJfpiwMyygTPcfUNcvalEGXkL0ansfnevDrNLgWH1xDOK6ABc3kBsyb621o4t28zWEP33ssTdl4dY3/KwJ8WYS/RfUp2rgBcSrPazMfWGETU31GmrWOrWMSosszw8T2ZfGBbiqi/GHwH/G6iNKasvrqS0UlyxHHjRzFaZ2bxGthHvSsK+Hyf2PU76c2xiLAmPyXitsN+PBsqBX5nZm2b2oJn1SPI9qvdzaIXPMam4zGyama0namL6fHiNDX0myRw3TflMgQy4CVMLzCBq+/wrM5tI1GwyI648B3gcuN/dS0LxNMKXTky9IcCjRG25tWaWVCBm1hP4HfAVdz9oZp+Kj62JWi02AHevASaFxPl7MzubqJ3zlC9kM7uMaIe7OKZ4JlGTQWy9bwDVwGNJB9E6sXzgvQ7FSe8LiZjZR4A97r7KzC6NmfWBuJqoRXElcLG7l5nZIGCJmb1DdIaUzL42C/hVXCyfAQqBS9oqlia+3pbu9zlEzYRfcvflZvZjon6cjfFxNVFLP8ek4gr/QE00s7OAhWbW2L6X9HHTFB32TIO4/4DNrICoc/Ymd98SV3c+sNndf9TA8r2JOs2/4e7LQnEF0DckHYgOirLYFZtZLtGX2GPu/kyidbfCa2tWbPHcfT9Ru/Ys4g4EMzsXeBCY7e4Voaw7Udvrjph6NwMfAT4d8x9OGVFHdZ1WjyWUJ3qvIbl9oSzElSjGi4BrzGwb8ARwuZn9Oj6uZmhpXKdw97Lwd09Yz9T4bTRgKlGbe10sVxJ1oF4TcwaQ9OeYTCyNHJOJtHS/LwVKw5cvRB3Pk+PXW4+GPoeWfo5NisvdNxJ11J9Nw59JMsdNk4/NetutMu1BTJsxcW15RCMT3gL+McFy3yX6osmKK38d6BWmuxCdun8lwfK/5dROty/GzDPgEeBHcWWJ2hlvJsk+jdaILaZOPn/rdOtGNBhgNvDnmDojiEZWXBi37D8QM+KM6At+A5AfV28ip3aEl5C4I7wlsXzgvW7GvhDfUXl1gjqXAn8kaof+c4L5d5NEn0YbxNUjZp/oEfaRq5LZ18Ln80TM8/OImnrGxy3Xn6jtvV94bAX6NyeWhl5vW+73YZ86I+az+kEy71F9n0NrfY6NxUV03NT104wEdhBd0TaH6Hgazd86sicSt39S/3GTcPn6PhN37xhJg6hpaSdwkihrfx1YEDP/m8AR/jZkbQ3RaJcCorbXjTHlnyP68no5ZvnPhHXHLj8pzBsTdobisLN2jVnu4rD+tTHL/d/Y2EK9bUQdZodD/HUjrL4fnteGv3e3Vmwxy59LNIx0LbAuxHcdcHdMnQeBfTHrLwrlPwEujalXTNQ+Wlfv5zHzvkH0RfQu9Y/+aUksid7rq4maVxrdF8K8wrDdLeG1JeqYvJQoacTHdVr4jA4C+8N073r2z7ltENcYogO+brjyN+K3Ud++BvwrcHNMnf8m6miti2NRzLzPhs+5GLilns+x0Vgaeb1ttt8TDdUtCvvJH4hGvCV7PH7gc2itz7GxuIAbw3u5hmiAyLUx864mGi24heisC5I8bupbvqFHh7yMiJl9Eyh29yeaufxngAJ3v6d1I0vv2GK28SDwoP/tlL++equBae5+MtWxNLB8i97vTIurOdswsyVETSk7Ux1LA8u3yX7fCnG11f7V0rhatH82uO6OmDRERKRtdOSOcBERaWVKGiIikjQlDRERSZqShoiIJE1JQ0REkqakISIiSfv/+HHdwfd1NycAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bgnQCppwjvmw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285
        },
        "outputId": "23fb6e83-7476-4fe0-ab1b-d922dc3d7b93"
      },
      "source": [
        "corona_dataset_aggregated.loc[\"India\"][:75].plot()\n",
        "\n",
        "corona_dataset_aggregated.loc[\"China\"][:75].plot()\n",
        "\n",
        "plt.legend()"
      ],
      "execution_count": 121,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f5cb5177e80>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 121
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MUzI90kKnS_D",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "outputId": "0e4438bc-dfc1-4f30-9d3a-991948789ecd"
      },
      "source": [
        "corona_dataset_aggregated.loc[\"India\"][-75:].plot()\n",
        "\n",
        "corona_dataset_aggregated.loc[\"China\"][-75:].plot()\n",
        "\n",
        "plt.legend()"
      ],
      "execution_count": 122,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f5cb50a8f28>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 122
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zHDJt-Rcjvmz",
        "colab_type": "text"
      },
      "source": [
        "### task 3.1: caculating the first derivative of the curve"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lYyrQBtwjvmz",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "outputId": "a9ba0748-800e-4cba-e1aa-c3da45967e77"
      },
      "source": [
        "corona_dataset_aggregated.loc[\"China\"].diff().plot()"
      ],
      "execution_count": 123,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f5cb502b208>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 123
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5dY0CSj5jvm2",
        "colab_type": "text"
      },
      "source": [
        "### task 3.2: find maxmimum infection rate for China"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xiMac2cyjvm2",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "308857aa-89f8-412e-e92f-754be91bf7ff"
      },
      "source": [
        "corona_dataset_aggregated.loc[\"China\"].diff().max()"
      ],
      "execution_count": 124,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3995.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 124
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5xpXWFB0jvm5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "outputId": "db0b37e1-27d1-40d3-d0ab-e5ca3ac1768f"
      },
      "source": [
        "corona_dataset_aggregated.loc[\"Italy\"].diff().plot()"
      ],
      "execution_count": 125,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f5cb4f25710>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 125
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nykv1Gltjvm8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "43f0835f-8495-465f-930e-0879c94e39be"
      },
      "source": [
        "corona_dataset_aggregated.loc[\"Italy\"].diff().max()"
      ],
      "execution_count": 126,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "8014.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 126
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WgoxP6AYjvnB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "ceba411d-8532-47f0-9186-b892a8a2b467"
      },
      "source": [
        "corona_dataset_aggregated.loc[\"Spain\"].diff().max()"
      ],
      "execution_count": 127,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "6399.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 127
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XZ7Y9GKijvnF",
        "colab_type": "text"
      },
      "source": [
        "### Task 3.3: find maximum infection rate for all of the countries. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5a4XttnIjvnF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "f7c8743b-4c3e-4545-cbd3-004429a090ef"
      },
      "source": [
        "countires = list(corona_dataset_aggregated.index)\n",
        "max_infection_rates = []\n",
        "\n",
        "for c in countires:\n",
        "    max_infection_rates.append(corona_dataset_aggregated.loc[c].diff().max())\n",
        "len(max_infection_rates)"
      ],
      "execution_count": 128,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "188"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 128
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mgKUA332jvnH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "corona_dataset_aggregated['max_infection_rates']=max_infection_rates"
      ],
      "execution_count": 129,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R-EUpfjqjvnJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "10708820-b563-474d-dbf5-14ffe4a087d8"
      },
      "source": [
        "corona_dataset_aggregated.head()"
      ],
      "execution_count": 130,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    .dataframe tbody tr th {\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>1/22/20</th>\n",
              "      <th>1/23/20</th>\n",
              "      <th>1/24/20</th>\n",
              "      <th>1/25/20</th>\n",
              "      <th>1/26/20</th>\n",
              "      <th>1/27/20</th>\n",
              "      <th>1/28/20</th>\n",
              "      <th>1/29/20</th>\n",
              "      <th>1/30/20</th>\n",
              "      <th>1/31/20</th>\n",
              "      <th>2/1/20</th>\n",
              "      <th>2/2/20</th>\n",
              "      <th>2/3/20</th>\n",
              "      <th>2/4/20</th>\n",
              "      <th>2/5/20</th>\n",
              "      <th>2/6/20</th>\n",
              "      <th>2/7/20</th>\n",
              "      <th>2/8/20</th>\n",
              "      <th>2/9/20</th>\n",
              "      <th>2/10/20</th>\n",
              "      <th>2/11/20</th>\n",
              "      <th>2/12/20</th>\n",
              "      <th>2/13/20</th>\n",
              "      <th>2/14/20</th>\n",
              "      <th>2/15/20</th>\n",
              "      <th>2/16/20</th>\n",
              "      <th>2/17/20</th>\n",
              "      <th>2/18/20</th>\n",
              "      <th>2/19/20</th>\n",
              "      <th>2/20/20</th>\n",
              "      <th>2/21/20</th>\n",
              "      <th>2/22/20</th>\n",
              "      <th>2/23/20</th>\n",
              "      <th>2/24/20</th>\n",
              "      <th>2/25/20</th>\n",
              "      <th>2/26/20</th>\n",
              "      <th>2/27/20</th>\n",
              "      <th>2/28/20</th>\n",
              "      <th>2/29/20</th>\n",
              "      <th>3/1/20</th>\n",
              "      <th>...</th>\n",
              "      <th>5/23/20</th>\n",
              "      <th>5/24/20</th>\n",
              "      <th>5/25/20</th>\n",
              "      <th>5/26/20</th>\n",
              "      <th>5/27/20</th>\n",
              "      <th>5/28/20</th>\n",
              "      <th>5/29/20</th>\n",
              "      <th>5/30/20</th>\n",
              "      <th>5/31/20</th>\n",
              "      <th>6/1/20</th>\n",
              "      <th>6/2/20</th>\n",
              "      <th>6/3/20</th>\n",
              "      <th>6/4/20</th>\n",
              "      <th>6/5/20</th>\n",
              "      <th>6/6/20</th>\n",
              "      <th>6/7/20</th>\n",
              "      <th>6/8/20</th>\n",
              "      <th>6/9/20</th>\n",
              "      <th>6/10/20</th>\n",
              "      <th>6/11/20</th>\n",
              "      <th>6/12/20</th>\n",
              "      <th>6/13/20</th>\n",
              "      <th>6/14/20</th>\n",
              "      <th>6/15/20</th>\n",
              "      <th>6/16/20</th>\n",
              "      <th>6/17/20</th>\n",
              "      <th>6/18/20</th>\n",
              "      <th>6/19/20</th>\n",
              "      <th>6/20/20</th>\n",
              "      <th>6/21/20</th>\n",
              "      <th>6/22/20</th>\n",
              "      <th>6/23/20</th>\n",
              "      <th>6/24/20</th>\n",
              "      <th>6/25/20</th>\n",
              "      <th>6/26/20</th>\n",
              "      <th>6/27/20</th>\n",
              "      <th>6/28/20</th>\n",
              "      <th>6/29/20</th>\n",
              "      <th>6/30/20</th>\n",
              "      <th>max_infection_rates</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Country/Region</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Afghanistan</th>\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>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>...</td>\n",
              "      <td>1040</td>\n",
              "      <td>1075</td>\n",
              "      <td>1097</td>\n",
              "      <td>1128</td>\n",
              "      <td>1138</td>\n",
              "      <td>1209</td>\n",
              "      <td>1259</td>\n",
              "      <td>1303</td>\n",
              "      <td>1328</td>\n",
              "      <td>1428</td>\n",
              "      <td>1450</td>\n",
              "      <td>1522</td>\n",
              "      <td>1585</td>\n",
              "      <td>1762</td>\n",
              "      <td>1830</td>\n",
              "      <td>1875</td>\n",
              "      <td>2171</td>\n",
              "      <td>2651</td>\n",
              "      <td>3013</td>\n",
              "      <td>3326</td>\n",
              "      <td>3928</td>\n",
              "      <td>4201</td>\n",
              "      <td>4725</td>\n",
              "      <td>5164</td>\n",
              "      <td>5508</td>\n",
              "      <td>6158</td>\n",
              "      <td>7660</td>\n",
              "      <td>7962</td>\n",
              "      <td>8292</td>\n",
              "      <td>8764</td>\n",
              "      <td>8841</td>\n",
              "      <td>9260</td>\n",
              "      <td>9869</td>\n",
              "      <td>10174</td>\n",
              "      <td>10306</td>\n",
              "      <td>10674</td>\n",
              "      <td>12604</td>\n",
              "      <td>13934</td>\n",
              "      <td>14131</td>\n",
              "      <td>1930.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Albania</th>\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>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>...</td>\n",
              "      <td>783</td>\n",
              "      <td>789</td>\n",
              "      <td>795</td>\n",
              "      <td>803</td>\n",
              "      <td>812</td>\n",
              "      <td>823</td>\n",
              "      <td>851</td>\n",
              "      <td>857</td>\n",
              "      <td>872</td>\n",
              "      <td>877</td>\n",
              "      <td>891</td>\n",
              "      <td>898</td>\n",
              "      <td>898</td>\n",
              "      <td>910</td>\n",
              "      <td>925</td>\n",
              "      <td>938</td>\n",
              "      <td>945</td>\n",
              "      <td>960</td>\n",
              "      <td>980</td>\n",
              "      <td>1001</td>\n",
              "      <td>1034</td>\n",
              "      <td>1039</td>\n",
              "      <td>1044</td>\n",
              "      <td>1055</td>\n",
              "      <td>1064</td>\n",
              "      <td>1077</td>\n",
              "      <td>1086</td>\n",
              "      <td>1114</td>\n",
              "      <td>1126</td>\n",
              "      <td>1134</td>\n",
              "      <td>1159</td>\n",
              "      <td>1195</td>\n",
              "      <td>1217</td>\n",
              "      <td>1250</td>\n",
              "      <td>1298</td>\n",
              "      <td>1346</td>\n",
              "      <td>1384</td>\n",
              "      <td>1438</td>\n",
              "      <td>1459</td>\n",
              "      <td>54.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Algeria</th>\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>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>...</td>\n",
              "      <td>4426</td>\n",
              "      <td>4784</td>\n",
              "      <td>4747</td>\n",
              "      <td>4918</td>\n",
              "      <td>5129</td>\n",
              "      <td>5277</td>\n",
              "      <td>5422</td>\n",
              "      <td>5549</td>\n",
              "      <td>5748</td>\n",
              "      <td>5894</td>\n",
              "      <td>6067</td>\n",
              "      <td>6218</td>\n",
              "      <td>6297</td>\n",
              "      <td>6453</td>\n",
              "      <td>6631</td>\n",
              "      <td>6717</td>\n",
              "      <td>6799</td>\n",
              "      <td>6951</td>\n",
              "      <td>7074</td>\n",
              "      <td>7255</td>\n",
              "      <td>7322</td>\n",
              "      <td>7420</td>\n",
              "      <td>7606</td>\n",
              "      <td>7735</td>\n",
              "      <td>7842</td>\n",
              "      <td>7943</td>\n",
              "      <td>8078</td>\n",
              "      <td>8196</td>\n",
              "      <td>8324</td>\n",
              "      <td>8422</td>\n",
              "      <td>8559</td>\n",
              "      <td>8674</td>\n",
              "      <td>8792</td>\n",
              "      <td>8920</td>\n",
              "      <td>9066</td>\n",
              "      <td>9202</td>\n",
              "      <td>9371</td>\n",
              "      <td>9674</td>\n",
              "      <td>9897</td>\n",
              "      <td>358.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Andorra</th>\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>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>...</td>\n",
              "      <td>653</td>\n",
              "      <td>653</td>\n",
              "      <td>663</td>\n",
              "      <td>676</td>\n",
              "      <td>676</td>\n",
              "      <td>681</td>\n",
              "      <td>684</td>\n",
              "      <td>692</td>\n",
              "      <td>694</td>\n",
              "      <td>698</td>\n",
              "      <td>733</td>\n",
              "      <td>735</td>\n",
              "      <td>738</td>\n",
              "      <td>741</td>\n",
              "      <td>741</td>\n",
              "      <td>744</td>\n",
              "      <td>751</td>\n",
              "      <td>757</td>\n",
              "      <td>759</td>\n",
              "      <td>780</td>\n",
              "      <td>781</td>\n",
              "      <td>781</td>\n",
              "      <td>781</td>\n",
              "      <td>789</td>\n",
              "      <td>789</td>\n",
              "      <td>791</td>\n",
              "      <td>792</td>\n",
              "      <td>792</td>\n",
              "      <td>792</td>\n",
              "      <td>792</td>\n",
              "      <td>796</td>\n",
              "      <td>797</td>\n",
              "      <td>797</td>\n",
              "      <td>797</td>\n",
              "      <td>799</td>\n",
              "      <td>799</td>\n",
              "      <td>799</td>\n",
              "      <td>799</td>\n",
              "      <td>799</td>\n",
              "      <td>57.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Angola</th>\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>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>...</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>18</td>\n",
              "      <td>18</td>\n",
              "      <td>18</td>\n",
              "      <td>18</td>\n",
              "      <td>18</td>\n",
              "      <td>21</td>\n",
              "      <td>24</td>\n",
              "      <td>24</td>\n",
              "      <td>38</td>\n",
              "      <td>38</td>\n",
              "      <td>40</td>\n",
              "      <td>41</td>\n",
              "      <td>42</td>\n",
              "      <td>61</td>\n",
              "      <td>61</td>\n",
              "      <td>64</td>\n",
              "      <td>64</td>\n",
              "      <td>64</td>\n",
              "      <td>64</td>\n",
              "      <td>66</td>\n",
              "      <td>66</td>\n",
              "      <td>77</td>\n",
              "      <td>77</td>\n",
              "      <td>77</td>\n",
              "      <td>77</td>\n",
              "      <td>81</td>\n",
              "      <td>81</td>\n",
              "      <td>81</td>\n",
              "      <td>81</td>\n",
              "      <td>93</td>\n",
              "      <td>93</td>\n",
              "      <td>19.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 162 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                1/22/20  1/23/20  ...  6/30/20  max_infection_rates\n",
              "Country/Region                    ...                              \n",
              "Afghanistan           0        0  ...    14131               1930.0\n",
              "Albania               0        0  ...     1459                 54.0\n",
              "Algeria               0        0  ...     9897                358.0\n",
              "Andorra               0        0  ...      799                 57.0\n",
              "Angola                0        0  ...       93                 19.0\n",
              "\n",
              "[5 rows x 162 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 130
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VvxTroTYjvnM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 130,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FiANPtNWjvnO",
        "colab_type": "text"
      },
      "source": [
        "### Task 3.4: create a new dataframe with only needed column "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FMAGNsjrjvnP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "corona_data=pd.DataFrame(corona_dataset_aggregated[\"max_infection_rates\"])"
      ],
      "execution_count": 131,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8WtLSH22jvnU",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 233
        },
        "outputId": "8c9f86f4-a742-46f6-ca26-47051602a752"
      },
      "source": [
        "corona_data.head()"
      ],
      "execution_count": 132,
      "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>max_infection_rates</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Country/Region</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Afghanistan</th>\n",
              "      <td>1930.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Albania</th>\n",
              "      <td>54.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Algeria</th>\n",
              "      <td>358.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Andorra</th>\n",
              "      <td>57.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Angola</th>\n",
              "      <td>19.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                max_infection_rates\n",
              "Country/Region                     \n",
              "Afghanistan                  1930.0\n",
              "Albania                        54.0\n",
              "Algeria                       358.0\n",
              "Andorra                        57.0\n",
              "Angola                         19.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 132
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IE-mx5kGjvnW",
        "colab_type": "text"
      },
      "source": [
        "### Task4: \n",
        "- Importing the WorldHappinessReport.csv dataset\n",
        "- selecting needed columns for our analysis \n",
        "- join the datasets \n",
        "- calculate the correlations as the result of our analysis"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EHBu7hJdjvnX",
        "colab_type": "text"
      },
      "source": [
        "### Task 4.1 : importing the dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O-k2jixLjvnX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "happiness_report_csv=pd.read_csv(\"/content/sample_data/worldwide_happiness_report.csv\")"
      ],
      "execution_count": 133,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "295eZWmzjvna",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "outputId": "6824bdc1-6945-4e98-fbbe-2f5fc925f4e1"
      },
      "source": [
        "happiness_report_csv.head()"
      ],
      "execution_count": 134,
      "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>Overall rank</th>\n",
              "      <th>Country or region</th>\n",
              "      <th>Score</th>\n",
              "      <th>GDP per capita</th>\n",
              "      <th>Social support</th>\n",
              "      <th>Healthy life expectancy</th>\n",
              "      <th>Freedom to make life choices</th>\n",
              "      <th>Generosity</th>\n",
              "      <th>Perceptions of corruption</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>Finland</td>\n",
              "      <td>7.769</td>\n",
              "      <td>1.340</td>\n",
              "      <td>1.587</td>\n",
              "      <td>0.986</td>\n",
              "      <td>0.596</td>\n",
              "      <td>0.153</td>\n",
              "      <td>0.393</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>Denmark</td>\n",
              "      <td>7.600</td>\n",
              "      <td>1.383</td>\n",
              "      <td>1.573</td>\n",
              "      <td>0.996</td>\n",
              "      <td>0.592</td>\n",
              "      <td>0.252</td>\n",
              "      <td>0.410</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>Norway</td>\n",
              "      <td>7.554</td>\n",
              "      <td>1.488</td>\n",
              "      <td>1.582</td>\n",
              "      <td>1.028</td>\n",
              "      <td>0.603</td>\n",
              "      <td>0.271</td>\n",
              "      <td>0.341</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>Iceland</td>\n",
              "      <td>7.494</td>\n",
              "      <td>1.380</td>\n",
              "      <td>1.624</td>\n",
              "      <td>1.026</td>\n",
              "      <td>0.591</td>\n",
              "      <td>0.354</td>\n",
              "      <td>0.118</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>Netherlands</td>\n",
              "      <td>7.488</td>\n",
              "      <td>1.396</td>\n",
              "      <td>1.522</td>\n",
              "      <td>0.999</td>\n",
              "      <td>0.557</td>\n",
              "      <td>0.322</td>\n",
              "      <td>0.298</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Overall rank Country or region  ...  Generosity  Perceptions of corruption\n",
              "0             1           Finland  ...       0.153                      0.393\n",
              "1             2           Denmark  ...       0.252                      0.410\n",
              "2             3            Norway  ...       0.271                      0.341\n",
              "3             4           Iceland  ...       0.354                      0.118\n",
              "4             5       Netherlands  ...       0.322                      0.298\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 134
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NLXe9B29jvnd",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "9acab74a-5e4f-47fe-b2a9-f2ce3f642567"
      },
      "source": [
        "happiness_report_csv.shape"
      ],
      "execution_count": 135,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(156, 9)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 135
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "inqAI5Yejvng",
        "colab_type": "text"
      },
      "source": [
        "### Task 4.2: let's drop the useless columns "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GCk70Gz_jvng",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104
        },
        "outputId": "b81a546f-44db-4f8e-e6c8-d8ac3aee1611"
      },
      "source": [
        "happiness_report_csv.columns"
      ],
      "execution_count": 136,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Overall rank', 'Country or region', 'Score', 'GDP per capita',\n",
              "       'Social support', 'Healthy life expectancy',\n",
              "       'Freedom to make life choices', 'Generosity',\n",
              "       'Perceptions of corruption'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 136
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GmSWzYCujvnj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "useless_cols=['Overall rank','Score','Generosity','Perceptions of corruption']"
      ],
      "execution_count": 137,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Qy_uyassjvnm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        },
        "outputId": "f8a48f82-0b49-4283-8c74-a875be372501"
      },
      "source": [
        "happiness_report_csv.drop(useless_cols,axis=1,inplace=True)\n",
        "happiness_report_csv.head()"
      ],
      "execution_count": 138,
      "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 or region</th>\n",
              "      <th>GDP per capita</th>\n",
              "      <th>Social support</th>\n",
              "      <th>Healthy life expectancy</th>\n",
              "      <th>Freedom to make life choices</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Finland</td>\n",
              "      <td>1.340</td>\n",
              "      <td>1.587</td>\n",
              "      <td>0.986</td>\n",
              "      <td>0.596</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Denmark</td>\n",
              "      <td>1.383</td>\n",
              "      <td>1.573</td>\n",
              "      <td>0.996</td>\n",
              "      <td>0.592</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Norway</td>\n",
              "      <td>1.488</td>\n",
              "      <td>1.582</td>\n",
              "      <td>1.028</td>\n",
              "      <td>0.603</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Iceland</td>\n",
              "      <td>1.380</td>\n",
              "      <td>1.624</td>\n",
              "      <td>1.026</td>\n",
              "      <td>0.591</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Netherlands</td>\n",
              "      <td>1.396</td>\n",
              "      <td>1.522</td>\n",
              "      <td>0.999</td>\n",
              "      <td>0.557</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Country or region  ...  Freedom to make life choices\n",
              "0           Finland  ...                         0.596\n",
              "1           Denmark  ...                         0.592\n",
              "2            Norway  ...                         0.603\n",
              "3           Iceland  ...                         0.591\n",
              "4       Netherlands  ...                         0.557\n",
              "\n",
              "[5 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 138
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XvxT8R4Hjvnp",
        "colab_type": "text"
      },
      "source": [
        "### Task 4.3: changing the indices of the dataframe"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UWFQ_hFLjvnq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "happiness_report_csv.set_index(\"Country or region\",inplace=True)"
      ],
      "execution_count": 139,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r6j5Il2vjvns",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 233
        },
        "outputId": "36c8056a-74f7-48cd-fe86-1f795e0eb817"
      },
      "source": [
        "happiness_report_csv.head()"
      ],
      "execution_count": 140,
      "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>GDP per capita</th>\n",
              "      <th>Social support</th>\n",
              "      <th>Healthy life expectancy</th>\n",
              "      <th>Freedom to make life choices</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Country or region</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Finland</th>\n",
              "      <td>1.340</td>\n",
              "      <td>1.587</td>\n",
              "      <td>0.986</td>\n",
              "      <td>0.596</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Denmark</th>\n",
              "      <td>1.383</td>\n",
              "      <td>1.573</td>\n",
              "      <td>0.996</td>\n",
              "      <td>0.592</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Norway</th>\n",
              "      <td>1.488</td>\n",
              "      <td>1.582</td>\n",
              "      <td>1.028</td>\n",
              "      <td>0.603</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Iceland</th>\n",
              "      <td>1.380</td>\n",
              "      <td>1.624</td>\n",
              "      <td>1.026</td>\n",
              "      <td>0.591</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Netherlands</th>\n",
              "      <td>1.396</td>\n",
              "      <td>1.522</td>\n",
              "      <td>0.999</td>\n",
              "      <td>0.557</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                   GDP per capita  ...  Freedom to make life choices\n",
              "Country or region                  ...                              \n",
              "Finland                     1.340  ...                         0.596\n",
              "Denmark                     1.383  ...                         0.592\n",
              "Norway                      1.488  ...                         0.603\n",
              "Iceland                     1.380  ...                         0.591\n",
              "Netherlands                 1.396  ...                         0.557\n",
              "\n",
              "[5 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 140
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k9heAl1hjvnu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 140,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UfbHbuuXjvnv",
        "colab_type": "text"
      },
      "source": [
        "### Task4.4: now let's join two dataset we have prepared  "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XsOOBioKjvnw",
        "colab_type": "text"
      },
      "source": [
        "#### Corona Dataset :"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FG-s6dWFjvnw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 233
        },
        "outputId": "0d3980b4-e730-4633-f7e4-30bc65297f2a"
      },
      "source": [
        "corona_data.head()"
      ],
      "execution_count": 141,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "    }\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>max_infection_rates</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Country/Region</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Afghanistan</th>\n",
              "      <td>1930.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Albania</th>\n",
              "      <td>54.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Algeria</th>\n",
              "      <td>358.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Andorra</th>\n",
              "      <td>57.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Angola</th>\n",
              "      <td>19.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                max_infection_rates\n",
              "Country/Region                     \n",
              "Afghanistan                  1930.0\n",
              "Albania                        54.0\n",
              "Algeria                       358.0\n",
              "Andorra                        57.0\n",
              "Angola                         19.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 141
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pzFXHUaUjvny",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "3cc216ba-90b3-44a5-9291-8c9ca34b884d"
      },
      "source": [
        "corona_data.shape"
      ],
      "execution_count": 142,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(188, 1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 142
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WrG3fvmtjvn1",
        "colab_type": "text"
      },
      "source": [
        "#### wolrd happiness report Dataset :"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RdB3f6fPjvn2",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "86587526-7421-4a66-bfe2-ef2db8312a64"
      },
      "source": [
        "happiness_report_csv.shape"
      ],
      "execution_count": 143,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(156, 4)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 143
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zvfdUBpVjvn4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 219
        },
        "outputId": "2d0ac507-f128-423d-ffaa-3555bdcac06b"
      },
      "source": [
        "data=corona_data.join(happiness_report_csv, how=\"inner\")\n",
        "data.head()"
      ],
      "execution_count": 144,
      "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>max_infection_rates</th>\n",
              "      <th>GDP per capita</th>\n",
              "      <th>Social support</th>\n",
              "      <th>Healthy life expectancy</th>\n",
              "      <th>Freedom to make life choices</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Afghanistan</th>\n",
              "      <td>1930.0</td>\n",
              "      <td>0.350</td>\n",
              "      <td>0.517</td>\n",
              "      <td>0.361</td>\n",
              "      <td>0.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Albania</th>\n",
              "      <td>54.0</td>\n",
              "      <td>0.947</td>\n",
              "      <td>0.848</td>\n",
              "      <td>0.874</td>\n",
              "      <td>0.383</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Algeria</th>\n",
              "      <td>358.0</td>\n",
              "      <td>1.002</td>\n",
              "      <td>1.160</td>\n",
              "      <td>0.785</td>\n",
              "      <td>0.086</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Argentina</th>\n",
              "      <td>3628.0</td>\n",
              "      <td>1.092</td>\n",
              "      <td>1.432</td>\n",
              "      <td>0.881</td>\n",
              "      <td>0.471</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Armenia</th>\n",
              "      <td>1013.0</td>\n",
              "      <td>0.850</td>\n",
              "      <td>1.055</td>\n",
              "      <td>0.815</td>\n",
              "      <td>0.283</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             max_infection_rates  ...  Freedom to make life choices\n",
              "Afghanistan               1930.0  ...                         0.000\n",
              "Albania                     54.0  ...                         0.383\n",
              "Algeria                    358.0  ...                         0.086\n",
              "Argentina                 3628.0  ...                         0.471\n",
              "Armenia                   1013.0  ...                         0.283\n",
              "\n",
              "[5 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 144
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JgyULhQ0jvn8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "efb291ff-8b2b-4ecb-f1ed-b3f451bf9980"
      },
      "source": [
        "data.shape"
      ],
      "execution_count": 145,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(144, 5)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 145
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "y0K5elCljvn_",
        "colab_type": "text"
      },
      "source": [
        "### Task 4.5: correlation matrix "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N1V5Z_OljvoA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "outputId": "2f838931-ec32-4020-ace0-a8d13a7b5833"
      },
      "source": [
        "data.corr()"
      ],
      "execution_count": 146,
      "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>max_infection_rates</th>\n",
              "      <th>GDP per capita</th>\n",
              "      <th>Social support</th>\n",
              "      <th>Healthy life expectancy</th>\n",
              "      <th>Freedom to make life choices</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>max_infection_rates</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.121520</td>\n",
              "      <td>0.106305</td>\n",
              "      <td>0.122503</td>\n",
              "      <td>0.040042</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GDP per capita</th>\n",
              "      <td>0.121520</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.757521</td>\n",
              "      <td>0.859431</td>\n",
              "      <td>0.394799</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Social support</th>\n",
              "      <td>0.106305</td>\n",
              "      <td>0.757521</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.751632</td>\n",
              "      <td>0.456317</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Healthy life expectancy</th>\n",
              "      <td>0.122503</td>\n",
              "      <td>0.859431</td>\n",
              "      <td>0.751632</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.423146</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Freedom to make life choices</th>\n",
              "      <td>0.040042</td>\n",
              "      <td>0.394799</td>\n",
              "      <td>0.456317</td>\n",
              "      <td>0.423146</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                              max_infection_rates  ...  Freedom to make life choices\n",
              "max_infection_rates                      1.000000  ...                      0.040042\n",
              "GDP per capita                           0.121520  ...                      0.394799\n",
              "Social support                           0.106305  ...                      0.456317\n",
              "Healthy life expectancy                  0.122503  ...                      0.423146\n",
              "Freedom to make life choices             0.040042  ...                      1.000000\n",
              "\n",
              "[5 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 146
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aWrE3G4GjvoF",
        "colab_type": "text"
      },
      "source": [
        "### Task 5: Visualization of the results\n",
        "our Analysis is not finished unless we visualize the results in terms figures and graphs so that everyone can understand what you get out of our analysis"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fYE9jiHWjvoG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 219
        },
        "outputId": "d0af9b44-a242-414c-d02c-14fedb357e32"
      },
      "source": [
        "data.head()"
      ],
      "execution_count": 147,
      "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",
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              "\n",
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              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>max_infection_rates</th>\n",
              "      <th>GDP per capita</th>\n",
              "      <th>Social support</th>\n",
              "      <th>Healthy life expectancy</th>\n",
              "      <th>Freedom to make life choices</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Afghanistan</th>\n",
              "      <td>1930.0</td>\n",
              "      <td>0.350</td>\n",
              "      <td>0.517</td>\n",
              "      <td>0.361</td>\n",
              "      <td>0.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Albania</th>\n",
              "      <td>54.0</td>\n",
              "      <td>0.947</td>\n",
              "      <td>0.848</td>\n",
              "      <td>0.874</td>\n",
              "      <td>0.383</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Algeria</th>\n",
              "      <td>358.0</td>\n",
              "      <td>1.002</td>\n",
              "      <td>1.160</td>\n",
              "      <td>0.785</td>\n",
              "      <td>0.086</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Argentina</th>\n",
              "      <td>3628.0</td>\n",
              "      <td>1.092</td>\n",
              "      <td>1.432</td>\n",
              "      <td>0.881</td>\n",
              "      <td>0.471</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Armenia</th>\n",
              "      <td>1013.0</td>\n",
              "      <td>0.850</td>\n",
              "      <td>1.055</td>\n",
              "      <td>0.815</td>\n",
              "      <td>0.283</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             max_infection_rates  ...  Freedom to make life choices\n",
              "Afghanistan               1930.0  ...                         0.000\n",
              "Albania                     54.0  ...                         0.383\n",
              "Algeria                    358.0  ...                         0.086\n",
              "Argentina                 3628.0  ...                         0.471\n",
              "Armenia                   1013.0  ...                         0.283\n",
              "\n",
              "[5 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 147
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RanXKtfVjvoL",
        "colab_type": "text"
      },
      "source": [
        "### Task 5.1: Plotting GDP vs maximum Infection rate"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ulhedW7qjvoL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        },
        "outputId": "130a459b-6d84-42e7-b3da-acd5460d01b1"
      },
      "source": [
        "x=data[\"GDP per capita\"]\n",
        "y=data[\"max_infection_rates\"]\n",
        "sns.scatterplot(x,np.log(y+1))"
      ],
      "execution_count": 148,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f5cb4f1eef0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 148
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ir69i1fptI58Jm1IrT1FN7RuXi/RcHkUKDmZ2PHBo6XzuflPw+901ZhNpa43mmvOYoy6/cu+fPJ4XX3mVc65fPWYrndJt3bx9N5t37B613LAptfI6kG0De9quCWlWhE4rmdnNwFXAW4Bjgp/INeDSXtrpOcbttC1JGB52Bof28g/veSPXnT2Pow6eyoUnvG7kmQsQLq3TTEqtfN7lK9dzzZlHZz49l0dR7hzmA3M8rravkntZbtsdVZhtyWpTxVaotn+uXDS3ocd5NpNSK5+32Fopiec+dLooFdJrgNckVRDJn6yOJ9PIHUC9bWnHgdWiqLZ/Llu+mikTxjXU6mqs4SvqKZ13/ykTmDqx8Pes/QqvFRjiESU4zADWmtldZnZn8Sepgkn2ZbGtfqMn8XrbktVA2Cq19s+k8d25aHUl0UVJK12RVCEkn7I4nkytk3i93ri1tqWvt5vN23eza3AolkCY19RUrf3T09VVM0WUxLbmdf/lUeg7B3f//xSeIT0l+HkymCYdKott9Ru9m6m2LTedfywvvlIY+uHnL2xvutNa2LuaLFaMj/VdV0sRJZGG6/TUXqtFeYb0nwFLgfspDJXx+8Cl7n5bYqUrobGVsilrV3LNjOMz1jOZjzp4Kh87efbIGP+NVL6HfXZxViv5o3zXSYynlNcxmtLWirGVLgeOcfdNwQr7gR8ALQkOkk3NttWPM7gMDzvdXXDjecew/qXCA2F2De7lkOkTQ93NlG/Lxq27Rk5Eq9Zv46q71rFk4Rx+9zVT6OvtiVzWMHc1jabFWiHKd51EfVQW67jaWZQK6a5iYAhsiTj/KGY21cxuM7Ofm9mTZvbmRpfV6bKYhggjzjRBcVmfWL6aLTsGWXLHmpHHO+4eGm6ofOVjAq1av41Pf3ctfb09kVvYVFseVKam6g1Sl5fvN4mxo/IyHlW7iHJy/37QUulcMzsX+Hfge02s+/PA99399cAbgSebWFbHynMeNs4WQMVlLZp3MB/91uOxLLOZOpVqATvM8uIcpC5NSdRHZbGOq51Fep6DmS0CFgQvf+zutze0UrN9gceAw8N2qkurziFrOfVySedhk9z+OJ4VUL6sWxcfxxnLHoplmdDY9o9VbwBjP6Sm2rxLT5/L576/bmQsooOm9XHr4uNqzp+V41WtlbKhJc9zcPflwPKoK6niMGAzcIOZvRFYCXzY3XeWfsjMFgOLAWbNmhXDaqPJcuVgUZJ52KS3P86msMVlxT3WTiN1KvXqDcZaXthB6jZsHeCj33p81PeRteM1ibGj8jgeVV7VTSuZ2X8Gv7eb2SslP9vN7JUG19sDHA38s7sfBewEPlH+IXdf5u7z3X1+f39/g6saLUp+Pg8dn2qlIYCm89NJb3+caYLispavXM+Vi+ammnpoNmCXNg3t7emuOkjdtoE9Fd9HFo7XvNZ/SaW6dw7u/pbg95QY17sB2ODuPw1e30aV4BC3qFdWeWgdUe0B7EtPn8vFX1/F5h27m7pyTHr74xy2urisz5w2l+HhYb55wZtx91hSD1FTGXHeEVX7fq9cNJer7loHjP4+Sr+v4qB4U/vGMTi0l+FhT/zuIWt3LtKc0GklM7vZ3c+uNy0Md3/BzNab2Wx3XwecBKyNupyoojYTzGIP4HKlJ9iBPXt5etOOUfnp8u2LcqJrxfbHmSZIIuXQyAmv2gm90buXat/vVXeNrn8ofh/F76t/8vim+2Q0IsvNcCW6KHUObyh9YWY9wLwm1v0h4Gtm1gs8A5zXxLJCiXolHOc/eZKKJ8WNW3dx3o2PjHqvdPuinujysv1JauSEF/eDfIrf7/Cws3P30Eiaqfz7KH5fL7z86khgCFvmOOThTlvCqxsczOyTwF8DfSV1DAYMAssaXbG7P0aLnwcR9Uo4T0/rgvrbF/VEl7ftT0KjJ7ykKmPH+j6K708aH30Y7Tjk4U5bwqtbIe3unw3qG5a6+z7BzxR3n+7un2xBGWPTSAVoM0MLt1q97WvkRJen7U9C1jpe1fs+urqMyeN7uOHcY7h18XEjD+VpRZnVD6G9REkrPWxm+7r7y1Do4Qyc4O7fSaZo8Wv3K+F629fIlV2ntyvPW2pteNh58ZXdLLljzagGCjP3mZB4mZP4/+r04y9NUQbee8zd31Q2bVXQFDVxGniveVHrHNT6pCBPJ6hanSK/fdHx7D9lQooli07HXzxa0QmuWgoqUic6SVfUK7t2bn0S9oSfp8AAtVOHexocXypN7Xz85UGUk/sKM/tH4EvB6w9S6NksOZL2yJpZEPaKdGhomHWbtnPBzStzc+Wa5UrhqIG2XY+/vIgy8N6HKLRQuhX4BvAqhQAhLdLq3qdZq4yNS5iexMPDzm9eHhgJDLU+lzVZrRRuZIDIdj3+8iL0nUMw7tEnzGxS+RhIUl+z6Yk08q+NVMamkYZJ4op0y85BNm3fnbsr16w2umgkRZS3xgDtJkoP6eOBLwOTgVnBgHkXuPtFSRWuXcRxYk8j/xr1RJNGAGtknWFSL4NDe9myczCzKZqxtGJwulakiLIa6DpFlLTS1cDJFB7yg7s/Drw1iUK1mzgGREsr/xqln0OU7YwrRdbIvg37XIVqg/hdd/a8XF+5xrHfW5ki6vR+NmmKOmT3erNRX052768zJI4Te5YrGovCbmecdxhJXZFOn9TLR94+m6vvKTwadPqkXvafMp7X7tuX2xNUXPtdKaLOEOXOYX2QWnIzG2dmH0NPbwsljoq1rFY0lgq7nXEOLZ3UFWnpKK9HvnYfDpk+iYOmTaSnp+En46Yurv0+1qNMa909lAbkBy47kdsvWpDpVl8SLThcSKF10oHARuBNqLVSKHGc2PPwzxV2O+NMkSUZNLOS0ogrBRfXfh/rUaZjpZeysj8lnDAD713p7pcBJ7r7mS0oU9tptmKtvPLvgJRTG7UqI8NuZ5wpsnavtIwzBRfXfh/rGRObd+xWJ7U2UXf4DDN7ApgLrHT3o1tSqio6dfiMrA0hEEd5srZNWRbnM8Lj3O/Dw87zLxceV7ptYA/X3v/0yDMmGn1etyQjyeEzvg9sBSYHQ3Yb4MXf7r5P1JVKeFkbQiCO8jR6tZ+loSzi6LcSZv4olfz1lhf3k/d6e7r56Lcez3QjCWlcmMeEXgpcamZ3uPupLSiTlMjaEAJxlSdqW/ws3W00W5Za88/cZzwDg6NP2mFSQVHK08h+rxV0wrRAihJEGwm4WbpgaDehK6QVGNKRtSEEmilPMxWrcbZwalazZak1/+PrX67oNxCmwj2pfVOvP0O9RhJR+kM00neikXkkvNDBwczebWZPmdnLZvaKmW0veTKcJGRa3zi+/he/x20Xvpnrzp7HH83ZP9UmrI22Dmr2HzlLd1DNlqXW/BN7u0f+Lp7cw7RSS2rfhAk6Y7VAihK0GglwWbpgaEdROsF9Dninu6tvQ4sMDztPbd4x6rb9urPncUT/5NSGl240b91sXUWWOgE2W5Za828b2DPyuvTkXi8VlNS+SSoIVpu/kXVl6YKhHUXp5/CiAkNrVTuhXnDzSraWnESKWnmL3Uh79Wb/kbPUCbBaWW46/1gcD5Uyqzb/0tPncu39T498JsrJPal902xKM8r8jawraynXdhPlSXCfB14DfAfYXZzu7t9OpmijdWJT1o1bd7HgyvsqpldrKhhnk8ckxFG+LFU+lpalr7ebF1/ZHamCunT+cT1d7Hh1iHOuf7ip5sFx75ukKt6rzd/IurLUSCHLGm3KGiU43FBlsrv7+VFX2ohODA5RTqhRAkka2vkfuR0CX631t6rJbtTPNjNPp0n8MaHufl7UhXeCJA/OKIOVZSknX00792SOI/fd1WVMn9Q7cixt2TnYsv1TL3A3c+cZZf5G1tWK4ck7VZjhMz7u7p8zsy9Q6Pw2irtfkkjJciDpq+EoJ9Q8jHrZrv/IcQTmNO+skupoqav6fAtz51CshO6snE4Irei9HPaEWh5IxvV00dNlPP/yAH293QwNO3uGhunt6WZa3zi2DuzRP21M4gjMafaET6LVTzunETtFmB7S/xb8/spYnzOzL7j7h6Ks3My6KQSdje6+MMq8WZC1pnTFQFL6j9k/eTwfP2U2l962euSf9Nqz5vFP9/6Cu9du0j9tDOJImaV5LCWRkszasC8SXZyD0y9oYJ4Pk+NnQmS1KV3pP+aFJ7xuJDBA4Z/0wq+uZNG8g0deq+NQNNV6ejc7HPW4nq7UjqUkmsJm7cJJokvtySVmdhDwJxSeS51LWWp7D/990to1ODTyjzm1b1zVf9KpfeNGvdY/bThJ9CcZHnZ2vDrE0tPnpnIsJfGskKxeOEl4kR4TGrP/B3wcmJJiGZqSZguc8sq+aX3jRnpTL1k4ZyRNsG1gT93euPqnDS+JdMmWnYOcc/3D9E8ez5KFc5jaN45dg3uZuU/rHogTd2OBPDSQkLHFGRxCH8VmthDY5O4rzeyEMT63GFgMMGvWrMgFatVwEknnUMcKBKXDanz+B79gw9YBrr3/aa5cNJfLlq/m2vufZunpc6vWOUD6dzt5k0S6pLjMYg/4ogcuOxEmNbzYVLVz0+VOETo4mNkEd3+1bNoMd/9t8PLzEda7AHiXmb0DmADsY2ZfdfezSj/k7suAZVDoBBdh+W3TWqLadpQGAvjvYTWWLJzD3Ws3sWr9Nq66ax1LFs7hd18zhckTevj2RcePaq30mdPm8ql36p82qiQqb+NeZlY6k7Vr0+VOEaXO4REzO674wswWAQ8WX7v7jWEX5O6fdPeD3P1Q4L3AD8sDQ7PaZcTGWuMrFSuUizZsHRh19b9q/TY+/d219PX2sN+k8ew/ZcJIZWlPT5ee5dugJOqZ4lxmXoe+jus52RKfKGml/wlcb2b3A68FpgN/mESh4tAurSVqbUf5ieOgaX3sP2X8yBWo0kXJSCJdEucyG6kTSbvZabvc5bebKMNnPGFmnwFuBrYDb3X3Dc0WwN3vB+5vdjnlsj6cRFhmVnU7+qsEgtfu2xf6BKPeq41LIl0S1zLzOPR12sFJqotS5/CvwOuAucDvAN8NOr59KanCNaNdWkt0GyOVy8XtuHLRXCb1dlcNBGH+mXSl1r4auShK+0Iq7eAk1UWpc3gCONHdn3X3u4DfA45OpljNS6Ltdhq6urr4yoPPsmThHG5dfBxLFs7hKw8+i9N4p6t2qY/JujTy6I3UX6TdX0d9IrIp9JDdaUtiyO48pFbiGFO/fBuff3kg08N7J60V33tad2fDw862gUEGBvey12HCuC5mTKp/8ZDm/4LuZJOV+JDdZnYE8FlgDoXmpwC4++FRV5oFeTkgm6msrLWNM/cZ3xb1MY1o1feeRh691rbNmBRu4Ma08vtxVcjn4WIvT6KklW4A/hkYAk4EbgK+mkShWiFPqZVGx+2ptY1Dw56pYT9aqVXfexp59Dwd0+WaHZsqC81x202Upqx97n6vmZm7PwdcYWYrgb9NqGyJSqsSrJVXN7W2cc/QcMf2Xm3V955GJW8nV+yqxVP8otw57DazLuApM7vYzE4DJidUrsSlUQnW6qubsbax2Su1vGrV955GJW8nV+x2cmBMSpTg8GFgInAJMA84CzgniUK1Qhr/vK2+7U+7FUoWtWqfpNFarpO/704OjEkJ3VrJzOYDlwOHAMXxnt3d5yZUtlFa1VoJSCzts3Hrrpa3ElIlXaUs7pO4ypTFbWuFvDQwSUPirZWArwGXUujvMBx1RVlU3kIj6QMsjTy0Bj+rlLV9Eudxl7VtaxWNAhu/KGmlze5+Z9AJ7rniT2IlS0HSaZ9Ovu2X2vLcyihLOrUeLSlR7hw+ZWZfBu4Fdhcnuvu3Yy9VSpKu1NLVjVSjylTJoijB4Tzg9RTqG4ppJQfaJji0Iu3Tqbf9UlvaYxuJVBMlrXSMu8939/e5+3nBz/mJlSwFSvu0lsbwL9BxJ1kUpbXSDcBSd1+bbJGqS6K1UjWd2tqj1dS6ZDQdd5KUVrRWOg54zMyepVDnYLSwKWurKO3TGurROpqOO8maKMHhlMRKIR1HlbAi2RblSXBt1WxV0qVKWJFsi1IhLRKbMJWwqrAWSU+UtJJIbOr1+VCFtUi6dOcgqRmrR6t6DYukS8FBMkkV1iLpUnCQTCYPktMAAAu+SURBVNIQzCLpUnCQTFKvYZF0qUJaMkmDFIqkS8FBMku9hkXSk0paycwONrP7zGytmf3MzD6cRjlERKS6tO4choCPuvujZjYFWGlm96Q1qF9eabC21tG+lk6TSnBw9+eB54O/t5vZk8CBgIJDSOok1jra19KJUm+tZGaHAkcBP63y3mIzW2FmKzZv3tzqomWaOom1jva1dKJUg4OZTQaWA//L3V8pf9/dlwUPGJrf39/f+gJmmDqJtU5e9rXGopI4pdZayczGUQgMX2un51C3ikY1bZ087GulviRuabVWMuBfgSfd/R/TKEPeqZNY6+RhXyv1JXFL685hAXA28ISZPRZM+2t3/15K5ckddRKLT72WSHnY13lJfUl+pNVa6T8pPGZUmqBOYs0Lm47J+r7OQ+pL8iX11koiaWqXdEweUl+SLxo+Qzpau6Rj8pD6knxRcJCO1k7pmKynviRflFaSjqZ0jEh1unOQjqZ0jEh1Cg7S8ZSOEamktJKIiFTQnYM0TMNY16Z9I3mn4CAN0Vg+tWnfSDtQWkka0i6dx5KgfSPtQMFBGtIunceSoH0j7UDBQRpS7DxWKq+dx+KmfSPtQMFBGqLOY7Vp30g7MPd8PC1q/vz5vmLFirSLISXUIqc27RvJCjNb6e7zo86n1krSMHUeq037RvJOaSUREamg4CAiIhUUHEREpIKCg4iIVFBwEBGRCgoOIiJSQcFBREQqKDiIiEgFBQcREamg4CAiIhVSCw5mdoqZrTOzX5rZJ9Iqh4iIVEplbCUz6wa+BLwd2AA8YmZ3uvvaNMojIvmmgQ7jl9bAe8cCv3T3ZwDM7BvAqYCCg4hEoseyJiOttNKBwPqS1xuCaSIikeixrMnIdIW0mS02sxVmtmLz5s1pF0dEMkiPZU1GWsFhI3BwyeuDgmmjuPsyd5/v7vP7+/tbVjgRyQ89ljUZaQWHR4AjzOwwM+sF3gvcmVJZRCTH9FjWZKRSIe3uQ2Z2MXAX0A1c7+4/S6MsIpJvXV3G7JlTuP2iBWqtFKPUHhPq7t8DvpfW+kWkfeixrPHLdIW0iIikQ8FBREQqKDiIiEgFBQcREamg4CAiIhXM3dMuQyhmthl4rsHZZwC/jbE4raAyt04ey60yt0Y7lPkQd4/cizg3waEZZrbC3eenXY4oVObWyWO5VebW6OQyK60kIiIVFBxERKRCpwSHZWkXoAEqc+vksdwqc2t0bJk7os5BRESi6ZQ7BxERiSD3wcHMTjGzdWb2SzP7RJX3x5vZrcH7PzWzQ0ve+2QwfZ2ZnZyhMv+Vma01s9Vmdq+ZHVLy3l4zeyz4adkw5yHKfK6ZbS4p21+UvPc+M3sq+Hlfhsp8dUl5f2Fm20reS2s/X29mm8xsTY33zcz+Kdim1WZ2dMl7ae3nemU+MyjrE2b2oJm9seS9XwXTHzOzFRkq8wlm9nLJMfC3Je+NeVylWOZLS8q7JjiG9wvei76f3T23PxSG+34aOBzoBR4H5pR95iLg2uDv9wK3Bn/PCT4/HjgsWE53Rsp8IjAx+Psvi2UOXu/I6H4+F/hilXn3A54Jfk8L/p6WhTKXff5DFIaOT20/B+t9K3A0sKbG++8A/gMw4Djgp2nu55BlPr5YFuCPi2UOXv8KmJHB/XwC8N1mj6tWlrnss+8EftjMfs77ncOxwC/d/Rl3HwS+AZxa9plTga8Ef98GnGRmFkz/hrvvdvdngV8Gy0u9zO5+n7vvCl4+ROFJeWkKs59rORm4x91fcvetwD3AKQmVs1TUMv85cEsLyjUmd/8R8NIYHzkVuMkLHgKmmtkBpLef65bZ3R8MygTZOJ7D7OdamvlfaErEMjd9POc9OBwIrC95vSGYVvUz7j4EvAxMDzlvEqKu9/0UrhSLJgTP1X7IzP40iQJWEbbMi4L0wW1mVnwMbOb3c5C2Owz4YcnkNPZzGLW2K639HFX58ezA3Wa20swWp1SmWt5sZo+b2X+Y2RuCaZnfz2Y2kcKFwfKSyZH3c2oP+5H6zOwsYD7wByWTD3H3jWZ2OPBDM3vC3Z9Op4Sj/Btwi7vvNrMLKNyt/WHKZQrrvcBt7l76RPqs7ufcMrMTKQSHt5RMfkuwn/cH7jGznwdXyGl7lMIxsMPM3gF8Bzgi5TKF9U7gAXcvvcuIvJ/zfuewETi45PVBwbSqnzGzHmBfYEvIeZMQar1m9jbgcuBd7r67ON3dNwa/nwHuB45KsrCBumV29y0l5fwyMC/svAmJst73UnYLntJ+DqPWdqW1n0Mxs7kUjotT3X1LcXrJft4E3E5rUrt1ufsr7r4j+Pt7wDgzm0HG93NgrOM5/H5uRUVKUj8U7nyeoZASKFYOvaHsMx9kdIX0N4O/38DoCulnaE2FdJgyH0Wh0uuIsunTgPHB3zOAp2hBZVjIMh9Q8vdpwEPB3/sBzwZlnxb8vV8Wyhx87vUUKuss7f1csv5DqV1R+ieMrpB+OM39HLLMsyjU6R1fNn0SMKXk7weBUzJS5tcUjwkKJ9JfB/s81HGVRpmD9/elUC8xqdn93JINSnhnvQP4RXAyvTyY9ncUrrgBJgDfCg7Oh4HDS+a9PJhvHfDHGSrzD4AXgceCnzuD6ccDTwQH5BPA+zNU5s8CPwvKdh/w+pJ5zw/2/y+B87JS5uD1FcDfl82X5n6+BXge2EMhn/1+4ELgwuB9A74UbNMTwPwM7Od6Zf4ysLXkeF4RTD882MePB8fO5Rkq88Ulx/NDlAS2asdVFsocfOZcCg1tSudraD+rh7SIiFTIe52DiIgkQMFBREQqKDiIiEgFBQcREamg4CAiIhUUHCR3zGymmX3dzJ4JhgP4iZmdFrxXHE1zVTBy5o/MbGHJvFeY2caSkSvfld6WRGNm3zOzqcHPRWmXR9qbgoPkSjBo4neAH7n74e4+j0LnxtLB3H7s7ke5+2zgEuCLZnZSyftXu/ubgPcA15tZbP8HwZDaifxfufs73H0bMJXCaMMiiVFwkLz5Q2DQ3a8tTnD359z9C9U+7O6PUej4dnGV954Ehij0gh4R3F3cHNyRPGVmHyh571IzeyQYYPB/B9MODe5SbgLWMHp4BczsmOA5Bo+b2cNmNiWY58dm9mjwc3zw2ROCu51/D5Z5bTHYBGPyzwD+HnhdcPez1MwmW+G5H48GY/a3ZJRQaW8aeE/y5g0UBkWL4lHg0vKJZvZ7wDCwuco8cykMTzEJWGVm/w4cSWHwtWMp9FS+08zeSmFohSOA93lhGO3SdfQCtwJnuPsjZrYPMABsAt7u7q+a2REUer/OD2Y7lsLzRp4Dvg+8m8Jw80WfAI4M7n6KY4ad5u6vBMHjITO709XDVZqg4CC5ZmZfojDK56C7H1PrY2WvPxKMeLudwkm72kn0DncfAAbM7D4KJ+y3AH8ErAo+M5lCUPg18Fx5YAjMBp5390egMKBbUO5JFNJdbwL2Ar9TMs/DXhjwDzO7JVjvbdRmwP8NAtUwhSGkZwIvjDGPyJgUHCRvfgYsKr5w9w8GV8tjPfrwKODJktdXu/tVddZTHjCcwkn4s+5+XekbVnj07M46yyv3EQrjZ72RQnr31TrrHsuZQD8wz933mNmvKIwpJtIw1TlI3vyQwoN4/rJk2sRaHw6Gil5CYbC6KE41swlmNp3CIyMfAe4CzjezycGyDwzGxx/LOuAAMzsmmGdKydDxz7v7MHA2hcdPFh1rZocFdQ1nAP9ZtsztwJSS1/sCm4LAcCJwCCJN0p2D5Iq7e/BktqvN7OMU6gt2ApeVfOz3zWwVhaCxCbjE3e+NuKrVFEaXnQF82t1/A/zGzH4X+Emh0RQ7gLMopIVqlXfQzM4AvmBmfRTqG94GXAMsN7NzKNQrlN55PAJ8EfgfQRluL1vmFjN7wAoPmv8P4Erg38zsCQp3UD+PuK0iFTQqq0gZM7sC2BEi9ZTEuk8APubuC+t9ViRJSiuJiEgF3TmIiEgF3TmIiEgFBQcREamg4CAiIhUUHEREpIKCg4iIVFBwEBGRCv8FtM5XRCoIPaYAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lvbEjjM7jvoQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        },
        "outputId": "4f7214e3-2d7b-450a-f8ea-d6a5f1053858"
      },
      "source": [
        "sns.regplot(x,np.log(y+1))"
      ],
      "execution_count": 149,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f5cb4f0cf60>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 149
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1ELkSBE9jvoS",
        "colab_type": "text"
      },
      "source": [
        "### Task 5.2: Plotting Social support vs maximum Infection rate"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r3Zw-6xEjvoT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        },
        "outputId": "e0f0ce75-f1f0-43a7-b5d5-a8a13d761a8a"
      },
      "source": [
        "x=data[\"Social support\"]\n",
        "y=data[\"max_infection_rates\"]\n",
        "sns.scatterplot(x,np.log(y+1))"
      ],
      "execution_count": 150,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f5cb4e0be48>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 150
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2K_U-FH-jvoX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        },
        "outputId": "ddf30bc9-8366-4d09-8c8a-2f232be5764c"
      },
      "source": [
        "sns.regplot(x,np.log(y+1))"
      ],
      "execution_count": 151,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f5cb4d6bef0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 151
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Er8MnBJpjvoa",
        "colab_type": "text"
      },
      "source": [
        "### Task 5.3: Plotting Healthy life expectancy vs maximum Infection rate"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ql2T97rTjvob",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        },
        "outputId": "d2becf5e-947f-4f18-8568-8670907ebd36"
      },
      "source": [
        "x=data[\"Healthy life expectancy\"]\n",
        "y=data[\"max_infection_rates\"]\n",
        "sns.scatterplot(x,np.log(y+1))"
      ],
      "execution_count": 152,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f5cb4d5d828>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 152
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1hKfRxTSjvod",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        },
        "outputId": "482e3da7-1d31-4396-cef5-c09d4e90c73d"
      },
      "source": [
        "sns.regplot(x,np.log(y+1))"
      ],
      "execution_count": 153,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f5cb4d5d5c0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 153
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H1p9R1Oxjvof",
        "colab_type": "text"
      },
      "source": [
        "### Task 5.4: Plotting Freedom to make life choices vs maximum Infection rate"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mbgxtWupjvog",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        },
        "outputId": "5d4a4d2d-7fc0-4d81-bf28-b80dc4611329"
      },
      "source": [
        "x=data[\"Freedom to make life choices\"]\n",
        "y=data[\"max_infection_rates\"]\n",
        "sns.scatterplot(x,np.log(y+1))"
      ],
      "execution_count": 154,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f5cb4d54ef0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 154
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "67_H9y_9jvoi",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        },
        "outputId": "9d84024b-0181-441a-e4a9-2af5dfca91f5"
      },
      "source": [
        "sns.regplot(x,np.log(y+1))"
      ],
      "execution_count": 155,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f5cb4c09a90>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 155
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Dq83hlUpoP6F",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 155,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wgo4SiDDoQxx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 155,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2ylj8F2NoQr_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 155,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OG1cTuvMsZmV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 155,
      "outputs": []
    }
  ]
}