Positive_Cases_of_covid19_data_analysis_notebook.ipynb

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    "colab": {
      "name": "Positive Cases 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": "c6e380e6-54b4-49b1-ff14-c06f37fe758a"
      },
      "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": 56,
      "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": "b4afe05a-8543-4174-9f2e-6ad16c57c938"
      },
      "source": [
        "corona_dataset_csv= pd.read_csv(\"sample_data/time_series_covid19_confirmed_global.csv\")\n",
        "corona_dataset_csv.head(10)"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
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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>0</td>\n",
              "      <td>...</td>\n",
              "      <td>60</td>\n",
              "      <td>61</td>\n",
              "      <td>69</td>\n",
              "      <td>70</td>\n",
              "      <td>70</td>\n",
              "      <td>71</td>\n",
              "      <td>74</td>\n",
              "      <td>81</td>\n",
              "      <td>84</td>\n",
              "      <td>86</td>\n",
              "      <td>86</td>\n",
              "      <td>86</td>\n",
              "      <td>86</td>\n",
              "      <td>86</td>\n",
              "      <td>86</td>\n",
              "      <td>88</td>\n",
              "      <td>91</td>\n",
              "      <td>92</td>\n",
              "      <td>96</td>\n",
              "      <td>113</td>\n",
              "      <td>118</td>\n",
              "      <td>130</td>\n",
              "      <td>138</td>\n",
              "      <td>140</td>\n",
              "      <td>142</td>\n",
              "      <td>148</td>\n",
              "      <td>155</td>\n",
              "      <td>166</td>\n",
              "      <td>172</td>\n",
              "      <td>176</td>\n",
              "      <td>183</td>\n",
              "      <td>186</td>\n",
              "      <td>189</td>\n",
              "      <td>197</td>\n",
              "      <td>212</td>\n",
              "      <td>212</td>\n",
              "      <td>259</td>\n",
              "      <td>267</td>\n",
              "      <td>276</td>\n",
              "      <td>284</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>25</td>\n",
              "      <td>25</td>\n",
              "      <td>25</td>\n",
              "      <td>25</td>\n",
              "      <td>25</td>\n",
              "      <td>25</td>\n",
              "      <td>25</td>\n",
              "      <td>25</td>\n",
              "      <td>25</td>\n",
              "      <td>26</td>\n",
              "      <td>26</td>\n",
              "      <td>26</td>\n",
              "      <td>26</td>\n",
              "      <td>26</td>\n",
              "      <td>26</td>\n",
              "      <td>26</td>\n",
              "      <td>26</td>\n",
              "      <td>26</td>\n",
              "      <td>26</td>\n",
              "      <td>26</td>\n",
              "      <td>26</td>\n",
              "      <td>26</td>\n",
              "      <td>26</td>\n",
              "      <td>26</td>\n",
              "      <td>26</td>\n",
              "      <td>26</td>\n",
              "      <td>26</td>\n",
              "      <td>26</td>\n",
              "      <td>26</td>\n",
              "      <td>26</td>\n",
              "      <td>26</td>\n",
              "      <td>26</td>\n",
              "      <td>26</td>\n",
              "      <td>26</td>\n",
              "      <td>65</td>\n",
              "      <td>65</td>\n",
              "      <td>65</td>\n",
              "      <td>69</td>\n",
              "      <td>69</td>\n",
              "      <td>69</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>10649</td>\n",
              "      <td>11353</td>\n",
              "      <td>12076</td>\n",
              "      <td>12628</td>\n",
              "      <td>13228</td>\n",
              "      <td>13933</td>\n",
              "      <td>14702</td>\n",
              "      <td>15419</td>\n",
              "      <td>16214</td>\n",
              "      <td>16851</td>\n",
              "      <td>17415</td>\n",
              "      <td>18319</td>\n",
              "      <td>19268</td>\n",
              "      <td>20197</td>\n",
              "      <td>21037</td>\n",
              "      <td>22020</td>\n",
              "      <td>22794</td>\n",
              "      <td>23620</td>\n",
              "      <td>24761</td>\n",
              "      <td>25987</td>\n",
              "      <td>27373</td>\n",
              "      <td>28764</td>\n",
              "      <td>30295</td>\n",
              "      <td>31577</td>\n",
              "      <td>32785</td>\n",
              "      <td>34159</td>\n",
              "      <td>35552</td>\n",
              "      <td>37510</td>\n",
              "      <td>39570</td>\n",
              "      <td>41204</td>\n",
              "      <td>42785</td>\n",
              "      <td>44931</td>\n",
              "      <td>47203</td>\n",
              "      <td>49851</td>\n",
              "      <td>52457</td>\n",
              "      <td>55343</td>\n",
              "      <td>57744</td>\n",
              "      <td>59933</td>\n",
              "      <td>62268</td>\n",
              "      <td>64530</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>5928</td>\n",
              "      <td>6302</td>\n",
              "      <td>6661</td>\n",
              "      <td>7113</td>\n",
              "      <td>7402</td>\n",
              "      <td>7774</td>\n",
              "      <td>8216</td>\n",
              "      <td>8676</td>\n",
              "      <td>8927</td>\n",
              "      <td>9282</td>\n",
              "      <td>9492</td>\n",
              "      <td>10009</td>\n",
              "      <td>10524</td>\n",
              "      <td>11221</td>\n",
              "      <td>11817</td>\n",
              "      <td>12364</td>\n",
              "      <td>13130</td>\n",
              "      <td>13325</td>\n",
              "      <td>13675</td>\n",
              "      <td>14103</td>\n",
              "      <td>14669</td>\n",
              "      <td>15281</td>\n",
              "      <td>16004</td>\n",
              "      <td>16667</td>\n",
              "      <td>17064</td>\n",
              "      <td>17489</td>\n",
              "      <td>18033</td>\n",
              "      <td>18698</td>\n",
              "      <td>19157</td>\n",
              "      <td>19708</td>\n",
              "      <td>20268</td>\n",
              "      <td>20588</td>\n",
              "      <td>21006</td>\n",
              "      <td>21717</td>\n",
              "      <td>22488</td>\n",
              "      <td>23247</td>\n",
              "      <td>23909</td>\n",
              "      <td>24645</td>\n",
              "      <td>25127</td>\n",
              "      <td>25542</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>107</td>\n",
              "      <td>107</td>\n",
              "      <td>107</td>\n",
              "      <td>107</td>\n",
              "      <td>107</td>\n",
              "      <td>107</td>\n",
              "      <td>107</td>\n",
              "      <td>107</td>\n",
              "      <td>107</td>\n",
              "      <td>107</td>\n",
              "      <td>107</td>\n",
              "      <td>107</td>\n",
              "      <td>107</td>\n",
              "      <td>107</td>\n",
              "      <td>107</td>\n",
              "      <td>108</td>\n",
              "      <td>108</td>\n",
              "      <td>108</td>\n",
              "      <td>108</td>\n",
              "      <td>108</td>\n",
              "      <td>108</td>\n",
              "      <td>108</td>\n",
              "      <td>108</td>\n",
              "      <td>108</td>\n",
              "      <td>108</td>\n",
              "      <td>108</td>\n",
              "      <td>108</td>\n",
              "      <td>108</td>\n",
              "      <td>108</td>\n",
              "      <td>108</td>\n",
              "      <td>108</td>\n",
              "      <td>108</td>\n",
              "      <td>108</td>\n",
              "      <td>108</td>\n",
              "      <td>108</td>\n",
              "      <td>108</td>\n",
              "      <td>108</td>\n",
              "      <td>108</td>\n",
              "      <td>108</td>\n",
              "      <td>108</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>3</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>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>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>...</td>\n",
              "      <td>3086</td>\n",
              "      <td>3087</td>\n",
              "      <td>3090</td>\n",
              "      <td>3092</td>\n",
              "      <td>3089</td>\n",
              "      <td>3090</td>\n",
              "      <td>3092</td>\n",
              "      <td>3092</td>\n",
              "      <td>3095</td>\n",
              "      <td>3098</td>\n",
              "      <td>3104</td>\n",
              "      <td>3104</td>\n",
              "      <td>3106</td>\n",
              "      <td>3110</td>\n",
              "      <td>3110</td>\n",
              "      <td>3109</td>\n",
              "      <td>3112</td>\n",
              "      <td>3114</td>\n",
              "      <td>3117</td>\n",
              "      <td>3117</td>\n",
              "      <td>3115</td>\n",
              "      <td>3119</td>\n",
              "      <td>3128</td>\n",
              "      <td>3131</td>\n",
              "      <td>3134</td>\n",
              "      <td>3135</td>\n",
              "      <td>3137</td>\n",
              "      <td>3143</td>\n",
              "      <td>3144</td>\n",
              "      <td>3149</td>\n",
              "      <td>3151</td>\n",
              "      <td>3150</td>\n",
              "      <td>3159</td>\n",
              "      <td>3162</td>\n",
              "      <td>3168</td>\n",
              "      <td>3174</td>\n",
              "      <td>3177</td>\n",
              "      <td>3184</td>\n",
              "      <td>3189</td>\n",
              "      <td>3203</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  ...    31238    31517\n",
              "1                           NaN              Albania  ...     2466     2535\n",
              "2                           NaN              Algeria  ...    13571    13907\n",
              "3                           NaN              Andorra  ...      855      855\n",
              "4                           NaN               Angola  ...      276      284\n",
              "5                           NaN  Antigua and Barbuda  ...       69       69\n",
              "6                           NaN            Argentina  ...    62268    64530\n",
              "7                           NaN              Armenia  ...    25127    25542\n",
              "8  Australian Capital Territory            Australia  ...      108      108\n",
              "9               New South Wales            Australia  ...     3189     3203\n",
              "\n",
              "[10 rows x 165 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 57
        }
      ]
    },
    {
      "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": "b1c64d08-e89e-45c6-e52b-562f95f5b3ec"
      },
      "source": [
        "corona_dataset_csv.shape"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(266, 165)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 58
        }
      ]
    },
    {
      "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": 59,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SrdYJWKWjvmc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 251
        },
        "outputId": "c3066516-e3f0-407d-c7c4-7912be409d9c"
      },
      "source": [
        "\n",
        "corona_dataset_csv.head()"
      ],
      "execution_count": 60,
      "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>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>9216</td>\n",
              "      <td>9998</td>\n",
              "      <td>10582</td>\n",
              "      <td>11173</td>\n",
              "      <td>11831</td>\n",
              "      <td>12456</td>\n",
              "      <td>13036</td>\n",
              "      <td>13659</td>\n",
              "      <td>14525</td>\n",
              "      <td>15205</td>\n",
              "      <td>15750</td>\n",
              "      <td>16509</td>\n",
              "      <td>17267</td>\n",
              "      <td>18054</td>\n",
              "      <td>18969</td>\n",
              "      <td>19551</td>\n",
              "      <td>20342</td>\n",
              "      <td>20917</td>\n",
              "      <td>21459</td>\n",
              "      <td>22142</td>\n",
              "      <td>22890</td>\n",
              "      <td>23546</td>\n",
              "      <td>24102</td>\n",
              "      <td>24766</td>\n",
              "      <td>25527</td>\n",
              "      <td>26310</td>\n",
              "      <td>26874</td>\n",
              "      <td>27532</td>\n",
              "      <td>27878</td>\n",
              "      <td>28424</td>\n",
              "      <td>28833</td>\n",
              "      <td>29157</td>\n",
              "      <td>29481</td>\n",
              "      <td>29640</td>\n",
              "      <td>30175</td>\n",
              "      <td>30451</td>\n",
              "      <td>30616</td>\n",
              "      <td>30967</td>\n",
              "      <td>31238</td>\n",
              "      <td>31517</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>981</td>\n",
              "      <td>989</td>\n",
              "      <td>998</td>\n",
              "      <td>1004</td>\n",
              "      <td>1029</td>\n",
              "      <td>1050</td>\n",
              "      <td>1076</td>\n",
              "      <td>1099</td>\n",
              "      <td>1122</td>\n",
              "      <td>1137</td>\n",
              "      <td>1143</td>\n",
              "      <td>1164</td>\n",
              "      <td>1184</td>\n",
              "      <td>1197</td>\n",
              "      <td>1212</td>\n",
              "      <td>1232</td>\n",
              "      <td>1246</td>\n",
              "      <td>1263</td>\n",
              "      <td>1299</td>\n",
              "      <td>1341</td>\n",
              "      <td>1385</td>\n",
              "      <td>1416</td>\n",
              "      <td>1464</td>\n",
              "      <td>1521</td>\n",
              "      <td>1590</td>\n",
              "      <td>1672</td>\n",
              "      <td>1722</td>\n",
              "      <td>1788</td>\n",
              "      <td>1838</td>\n",
              "      <td>1891</td>\n",
              "      <td>1962</td>\n",
              "      <td>1995</td>\n",
              "      <td>2047</td>\n",
              "      <td>2114</td>\n",
              "      <td>2192</td>\n",
              "      <td>2269</td>\n",
              "      <td>2330</td>\n",
              "      <td>2402</td>\n",
              "      <td>2466</td>\n",
              "      <td>2535</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>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>7918</td>\n",
              "      <td>8113</td>\n",
              "      <td>8306</td>\n",
              "      <td>8503</td>\n",
              "      <td>8697</td>\n",
              "      <td>8857</td>\n",
              "      <td>8997</td>\n",
              "      <td>9134</td>\n",
              "      <td>9267</td>\n",
              "      <td>9394</td>\n",
              "      <td>9513</td>\n",
              "      <td>9626</td>\n",
              "      <td>9733</td>\n",
              "      <td>9831</td>\n",
              "      <td>9935</td>\n",
              "      <td>10050</td>\n",
              "      <td>10154</td>\n",
              "      <td>10265</td>\n",
              "      <td>10382</td>\n",
              "      <td>10484</td>\n",
              "      <td>10589</td>\n",
              "      <td>10698</td>\n",
              "      <td>10810</td>\n",
              "      <td>10919</td>\n",
              "      <td>11031</td>\n",
              "      <td>11147</td>\n",
              "      <td>11268</td>\n",
              "      <td>11385</td>\n",
              "      <td>11504</td>\n",
              "      <td>11631</td>\n",
              "      <td>11771</td>\n",
              "      <td>11920</td>\n",
              "      <td>12076</td>\n",
              "      <td>12248</td>\n",
              "      <td>12445</td>\n",
              "      <td>12685</td>\n",
              "      <td>12968</td>\n",
              "      <td>13273</td>\n",
              "      <td>13571</td>\n",
              "      <td>13907</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>762</td>\n",
              "      <td>762</td>\n",
              "      <td>762</td>\n",
              "      <td>763</td>\n",
              "      <td>763</td>\n",
              "      <td>763</td>\n",
              "      <td>763</td>\n",
              "      <td>764</td>\n",
              "      <td>764</td>\n",
              "      <td>764</td>\n",
              "      <td>765</td>\n",
              "      <td>844</td>\n",
              "      <td>851</td>\n",
              "      <td>852</td>\n",
              "      <td>852</td>\n",
              "      <td>852</td>\n",
              "      <td>852</td>\n",
              "      <td>852</td>\n",
              "      <td>852</td>\n",
              "      <td>852</td>\n",
              "      <td>852</td>\n",
              "      <td>853</td>\n",
              "      <td>853</td>\n",
              "      <td>853</td>\n",
              "      <td>853</td>\n",
              "      <td>854</td>\n",
              "      <td>854</td>\n",
              "      <td>855</td>\n",
              "      <td>855</td>\n",
              "      <td>855</td>\n",
              "      <td>855</td>\n",
              "      <td>855</td>\n",
              "      <td>855</td>\n",
              "      <td>855</td>\n",
              "      <td>855</td>\n",
              "      <td>855</td>\n",
              "      <td>855</td>\n",
              "      <td>855</td>\n",
              "      <td>855</td>\n",
              "      <td>855</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>60</td>\n",
              "      <td>61</td>\n",
              "      <td>69</td>\n",
              "      <td>70</td>\n",
              "      <td>70</td>\n",
              "      <td>71</td>\n",
              "      <td>74</td>\n",
              "      <td>81</td>\n",
              "      <td>84</td>\n",
              "      <td>86</td>\n",
              "      <td>86</td>\n",
              "      <td>86</td>\n",
              "      <td>86</td>\n",
              "      <td>86</td>\n",
              "      <td>86</td>\n",
              "      <td>88</td>\n",
              "      <td>91</td>\n",
              "      <td>92</td>\n",
              "      <td>96</td>\n",
              "      <td>113</td>\n",
              "      <td>118</td>\n",
              "      <td>130</td>\n",
              "      <td>138</td>\n",
              "      <td>140</td>\n",
              "      <td>142</td>\n",
              "      <td>148</td>\n",
              "      <td>155</td>\n",
              "      <td>166</td>\n",
              "      <td>172</td>\n",
              "      <td>176</td>\n",
              "      <td>183</td>\n",
              "      <td>186</td>\n",
              "      <td>189</td>\n",
              "      <td>197</td>\n",
              "      <td>212</td>\n",
              "      <td>212</td>\n",
              "      <td>259</td>\n",
              "      <td>267</td>\n",
              "      <td>276</td>\n",
              "      <td>284</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  ...    30967    31238    31517\n",
              "1            NaN        Albania        0  ...     2402     2466     2535\n",
              "2            NaN        Algeria        0  ...    13273    13571    13907\n",
              "3            NaN        Andorra        0  ...      855      855      855\n",
              "4            NaN         Angola        0  ...      267      276      284\n",
              "\n",
              "[5 rows x 163 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vFtgrHxpl3GY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "countires= list(corona_dataset_csv['Country/Region'])"
      ],
      "execution_count": 61,
      "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": 62,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f3qZakqwjvmj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "26edac74-664c-4cb9-f32e-b731629382f3"
      },
      "source": [
        "corona_dataset_aggregated.head()"
      ],
      "execution_count": 63,
      "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",
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              "\n",
              "    .dataframe thead th {\n",
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              "<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>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>9216</td>\n",
              "      <td>9998</td>\n",
              "      <td>10582</td>\n",
              "      <td>11173</td>\n",
              "      <td>11831</td>\n",
              "      <td>12456</td>\n",
              "      <td>13036</td>\n",
              "      <td>13659</td>\n",
              "      <td>14525</td>\n",
              "      <td>15205</td>\n",
              "      <td>15750</td>\n",
              "      <td>16509</td>\n",
              "      <td>17267</td>\n",
              "      <td>18054</td>\n",
              "      <td>18969</td>\n",
              "      <td>19551</td>\n",
              "      <td>20342</td>\n",
              "      <td>20917</td>\n",
              "      <td>21459</td>\n",
              "      <td>22142</td>\n",
              "      <td>22890</td>\n",
              "      <td>23546</td>\n",
              "      <td>24102</td>\n",
              "      <td>24766</td>\n",
              "      <td>25527</td>\n",
              "      <td>26310</td>\n",
              "      <td>26874</td>\n",
              "      <td>27532</td>\n",
              "      <td>27878</td>\n",
              "      <td>28424</td>\n",
              "      <td>28833</td>\n",
              "      <td>29157</td>\n",
              "      <td>29481</td>\n",
              "      <td>29640</td>\n",
              "      <td>30175</td>\n",
              "      <td>30451</td>\n",
              "      <td>30616</td>\n",
              "      <td>30967</td>\n",
              "      <td>31238</td>\n",
              "      <td>31517</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>981</td>\n",
              "      <td>989</td>\n",
              "      <td>998</td>\n",
              "      <td>1004</td>\n",
              "      <td>1029</td>\n",
              "      <td>1050</td>\n",
              "      <td>1076</td>\n",
              "      <td>1099</td>\n",
              "      <td>1122</td>\n",
              "      <td>1137</td>\n",
              "      <td>1143</td>\n",
              "      <td>1164</td>\n",
              "      <td>1184</td>\n",
              "      <td>1197</td>\n",
              "      <td>1212</td>\n",
              "      <td>1232</td>\n",
              "      <td>1246</td>\n",
              "      <td>1263</td>\n",
              "      <td>1299</td>\n",
              "      <td>1341</td>\n",
              "      <td>1385</td>\n",
              "      <td>1416</td>\n",
              "      <td>1464</td>\n",
              "      <td>1521</td>\n",
              "      <td>1590</td>\n",
              "      <td>1672</td>\n",
              "      <td>1722</td>\n",
              "      <td>1788</td>\n",
              "      <td>1838</td>\n",
              "      <td>1891</td>\n",
              "      <td>1962</td>\n",
              "      <td>1995</td>\n",
              "      <td>2047</td>\n",
              "      <td>2114</td>\n",
              "      <td>2192</td>\n",
              "      <td>2269</td>\n",
              "      <td>2330</td>\n",
              "      <td>2402</td>\n",
              "      <td>2466</td>\n",
              "      <td>2535</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>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>7918</td>\n",
              "      <td>8113</td>\n",
              "      <td>8306</td>\n",
              "      <td>8503</td>\n",
              "      <td>8697</td>\n",
              "      <td>8857</td>\n",
              "      <td>8997</td>\n",
              "      <td>9134</td>\n",
              "      <td>9267</td>\n",
              "      <td>9394</td>\n",
              "      <td>9513</td>\n",
              "      <td>9626</td>\n",
              "      <td>9733</td>\n",
              "      <td>9831</td>\n",
              "      <td>9935</td>\n",
              "      <td>10050</td>\n",
              "      <td>10154</td>\n",
              "      <td>10265</td>\n",
              "      <td>10382</td>\n",
              "      <td>10484</td>\n",
              "      <td>10589</td>\n",
              "      <td>10698</td>\n",
              "      <td>10810</td>\n",
              "      <td>10919</td>\n",
              "      <td>11031</td>\n",
              "      <td>11147</td>\n",
              "      <td>11268</td>\n",
              "      <td>11385</td>\n",
              "      <td>11504</td>\n",
              "      <td>11631</td>\n",
              "      <td>11771</td>\n",
              "      <td>11920</td>\n",
              "      <td>12076</td>\n",
              "      <td>12248</td>\n",
              "      <td>12445</td>\n",
              "      <td>12685</td>\n",
              "      <td>12968</td>\n",
              "      <td>13273</td>\n",
              "      <td>13571</td>\n",
              "      <td>13907</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>762</td>\n",
              "      <td>762</td>\n",
              "      <td>762</td>\n",
              "      <td>763</td>\n",
              "      <td>763</td>\n",
              "      <td>763</td>\n",
              "      <td>763</td>\n",
              "      <td>764</td>\n",
              "      <td>764</td>\n",
              "      <td>764</td>\n",
              "      <td>765</td>\n",
              "      <td>844</td>\n",
              "      <td>851</td>\n",
              "      <td>852</td>\n",
              "      <td>852</td>\n",
              "      <td>852</td>\n",
              "      <td>852</td>\n",
              "      <td>852</td>\n",
              "      <td>852</td>\n",
              "      <td>852</td>\n",
              "      <td>852</td>\n",
              "      <td>853</td>\n",
              "      <td>853</td>\n",
              "      <td>853</td>\n",
              "      <td>853</td>\n",
              "      <td>854</td>\n",
              "      <td>854</td>\n",
              "      <td>855</td>\n",
              "      <td>855</td>\n",
              "      <td>855</td>\n",
              "      <td>855</td>\n",
              "      <td>855</td>\n",
              "      <td>855</td>\n",
              "      <td>855</td>\n",
              "      <td>855</td>\n",
              "      <td>855</td>\n",
              "      <td>855</td>\n",
              "      <td>855</td>\n",
              "      <td>855</td>\n",
              "      <td>855</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>60</td>\n",
              "      <td>61</td>\n",
              "      <td>69</td>\n",
              "      <td>70</td>\n",
              "      <td>70</td>\n",
              "      <td>71</td>\n",
              "      <td>74</td>\n",
              "      <td>81</td>\n",
              "      <td>84</td>\n",
              "      <td>86</td>\n",
              "      <td>86</td>\n",
              "      <td>86</td>\n",
              "      <td>86</td>\n",
              "      <td>86</td>\n",
              "      <td>86</td>\n",
              "      <td>88</td>\n",
              "      <td>91</td>\n",
              "      <td>92</td>\n",
              "      <td>96</td>\n",
              "      <td>113</td>\n",
              "      <td>118</td>\n",
              "      <td>130</td>\n",
              "      <td>138</td>\n",
              "      <td>140</td>\n",
              "      <td>142</td>\n",
              "      <td>148</td>\n",
              "      <td>155</td>\n",
              "      <td>166</td>\n",
              "      <td>172</td>\n",
              "      <td>176</td>\n",
              "      <td>183</td>\n",
              "      <td>186</td>\n",
              "      <td>189</td>\n",
              "      <td>197</td>\n",
              "      <td>212</td>\n",
              "      <td>212</td>\n",
              "      <td>259</td>\n",
              "      <td>267</td>\n",
              "      <td>276</td>\n",
              "      <td>284</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  ...    30967    31238    31517\n",
              "Albania               0        0        0  ...     2402     2466     2535\n",
              "Algeria               0        0        0  ...    13273    13571    13907\n",
              "Andorra               0        0        0  ...      855      855      855\n",
              "Angola                0        0        0  ...      267      276      284\n",
              "\n",
              "[5 rows x 161 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kyS-FnT1l1Yt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "corona_dataset_aggregated.to_csv(\"Aggregated_Positive.csv\")"
      ],
      "execution_count": 64,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TkVkKdkljvmm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "9a572acb-a5c7-4c91-f21b-755df3589661"
      },
      "source": [
        "corona_dataset_aggregated.shape"
      ],
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(188, 161)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 65
        }
      ]
    },
    {
      "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": "76aa897e-eee0-413f-c151-4b15debbf484"
      },
      "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": 66,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Number of Cases')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 66
        },
        {
          "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": 307
        },
        "outputId": "d2fafe22-ecd0-4b8f-ba46-415e36700493"
      },
      "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": 67,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Number of Cases')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 67
        },
        {
          "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": "QldLIptIrJcD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "countries=list(corona_dataset_aggregated.index)"
      ],
      "execution_count": 68,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l1rRf6j4riRf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "e5e341b5-c038-492e-eac5-a2f196b2997e"
      },
      "source": [
        "countries"
      ],
      "execution_count": 69,
      "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": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ppzM4PDJrqlR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 334
        },
        "outputId": "0a327352-56aa-4a98-dbf3-0a4445f0157c"
      },
      "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": 70,
      "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": 70
        },
        {
          "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": 469
        },
        "outputId": "47d57ffd-fde8-4a15-ee0f-7f48852e57e5"
      },
      "source": [
        "for c in countries:\n",
        "  corona_dataset_aggregated.loc[c].plot(figsize=(20,10))\n",
        "  plt.xticks(rotation=25)\n",
        "  plt.xlabel(\"Date\")\n",
        "  plt.ylabel(\"Number of positive Cases\")\n",
        "  "
      ],
      "execution_count": 109,
      "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": 71,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wWxbBXo9rqUx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 71,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "52pUSpc5rqRo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 71,
      "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": "74fc4f2d-c6d1-48d0-ac1e-663e2a1dca0c"
      },
      "source": [
        "corona_dataset_aggregated.loc['India'].plot()\n",
        "plt.legend()"
      ],
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f84771ee048>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 72
        },
        {
          "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": "bgnQCppwjvmw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "outputId": "1ef5764c-86d7-4efe-a037-533a397e1c02"
      },
      "source": [
        "corona_dataset_aggregated.loc[\"India\"][:75].plot()\n",
        "\n",
        "corona_dataset_aggregated.loc[\"China\"][:75].plot()\n",
        "\n",
        "plt.legend()"
      ],
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f847726b0f0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 73
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Z6O+c2xTU+QToH4wPAjbEzF8elB2svLyZ8gOY2RVmVmZmZVu2bIkjdAnNhy/712EnhBuHiMQlnmSQBxwL3OGcGwfsZl+XEADBL3qX+PD255yb65wrcc6V9O3bN9kfJ+2x40N/fUEXbSeRdBBPMigHyp1zrwXvF+CTw+agi4fg9dNg+kZgcMz8xUHZwcqLmymXdFaxEboXt15PRFJCq8nAOfcJsMHMPh8UnQSsBEqBxjOCZgOPBeOlwEXBWUUTgYqgO+lp4FQz6xkcOD4VeDqYttPMJgZnEV0UsyxJVzvLoVuzvX0ikoLifezlVcADZpYPrAUuwSeSh83sMuBD4Pyg7pPAGcBqoCqoi3Num5n9F7AkqPcz59y2YPw7wJ+ATsA/gkHSWcVGKP5C2FGISJziSgbOubeAkmYmndRMXQdc2cJy7gbubqa8DDgqnlgkDdRWwZ5t2jMQSSO6AlkSb+fH/lXHDETShpKBJF5FcAax9gxE0oaSgSTezuBksO5KBiLpQslAEq8iSAbaMxBJG0oGkng7y6FzX8grCDsSEYmTkoEkni44E0k7SgaSeDs3qotIJM0oGUjiac9AJO0oGUhiVVdA7S7tGYikGSUDSawKnVYqko6UDCSxGq8x6KZuIpF0omQgiVURPKdIewYiaUXJQBKrohwsB7ocFnYkItIGSgaSWDs3QtcBkBvv3dFFJBUoGUhiVeihNiLpSMlAEmvnRh0vEElDSgaSOM75Zxloz0Ak7SgZSOJUbYX6aug+OOxIRKSNlAwkcXRaqUjaUjKQxNmp5xiIpCslA0mcvbei0NXHIulGyUASZ2c55OZDUZ+wIxGRNlIykMSp2AjdBkKO/qxE0o3+ayVxdm7UDepE0pSSgSRORbnOJBJJU0oGkhjRBl1wJpLGlAwkMSo3g2vQnoFImlIykMSo0ENtRNKZkoEkxs7Gq4+VDETSkZKBJIaefSyS1vQEEjk00Sh89h6UL4HyMlj9LEQ6Q2GPsCMTkUOgZCBtV18Lfz4f1i7y7wt7QHEJjDkXzMKNTUQOiZKBtI1z8Pcf+ERw0g1w5NnQe4SSgEiaUzKQtnnlNlh6P3zpxzDl6rCjEZEE0QFkid+qJ2DhDb47aOq1YUcjIgmkZCDx+fgteORyGHQsnHOHbkYnkmH0Hy2tq94JD30DOvWCWQ9CpFPYEYlIgumYgbTu6f/wF5Vd+jR07R92NCKSBNozkIN7/2lYeh9M+j4MnhB2NCKSJHEnAzPLNbOlZvZE8H6Ymb1mZqvN7CEzyw/KC4L3q4PpQ2OWcW1Q/p6ZnRZTPj0oW21m1yTu60m7VG2D0qug3xgdMBbJcG3ZM/g+sCrm/S+B3zjnDge2A5cF5ZcB24Py3wT1MLPRwCxgDDAd+F2QYHKB3wKnA6OBrwV1JWxP/htUbYVz74S8grCjEZEkiisZmFkx8GVgXvDegGnAgqDKPcA5wfiM4D3B9JOC+jOA+c65GufcOmA1MCEYVjvn1jrnaoH5QV0J04pH4Z0FcMI1MODosKMRkSSLd8/gFuDHQDR43xvY4ZyrD96XA413KBsEbAAIplcE9feWN5mnpfIDmNkVZlZmZmVbtmyJM3Rpsz074O8/goHHwuQfhB2NiHSAVpOBmZ0JfOqce6MD4jko59xc51yJc66kb9++YYeTuV78le8eOusWyNUJZyLZIJ7/9EnA2WZ2BlAIdANuBXqYWV7w678YCO5hzEZgMFBuZnlAd2BrTHmj2HlaKpeOtnUNvPZ7OPYbMGBs2NGISAdpdc/AOXetc67YOTcUfwD4eefcBcAi4Lyg2mzgsWC8NHhPMP1555wLymcFZxsNA0YCrwNLgJHB2Un5wWeUJuTbSds9cz3kFcK0/ww7EhHpQO3pA/h3YL6Z3QQsBe4Kyu8C7jOz1cA2fOOOc26FmT0MrATqgSudcw0AZvZd4GkgF7jbObeiHXHJoVqzCN57Ek6+Ebr0CzsaEelA5n+0p5+SkhJXVlYWdhiZo6Eefj8FanfDla9DpDDsiEQkwczsDedcSXPTdHRQvKX3wqcr4fx7lQhEspBuRyH+SuPnfw6fm+QfViMiWUfJQGDhf8Ke7XD6L/XEMpEspWSQ7da96J9cdvxVcNi/hB2NiIREySCb1e2Bx+dAz2EwVfcHFMlmOoCczV78FWxbA9/4mx5YI5LltGeQrTavgJdvhbFfhxEnhh2NiIRMySAbRRug9HtQ2B1OvSnsaEQkBaibKBut/BtsLINz7oTOvcOORkRSgPYMsk00Ci/+GvocAUefH3Y0IpIilAyyzXt/91caT/kR5OSGHY2IpAglg2ziHPzvzf5U0qO+EnY0IpJClAyyyQfPwCfLYcoP9dAaEdmPkkG2aNwr6D4Exs4KOxoRSTFKBtli7SJ/BtHkOZAbCTsaEUkxSgbZ4n9/BV0HwrgLw45ERFKQkkE2WPcifPQKTPo+5BWEHY2IpCAlg0wXjcLCn0C3Yhg/u/X6IpKVdEpJplvxCHy8FM65QzejE5EWac8gk9XXwHM/g/5HwdEzw45GRFKY9gwyWdndsONDuPCvutpYRA5KewaZas8Of13B8Kkw4qSwoxGRFKdkkKlevgX2bINTfqbnGotIq5QMMlHFRlh8hz9OMGBs2NGISBpQMshEL98K0Xo48bqwIxGRNKFkkGmqtsHS++Bfzoeenws7GhFJE0oGmWbJPKirguOvCjsSEUkjSgaZpG4PvHYnjDwN+o8OOxoRSSNKBpnkrQegaqu/B5GISBsoGWSKaAO8cjsMGg+fOz7saEQkzSgZZIpVj8P2dX6vQNcViEgbKRlkAuf86aS9hsOoM8OORkTSkJJBJlj/T/j4TX8Gke5BJCKHQMkgEyz+HRT1hrFfCzsSEUlTSgbpbts6eO8fUHKpnlcgIodMySDdvf4H3zVUcmnYkYhIGlMySGc1lf7WE6NnQLeBYUcjImlMySCdLXsQanbCcd8OOxIRSXNKBukqGoXX58LAY6G4JOxoRCTNtZoMzGywmS0ys5VmtsLMvh+U9zKzhWb2QfDaMyg3M7vNzFab2XIzOzZmWbOD+h+Y2eyY8vFm9nYwz21mumqqVWufh8/eh+O+pYvMRKTd4tkzqAd+6JwbDUwErjSz0cA1wHPOuZHAc8F7gNOBkcFwBXAH+OQB3AAcB0wAbmhMIEGdy2Pmm97+r5bhXvs9dO4HY84JOxIRyQCtJgPn3Cbn3JvB+C5gFTAImAHcE1S7B2hslWYA9zpvMdDDzAYApwELnXPbnHPbgYXA9GBaN+fcYuecA+6NWZY0Z+sa+OAZ+MJlkFcQdjQikgHadMzAzIYC44DXgP7OuU3BpE+A/sH4IGBDzGzlQdnBysubKW/u868wszIzK9uyZUtbQs8sr94OOREYf0nYkYhIhog7GZhZF+CvwBzn3M7YacEvepfg2A7gnJvrnCtxzpX07ds32R+Xmra8D2/cA+NnQ9f+rdcXEYlDXMnAzCL4RPCAc+6RoHhz0MVD8PppUL4RGBwze3FQdrDy4mbKpTkLfwKRIph6bdiRiEgGiedsIgPuAlY55/4nZlIp0HhG0GzgsZjyi4KziiYCFUF30tPAqWbWMzhwfCrwdDBtp5lNDD7rophlSax1L8L7/4ApV0PnPmFHIyIZJC+OOpOAbwBvm9lbQdl/AL8AHjazy4APgfODaU8CZwCrgSrgEgDn3DYz+y9gSVDvZ865bcH4d4A/AZ2AfwSDxIpG4ZnroftgmKiLzEQksVpNBs65fwItnch+UjP1HXBlC8u6G7i7mfIy4KjWYslqbz8Mm5bBv/5BN6QTkYTTFcjpoLYKnvsZDBwHR50XdjQikoHi6SaSsC3+Hezc6PcKcpS/RSTx1LKkumgDLJkHI06CoZPCjkZEMpSSQapb9yLs2gTjLgw7EhHJYEoGqW75Q1DQDT5/etiRiEgGUzJIZbW7YWWpf3iNziASkSRSMkhl7/4d6nbD2FlhRyIiGU7JIJUtf8hfZDbk+LAjEZEMp2SQqnZthjXPw9Hn63RSEUk6tTKp6p0F4KJw9MywIxGRLKBkkKqWzYcBx0Dfz4cdiYhkASWDVPTpKvhkuQ4ci0iHUTJIRcvmg+XqPkQi0mGUDFLNZx9A2R9h5CnQJUuf5iYiHU7JIJXs2QEPzoLcPDj95rCjEZEsoruWpoqGelhwKWxfDxeVQs/PhR2RiGQRJYNUsfAnsOY5OOtW3Z1URDqcuolSwdL7YfFvYcIVMP7isKMRkSykZBC2dx6Bx78Pw06A0/5f2NGISJZSMgjTWw/CXy+D4i/AzPv9gWMRkRAoGYSl7I/wt2/D0Clw4V+hsFvYEYlIFlMyCMNrv4cn5vhrCb7+EOR3DjsiEclySgYd7e0F8I8fw6gzfdeQHlojIilAyaAjlZfB377jn09w3t2QVxB2RCIigJJBx6koh/lfh66Hwcz7lAhEJKXo9JWOULvb32aitgouegw69wk7IhGR/SgZJFvlp/D4HNi8Ar7+MPQ7MuyIREQOoGSQDJVbYFUprHgUPnzZP7Fs+i/82UMiIilIySDR1jwPD5wP0TroPRK+9G8w5lztEYhISlMySKSaXVD6Peg1DL76J+g3GszCjkpEpFVKBon07I3+rKHLnoH+Y8KORkQkbjq1NFHWvwxL5sHEb8PgCWFHIyLSJkoGiVC3B0qvgp5DYdr1YUcjIhmotj7Ky6s/Y/7rHyVl+eomSoRF/xe2rfFPKNN9hkQkQbbsqmHRe5/y/KpP+efqz6isqadHUYTzxheTl5vY3/JKBu1RVw1v3Q+v3u4fSjP8hLAjEpE05pxj1aZdPLdqM8+++ynLNuwAoH+3As4aO5Bpo/ox6fDeCU8EoGRwaKoroOxuePV3sPtTKJ4Ap/ws7KhEJA1Eo46lG7bz9IrNPLdqM59V1tIQddRHozREHXUNDoCxg3tw9SlHMG1UP8YM7IYl+cxEJYO2+OQdWHofvPVnqNkJI6bBpDkw7Es6hVREWhSNOpas30bpso95ZuVmtuyqIZJrfHFEH6aM7EtujpGbY+SYMaxPESeO6ke/roUdGqOSQWv2bIcVf4M374WP34TcfDjybDj+Khh4TNjRiUgKqm+Isr2qjg3bq3jqnU94fNnHbKqopjCSw7RR/ThtzGGcOKof3QojYYe6l5JBU9EobHoLVj/rh/Il/nYS/Ub7W0ocPROKeoUdpYh0AOcc1XVRdlXXsbO6nqraeuoaotQ1OOobHJU19ZRvr2LDtio+2lZF+fY9fFZZw449dTjf20NejnHCEX255vRRnHxkfzoXpGazmzJRmdl04FYgF5jnnPtFUj5o2zp/pfDeYSfs+BC2vA9b3oWtq6GuytcdOA6m/Ag+Px0GHquuIJE0UtcQpbY+6l+DBryyup4dVbXs2FNHRVUdlTX17KlrYE9tA3vqGqjYU8eWXTV+qKxh++5a6qOu1c/qWpDHkN5FDO/bmeOG96J35wJ6d8mnb5cCJg7vTc/O+R3wjdsnJZKBmeUCvwVOAcqBJWZW6pxbmfAP+91EqK8+sLz7YOhzBAydDAOO8ccDuvRN+MeLpBvnHFEX84rDOWiIOhqcIxr15VHniDoHjv3quWAZjfPUR329huDVBfPWR/2v7b2Nd71/ramLUlMfpaa+gd019eyuDV5rGqiua9jbmFfVNbCruo5d1fXsqq6jui4a93c0g6JILt06RejbtYAB3Qs5urg7PTvn060wQpfCPLoV5tE5P49IXg6RXCOSm0OnSC7FPTvRvVMk6Qd4ky0lkgEwAVjtnFsLYGbzgRlAwpPBzYXfo6ohl91WRJUVUUUnPsvpTbXrBFvww0rgucR9dOu/K5rUd22dQ9JJq1vXQX3UN5aNDe7B/yT8xMY6jVWdc0FDvP/7xkpRt68RdzGNd+Myoq1+bjgiuUbnAt8wd8rPpVPED90K8yju0YmuhXnBEKEwkkNeTo5vwHOMLoV59OiUT4+iCN07RehamEdhJJeCvJy0b8zbK1WSwSBgQ8z7cuC4ppXM7ArgCoAhQ4Yc0gd9XHwGtQ37fjF0AgYf0pLaxmjjH1p2/11mvNY2b16OkZuTQ16OkZNDqw1V49TGao1/b2Z+Wuz8jaO5ZuTkWFDHYur66TlmmBk5wfQcC2wwBocAAAf2SURBVJZnvm6u7TsDJscgN6gQW39fDH6evFxfPzfH9n5+4/w5OUZ+bg6R3Bzycv14QV4O+Xk5FOT5BrtzQR75ebpxQjKkSjKIi3NuLjAXoKSk5JB+s9wya1xCYxIRyQSpkmI3sv8P9OKgTEREOkCqJIMlwEgzG2Zm+cAsoDTkmEREskZKdBM55+rN7LvA0/hTS+92zq0IOSwRkayREskAwDn3JPBk2HGIiGSjVOkmEhGRECkZiIiIkoGIiCgZiIgIYOl66wMz2wJ8eIiz9wE+S2A4yaAYE0MxJkY6xAjpEWeYMX7OOdfsTdfSNhm0h5mVOedKwo7jYBRjYijGxEiHGCE94kzVGNVNJCIiSgYiIpK9yWBu2AHEQTEmhmJMjHSIEdIjzpSMMSuPGYiIyP6ydc9ARERiKBmIiEjjs0nTYwDuBj4F3mlSPhH4A/4Zym8Abwev04LpRcDfgXeBFcAvmsw/AHgGOAZ4NaizHJgZU2cY8BqwGngIyI8j3sHAIvyDNFcA328m5t5BnUrg9maWcQ1wAXB1sJzlwHP484Ub68wGPgiG2cmOC/g5/sl0lc0sOyHrMs4Ym93eHRVjk2UWAq8Dy4Ll/TRm2izgumA7Lg/ifQUY22QZdwKTgF8Ff6vLgUeBHjF1rg1ieg84LY5tHU9co4L1UAP8KJ5t0IbtMD4oXw3cRtA1nah111ExtrBuc4GlwBPxrtNkbuv2DkldeMKDhS8Bx3JgMvgp8BVgHDAwKDsK2BiMFwEnBuP5wEvA6THzXwL8EDgCGBmUDQQ2NW4c4GFgVsyG/HYc8Q4Ajg3GuwLvA6ObxNwZmAx8i+aTwSKgL3AiUBSUfRt4KBjvBawNXnsG4z2TGVfwDzaA5hvahKzLOGNsdnt3VIxNlmlAl2A8gk8kE4P39+AbnOMbtw1wOvBak2W8hW9gTgXygrJfAr8MxkfjG8wCfLJaA+QmIK5+wBfwCTQ2GbS4DdqwHV4PtoUB/yDm/y4R666jYmxh3V4N/Jn9k8FB12kyt3V7h9Ab+DYHDEM5MBn8E+jezB/YNqCgmWXcClwe8/6h2D+gmPJlwMhgWZ/FbLQvAk8fQuyPAac0FzNwMQc2ut2Al5tZzrjGcuBrwO9jpv0e+Foy44qZ1lxDm5R1ebAYD7a9OzLGmOUUAW/in+NtwXKtSZ2e7N8gHQk83MyyzgUeCMavBa6NmfY08MVExQXcSDMNV3PbIJ7tgG+o342Ztt/faqLWXUfHGNQrxu+hTyNIBvGu047Y1ocypP0xAzPrA9Q55yqaTPoK8KZzrqZJ/R7AWfgNiZnlAp93zq1sUm8Cfi9iDb7LZIdzrj6YXA4MamOcQ/GN+GsHibmpkxvjbOIy/C8Ygjg2xExrU2yHGFdLy0rKuowzxma3d0fF2LhsM3sL35W50Dn3WhD3Mhf8R8eI3Ybgf+0+1cxiL6Wd27qNcbW0jKHBPK8F7+PZDoOCGFuNt53rrkNibOIW4MdANKYs3nWatG3dHinzcJt2OBXf/7uXmY3B73Kd2qQ8D3gQuM05tzYoPo7gjyem3gDgPnz/e9TM2hWgmXUB/grMcc7tNLOvN425BdOBPzZZ1oVACXBCu4JqX1wtSfi6jCfGlrZ3R8XYyDnXABwT/OB41MyOwm/D/RouMzsR36BNjik+Dd99FVvvOqAeeOCQAmpjXC1pug2C4rj/7xIZYwvrrkNijFnGmcCnzrk3zGxqzKR412nStnV7pP2eAU2yrJkV4w/EXOScW9Ok7lzgA+fcLQeZvxv+YPN1zrnFQfFWoEeQTMDvIm6MJzgzi+D/SB9wzj3S3GcexAR8f2bjsk7GH5w6O+YX8Eb8QbRGccXWzrhaktB1GU+MrWzvpMfYHOfcDvyxnuk0aZDM7GhgHjDDObc1KCvCH6v4OKbexcCZwAUxvzQPaVvHE1dLWtgGEN922BjEGHe8bV13YcSIP/B7tpmtB+YD08zs/qbxNqejtvUhSWYfVDIGYo4Z0KSPDugRvP/XZua7Cf8Hk9Ok/BWgazCej++WmdPM/H9h/wOK34kjVgPuBW5pUtZcP+jFxPTNA2OA+THvx+G7MEY2ma8XsA7fj9ozGO+VrLiaTKts8j5h6zKeGA+2vTsixib1+7Lv4HMn/EkKM4B/xtQZgj875Pgm836ZmDPc8A3hSqBvk3pj2P+g4lpaP4DcalwxdW9k/wPIB2yDQ/i/a3pw9owEr7sOifEg63cq8ATQPc51mrRt3d4haQtOSrC+i2cTUIfvQ/t34E8x068HduOP1DcO/fBZ1QGrYsq/GfwRPh8z/4XBsmPnPyaYNjz4o1mNbygOODDdTLyTg89dHrO8n8TGHNRbjz+gVRl8r9HAj4CLY+o8C2yOWU5pzLRLg7hWA5ckM66g/ObgfTR4vTHR6zKeGFva3h0VY5N4j8afZrgceCeI9Tzgxpg684DtMZ9VFpTfDkyNqbca31/cWO/OmGnX4X8UvEccZ73EGddhwTraCewIxru1sA3OwHdTxrsdSoLPXRN8z+ZOLW3PuuuQGA+yfqfik0G86zRp27q9Q1rfjsLMrgdWO+fmH+L8FwLFzrlfJDayg35mXDGb2UL87uymVIrrIPMnfV2mQ4xNPm8eMM/t635qqd6bwHHOubpUiusg87drO8T5GSkfY5PPS8lt3RZpnQxERCQxMuEAsoiItJOSgYiIKBmIiIiSgYiIoGQgIiIoGYiICPD/AQl2Kn0pW1goAAAAAElFTkSuQmCC\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": "03b04c47-fc5d-4d92-87b7-d8d63bb6226c"
      },
      "source": [
        "corona_dataset_aggregated.loc[\"India\"][-75:].plot()\n",
        "\n",
        "corona_dataset_aggregated.loc[\"China\"][-75:].plot()\n",
        "\n",
        "plt.legend()"
      ],
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f8477194c18>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 74
        },
        {
          "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": "9cc85bcc-c3a9-4ffd-e04e-83229030fc68"
      },
      "source": [
        "corona_dataset_aggregated.loc[\"China\"].diff().plot()"
      ],
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f847710ef98>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 75
        },
        {
          "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": "23fd22bf-d6e3-4b89-a950-05b3fc4704f5"
      },
      "source": [
        "corona_dataset_aggregated.loc[\"China\"].diff().max()"
      ],
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "15136.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5xpXWFB0jvm5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "outputId": "628006f5-2f4a-47d4-e6a7-c9039f249086"
      },
      "source": [
        "corona_dataset_aggregated.loc[\"Italy\"].diff().plot()"
      ],
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f8477095c50>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 77
        },
        {
          "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": "16886f11-26cd-4a1d-fbf9-6e927a1d9d19"
      },
      "source": [
        "corona_dataset_aggregated.loc[\"Italy\"].diff().max()"
      ],
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "6557.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WgoxP6AYjvnB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "f8047e2f-6dc6-4b19-bc62-1818652be9b9"
      },
      "source": [
        "corona_dataset_aggregated.loc[\"Spain\"].diff().max()"
      ],
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "9630.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 79
        }
      ]
    },
    {
      "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": "04edf693-425f-44a6-bf90-bde7517a2084"
      },
      "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": 80,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "188"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mgKUA332jvnH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "corona_dataset_aggregated['max_infection_rates']=max_infection_rates"
      ],
      "execution_count": 81,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R-EUpfjqjvnJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "e1ecb9f5-c038-4e88-a04c-375e301e0b49"
      },
      "source": [
        "corona_dataset_aggregated.head()"
      ],
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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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",
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              "      <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",
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              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "      <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>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>9998</td>\n",
              "      <td>10582</td>\n",
              "      <td>11173</td>\n",
              "      <td>11831</td>\n",
              "      <td>12456</td>\n",
              "      <td>13036</td>\n",
              "      <td>13659</td>\n",
              "      <td>14525</td>\n",
              "      <td>15205</td>\n",
              "      <td>15750</td>\n",
              "      <td>16509</td>\n",
              "      <td>17267</td>\n",
              "      <td>18054</td>\n",
              "      <td>18969</td>\n",
              "      <td>19551</td>\n",
              "      <td>20342</td>\n",
              "      <td>20917</td>\n",
              "      <td>21459</td>\n",
              "      <td>22142</td>\n",
              "      <td>22890</td>\n",
              "      <td>23546</td>\n",
              "      <td>24102</td>\n",
              "      <td>24766</td>\n",
              "      <td>25527</td>\n",
              "      <td>26310</td>\n",
              "      <td>26874</td>\n",
              "      <td>27532</td>\n",
              "      <td>27878</td>\n",
              "      <td>28424</td>\n",
              "      <td>28833</td>\n",
              "      <td>29157</td>\n",
              "      <td>29481</td>\n",
              "      <td>29640</td>\n",
              "      <td>30175</td>\n",
              "      <td>30451</td>\n",
              "      <td>30616</td>\n",
              "      <td>30967</td>\n",
              "      <td>31238</td>\n",
              "      <td>31517</td>\n",
              "      <td>915.0</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>989</td>\n",
              "      <td>998</td>\n",
              "      <td>1004</td>\n",
              "      <td>1029</td>\n",
              "      <td>1050</td>\n",
              "      <td>1076</td>\n",
              "      <td>1099</td>\n",
              "      <td>1122</td>\n",
              "      <td>1137</td>\n",
              "      <td>1143</td>\n",
              "      <td>1164</td>\n",
              "      <td>1184</td>\n",
              "      <td>1197</td>\n",
              "      <td>1212</td>\n",
              "      <td>1232</td>\n",
              "      <td>1246</td>\n",
              "      <td>1263</td>\n",
              "      <td>1299</td>\n",
              "      <td>1341</td>\n",
              "      <td>1385</td>\n",
              "      <td>1416</td>\n",
              "      <td>1464</td>\n",
              "      <td>1521</td>\n",
              "      <td>1590</td>\n",
              "      <td>1672</td>\n",
              "      <td>1722</td>\n",
              "      <td>1788</td>\n",
              "      <td>1838</td>\n",
              "      <td>1891</td>\n",
              "      <td>1962</td>\n",
              "      <td>1995</td>\n",
              "      <td>2047</td>\n",
              "      <td>2114</td>\n",
              "      <td>2192</td>\n",
              "      <td>2269</td>\n",
              "      <td>2330</td>\n",
              "      <td>2402</td>\n",
              "      <td>2466</td>\n",
              "      <td>2535</td>\n",
              "      <td>82.0</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>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>8113</td>\n",
              "      <td>8306</td>\n",
              "      <td>8503</td>\n",
              "      <td>8697</td>\n",
              "      <td>8857</td>\n",
              "      <td>8997</td>\n",
              "      <td>9134</td>\n",
              "      <td>9267</td>\n",
              "      <td>9394</td>\n",
              "      <td>9513</td>\n",
              "      <td>9626</td>\n",
              "      <td>9733</td>\n",
              "      <td>9831</td>\n",
              "      <td>9935</td>\n",
              "      <td>10050</td>\n",
              "      <td>10154</td>\n",
              "      <td>10265</td>\n",
              "      <td>10382</td>\n",
              "      <td>10484</td>\n",
              "      <td>10589</td>\n",
              "      <td>10698</td>\n",
              "      <td>10810</td>\n",
              "      <td>10919</td>\n",
              "      <td>11031</td>\n",
              "      <td>11147</td>\n",
              "      <td>11268</td>\n",
              "      <td>11385</td>\n",
              "      <td>11504</td>\n",
              "      <td>11631</td>\n",
              "      <td>11771</td>\n",
              "      <td>11920</td>\n",
              "      <td>12076</td>\n",
              "      <td>12248</td>\n",
              "      <td>12445</td>\n",
              "      <td>12685</td>\n",
              "      <td>12968</td>\n",
              "      <td>13273</td>\n",
              "      <td>13571</td>\n",
              "      <td>13907</td>\n",
              "      <td>336.0</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>762</td>\n",
              "      <td>762</td>\n",
              "      <td>763</td>\n",
              "      <td>763</td>\n",
              "      <td>763</td>\n",
              "      <td>763</td>\n",
              "      <td>764</td>\n",
              "      <td>764</td>\n",
              "      <td>764</td>\n",
              "      <td>765</td>\n",
              "      <td>844</td>\n",
              "      <td>851</td>\n",
              "      <td>852</td>\n",
              "      <td>852</td>\n",
              "      <td>852</td>\n",
              "      <td>852</td>\n",
              "      <td>852</td>\n",
              "      <td>852</td>\n",
              "      <td>852</td>\n",
              "      <td>852</td>\n",
              "      <td>853</td>\n",
              "      <td>853</td>\n",
              "      <td>853</td>\n",
              "      <td>853</td>\n",
              "      <td>854</td>\n",
              "      <td>854</td>\n",
              "      <td>855</td>\n",
              "      <td>855</td>\n",
              "      <td>855</td>\n",
              "      <td>855</td>\n",
              "      <td>855</td>\n",
              "      <td>855</td>\n",
              "      <td>855</td>\n",
              "      <td>855</td>\n",
              "      <td>855</td>\n",
              "      <td>855</td>\n",
              "      <td>855</td>\n",
              "      <td>855</td>\n",
              "      <td>855</td>\n",
              "      <td>79.0</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>61</td>\n",
              "      <td>69</td>\n",
              "      <td>70</td>\n",
              "      <td>70</td>\n",
              "      <td>71</td>\n",
              "      <td>74</td>\n",
              "      <td>81</td>\n",
              "      <td>84</td>\n",
              "      <td>86</td>\n",
              "      <td>86</td>\n",
              "      <td>86</td>\n",
              "      <td>86</td>\n",
              "      <td>86</td>\n",
              "      <td>86</td>\n",
              "      <td>88</td>\n",
              "      <td>91</td>\n",
              "      <td>92</td>\n",
              "      <td>96</td>\n",
              "      <td>113</td>\n",
              "      <td>118</td>\n",
              "      <td>130</td>\n",
              "      <td>138</td>\n",
              "      <td>140</td>\n",
              "      <td>142</td>\n",
              "      <td>148</td>\n",
              "      <td>155</td>\n",
              "      <td>166</td>\n",
              "      <td>172</td>\n",
              "      <td>176</td>\n",
              "      <td>183</td>\n",
              "      <td>186</td>\n",
              "      <td>189</td>\n",
              "      <td>197</td>\n",
              "      <td>212</td>\n",
              "      <td>212</td>\n",
              "      <td>259</td>\n",
              "      <td>267</td>\n",
              "      <td>276</td>\n",
              "      <td>284</td>\n",
              "      <td>47.0</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  ...    31517                915.0\n",
              "Albania               0        0  ...     2535                 82.0\n",
              "Algeria               0        0  ...    13907                336.0\n",
              "Andorra               0        0  ...      855                 79.0\n",
              "Angola                0        0  ...      284                 47.0\n",
              "\n",
              "[5 rows x 162 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VvxTroTYjvnM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 82,
      "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": 83,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8WtLSH22jvnU",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 233
        },
        "outputId": "73c5f878-2edf-4077-8502-65167987ce3b"
      },
      "source": [
        "corona_data.head()"
      ],
      "execution_count": 84,
      "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>915.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Albania</th>\n",
              "      <td>82.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Algeria</th>\n",
              "      <td>336.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Andorra</th>\n",
              "      <td>79.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Angola</th>\n",
              "      <td>47.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                max_infection_rates\n",
              "Country/Region                     \n",
              "Afghanistan                   915.0\n",
              "Albania                        82.0\n",
              "Algeria                       336.0\n",
              "Andorra                        79.0\n",
              "Angola                         47.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 84
        }
      ]
    },
    {
      "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": 85,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "295eZWmzjvna",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "outputId": "8000a2f6-ebe0-4d21-c915-bd3342c50112"
      },
      "source": [
        "happiness_report_csv.head()"
      ],
      "execution_count": 86,
      "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": 86
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NLXe9B29jvnd",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "3c31feac-d8fc-4c43-a39f-e79008f1c776"
      },
      "source": [
        "happiness_report_csv.shape"
      ],
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(156, 9)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 87
        }
      ]
    },
    {
      "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": "2153b7f2-c773-45be-bd6d-c2fd7ff02ebf"
      },
      "source": [
        "happiness_report_csv.columns"
      ],
      "execution_count": 88,
      "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": 88
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GmSWzYCujvnj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "useless_cols=['Overall rank','Score','Generosity','Perceptions of corruption']"
      ],
      "execution_count": 89,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Qy_uyassjvnm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        },
        "outputId": "8c6c5e46-d646-49e9-8a39-e23f4c4e6b18"
      },
      "source": [
        "happiness_report_csv.drop(useless_cols,axis=1,inplace=True)\n",
        "happiness_report_csv.head()"
      ],
      "execution_count": 90,
      "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": 90
        }
      ]
    },
    {
      "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": 91,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r6j5Il2vjvns",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 233
        },
        "outputId": "5a55fc39-32f3-4d24-e488-9086599e1d35"
      },
      "source": [
        "happiness_report_csv.head()"
      ],
      "execution_count": 92,
      "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": 92
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k9heAl1hjvnu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 92,
      "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": "ca9024f8-c5b4-4ce6-cbd5-eada0320cf1e"
      },
      "source": [
        "corona_data.head()"
      ],
      "execution_count": 93,
      "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>915.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Albania</th>\n",
              "      <td>82.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Algeria</th>\n",
              "      <td>336.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Andorra</th>\n",
              "      <td>79.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Angola</th>\n",
              "      <td>47.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                max_infection_rates\n",
              "Country/Region                     \n",
              "Afghanistan                   915.0\n",
              "Albania                        82.0\n",
              "Algeria                       336.0\n",
              "Andorra                        79.0\n",
              "Angola                         47.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 93
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pzFXHUaUjvny",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "c1083c4f-7b5a-4b2b-fc15-26f85dd25314"
      },
      "source": [
        "corona_data.shape"
      ],
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(188, 1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 94
        }
      ]
    },
    {
      "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": "2a61d222-a424-41e3-f5f4-9afdad480d14"
      },
      "source": [
        "happiness_report_csv.shape"
      ],
      "execution_count": 95,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(156, 4)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 95
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zvfdUBpVjvn4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 219
        },
        "outputId": "1d1b3db8-30ca-45a6-915d-3a82acbd8cbf"
      },
      "source": [
        "data=corona_data.join(happiness_report_csv, how=\"inner\")\n",
        "data.head()"
      ],
      "execution_count": 96,
      "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>915.0</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>82.0</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>336.0</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>2886.0</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>771.0</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                915.0  ...                         0.000\n",
              "Albania                     82.0  ...                         0.383\n",
              "Algeria                    336.0  ...                         0.086\n",
              "Argentina                 2886.0  ...                         0.471\n",
              "Armenia                    771.0  ...                         0.283\n",
              "\n",
              "[5 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 96
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JgyULhQ0jvn8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "cfa9e9c2-a543-4d45-c088-a69f1d44100e"
      },
      "source": [
        "data.shape"
      ],
      "execution_count": 97,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(144, 5)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 97
        }
      ]
    },
    {
      "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": "2d45bf2f-a929-4f9f-d65b-879675f4a206"
      },
      "source": [
        "data.corr()"
      ],
      "execution_count": 98,
      "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>0.161995</td>\n",
              "      <td>0.124306</td>\n",
              "      <td>0.165086</td>\n",
              "      <td>0.048141</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GDP per capita</th>\n",
              "      <td>0.161995</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>0.124306</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>0.165086</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>0.048141</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  ...                      0.048141\n",
              "GDP per capita                           0.161995  ...                      0.394799\n",
              "Social support                           0.124306  ...                      0.456317\n",
              "Healthy life expectancy                  0.165086  ...                      0.423146\n",
              "Freedom to make life choices             0.048141  ...                      1.000000\n",
              "\n",
              "[5 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 98
        }
      ]
    },
    {
      "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": "bf3294d9-eb2b-4124-d17f-2b51c1b6bc48"
      },
      "source": [
        "data.head()"
      ],
      "execution_count": 99,
      "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>915.0</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>82.0</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>336.0</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>2886.0</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>771.0</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                915.0  ...                         0.000\n",
              "Albania                     82.0  ...                         0.383\n",
              "Algeria                    336.0  ...                         0.086\n",
              "Argentina                 2886.0  ...                         0.471\n",
              "Armenia                    771.0  ...                         0.283\n",
              "\n",
              "[5 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 99
        }
      ]
    },
    {
      "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": 297
        },
        "outputId": "9ab3b9fb-b8e7-420d-ba35-da8055c1450b"
      },
      "source": [
        "x=data[\"GDP per capita\"]\n",
        "y=data[\"max_infection_rates\"]\n",
        "sns.scatterplot(x,np.log(y))"
      ],
      "execution_count": 100,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f8477016978>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 100
        },
        {
          "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": "lvbEjjM7jvoQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "c73f7d62-0879-4503-9e58-e538b5a91076"
      },
      "source": [
        "sns.regplot(x,np.log(y))"
      ],
      "execution_count": 101,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f8476f7df98>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 101
        },
        {
          "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": 297
        },
        "outputId": "c8eff6e2-72f3-4eda-a477-98614d9c03f6"
      },
      "source": [
        "x=data[\"Social support\"]\n",
        "y=data[\"max_infection_rates\"]\n",
        "sns.scatterplot(x,np.log(y))"
      ],
      "execution_count": 102,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f8476f5f0f0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 102
        },
        {
          "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": 297
        },
        "outputId": "19adcfb7-17b1-4fa4-ba6b-9b1fc4c374de"
      },
      "source": [
        "sns.regplot(x,np.log(y))"
      ],
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f8476ec6a58>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 103
        },
        {
          "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": 297
        },
        "outputId": "71d7ec0b-97a2-4801-833b-f517a8035781"
      },
      "source": [
        "x=data[\"Healthy life expectancy\"]\n",
        "y=data[\"max_infection_rates\"]\n",
        "sns.scatterplot(x,np.log(y))"
      ],
      "execution_count": 104,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f8476e333c8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 104
        },
        {
          "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": 297
        },
        "outputId": "65b2fc0f-0598-48ca-80b1-cd9f31ea0bfb"
      },
      "source": [
        "sns.regplot(x,np.log(y))"
      ],
      "execution_count": 105,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f8476f3e668>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 105
        },
        {
          "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": 297
        },
        "outputId": "de6d57be-fa28-434e-c952-3e9c0d767244"
      },
      "source": [
        "x=data[\"Freedom to make life choices\"]\n",
        "y=data[\"max_infection_rates\"]\n",
        "sns.scatterplot(x,np.log(y))"
      ],
      "execution_count": 106,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f8476de1d68>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 106
        },
        {
          "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": 297
        },
        "outputId": "bd8aabd8-060b-41bb-aa74-85b748b15e2e"
      },
      "source": [
        "sns.regplot(x,np.log(y))"
      ],
      "execution_count": 107,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f8476d8aeb8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 107
        },
        {
          "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": "OG1cTuvMsZmV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 132
        },
        "outputId": "6515d224-a1e5-4eb2-9951-6d0083248e4e"
      },
      "source": [
        "I completed this short course to understand covid situation than blindly believing in the Indian Media, "
      ],
      "execution_count": 108,
      "outputs": [
        {
          "output_type": "error",
          "ename": "SyntaxError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-108-c65d972b9595>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    I completed this short course to understand covid situation than blindly believing in the Indian Media,\u001b[0m\n\u001b[0m              ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
          ]
        }
      ]
    }
  ]
}