Coronavirus_Dataset_Enrichment.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Coronavirus Dataset Enrichment.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyP2p63UYXWklk7cyPPF7JxA",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ritesh-chafer/coronavirus-analysis/blob/master/Coronavirus_Dataset_Enrichment.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WjOBeq88Uqa4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        },
        "outputId": "28f65e4f-c76c-4670-aab0-96abc7988b83"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fnoBKnarauX-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "b1a96353-fbbd-47e1-c2a0-acc8e656c72b"
      },
      "source": [
        "ls '/content/drive/My Drive/Colab Notebooks/Twitter.properties'"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "'/content/drive/My Drive/Colab Notebooks/Twitter.properties'\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HyOiQen7ZVz6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        },
        "outputId": "1da0240a-5bc4-484c-bc09-24da549e7461"
      },
      "source": [
        "!pip install ConfigParser"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting ConfigParser\n",
            "  Downloading https://files.pythonhosted.org/packages/4b/6b/01baa293090240cf0562cc5eccb69c6f5006282127f2b846fad011305c79/configparser-5.0.0-py3-none-any.whl\n",
            "Installing collected packages: ConfigParser\n",
            "Successfully installed ConfigParser-5.0.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lK3qxmaod-1a",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "import configparser\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2roXrAcNnS6W",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "corona_confirmed_df = pd.read_csv('https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1cY3AyoQxB_4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 253
        },
        "outputId": "45c055bb-15a8-495a-c3d5-cce31dea5154"
      },
      "source": [
        "corona_confirmed_df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Angola</td>\n",
              "      <td>-11.2027</td>\n",
              "      <td>17.8739</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>52</td>\n",
              "      <td>52</td>\n",
              "      <td>58</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",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 162 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "  Province/State Country/Region      Lat  ...  6/25/20  6/26/20  6/27/20\n",
              "0            NaN    Afghanistan  33.0000  ...    30175    30451    30616\n",
              "1            NaN        Albania  41.1533  ...     2192     2269     2330\n",
              "2            NaN        Algeria  28.0339  ...    12445    12685    12968\n",
              "3            NaN        Andorra  42.5063  ...      855      855      855\n",
              "4            NaN         Angola -11.2027  ...      212      212      259\n",
              "\n",
              "[5 rows x 162 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t8eJXXI8xbz8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "corona_death_df = pd.read_csv('https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CXB_zbGO2onX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 253
        },
        "outputId": "189881e9-371a-43f8-e9ee-44401881d5cf"
      },
      "source": [
        "corona_death_df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <td>51</td>\n",
              "      <td>51</td>\n",
              "      <td>51</td>\n",
              "      <td>51</td>\n",
              "      <td>51</td>\n",
              "      <td>51</td>\n",
              "      <td>51</td>\n",
              "      <td>51</td>\n",
              "      <td>52</td>\n",
              "      <td>52</td>\n",
              "      <td>52</td>\n",
              "      <td>52</td>\n",
              "      <td>52</td>\n",
              "      <td>52</td>\n",
              "      <td>52</td>\n",
              "      <td>52</td>\n",
              "      <td>52</td>\n",
              "      <td>52</td>\n",
              "      <td>52</td>\n",
              "      <td>52</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Angola</td>\n",
              "      <td>-11.2027</td>\n",
              "      <td>17.8739</td>\n",
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              "      <td>4</td>\n",
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              "      <td>4</td>\n",
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              "      <td>4</td>\n",
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              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
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              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>7</td>\n",
              "      <td>8</td>\n",
              "      <td>8</td>\n",
              "      <td>9</td>\n",
              "      <td>9</td>\n",
              "      <td>10</td>\n",
              "      <td>10</td>\n",
              "      <td>10</td>\n",
              "      <td>10</td>\n",
              "      <td>10</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 162 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "  Province/State Country/Region      Lat  ...  6/25/20  6/26/20  6/27/20\n",
              "0            NaN    Afghanistan  33.0000  ...      675      683      703\n",
              "1            NaN        Albania  41.1533  ...       49       51       53\n",
              "2            NaN        Algeria  28.0339  ...      878      885      892\n",
              "3            NaN        Andorra  42.5063  ...       52       52       52\n",
              "4            NaN         Angola -11.2027  ...       10       10       10\n",
              "\n",
              "[5 rows x 162 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-tNXearW22A9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "corona_recovered_df = pd.read_csv('https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_recovered_global.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qrHJdXQ63oE8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 253
        },
        "outputId": "9695daac-6859-47a3-d082-a5bd0b56db1d"
      },
      "source": [
        "corona_recovered_df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <td>676</td>\n",
              "      <td>681</td>\n",
              "      <td>684</td>\n",
              "      <td>692</td>\n",
              "      <td>694</td>\n",
              "      <td>698</td>\n",
              "      <td>733</td>\n",
              "      <td>735</td>\n",
              "      <td>738</td>\n",
              "      <td>741</td>\n",
              "      <td>741</td>\n",
              "      <td>744</td>\n",
              "      <td>751</td>\n",
              "      <td>757</td>\n",
              "      <td>759</td>\n",
              "      <td>780</td>\n",
              "      <td>781</td>\n",
              "      <td>781</td>\n",
              "      <td>781</td>\n",
              "      <td>789</td>\n",
              "      <td>789</td>\n",
              "      <td>791</td>\n",
              "      <td>792</td>\n",
              "      <td>792</td>\n",
              "      <td>792</td>\n",
              "      <td>792</td>\n",
              "      <td>796</td>\n",
              "      <td>797</td>\n",
              "      <td>797</td>\n",
              "      <td>797</td>\n",
              "      <td>799</td>\n",
              "      <td>799</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Angola</td>\n",
              "      <td>-11.2027</td>\n",
              "      <td>17.8739</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>17</td>\n",
              "      <td>17</td>\n",
              "      <td>17</td>\n",
              "      <td>17</td>\n",
              "      <td>18</td>\n",
              "      <td>18</td>\n",
              "      <td>18</td>\n",
              "      <td>18</td>\n",
              "      <td>18</td>\n",
              "      <td>18</td>\n",
              "      <td>18</td>\n",
              "      <td>18</td>\n",
              "      <td>18</td>\n",
              "      <td>18</td>\n",
              "      <td>18</td>\n",
              "      <td>18</td>\n",
              "      <td>18</td>\n",
              "      <td>21</td>\n",
              "      <td>24</td>\n",
              "      <td>24</td>\n",
              "      <td>38</td>\n",
              "      <td>38</td>\n",
              "      <td>40</td>\n",
              "      <td>41</td>\n",
              "      <td>42</td>\n",
              "      <td>61</td>\n",
              "      <td>61</td>\n",
              "      <td>64</td>\n",
              "      <td>64</td>\n",
              "      <td>64</td>\n",
              "      <td>64</td>\n",
              "      <td>66</td>\n",
              "      <td>66</td>\n",
              "      <td>77</td>\n",
              "      <td>77</td>\n",
              "      <td>77</td>\n",
              "      <td>77</td>\n",
              "      <td>81</td>\n",
              "      <td>81</td>\n",
              "      <td>81</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 162 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "  Province/State Country/Region      Lat  ...  6/25/20  6/26/20  6/27/20\n",
              "0            NaN    Afghanistan  33.0000  ...    10174    10306    10674\n",
              "1            NaN        Albania  41.1533  ...     1250     1298     1346\n",
              "2            NaN        Algeria  28.0339  ...     8920     9066     9202\n",
              "3            NaN        Andorra  42.5063  ...      797      799      799\n",
              "4            NaN         Angola -11.2027  ...       81       81       81\n",
              "\n",
              "[5 rows x 162 columns]"
            ]
          },
          "metadata": {
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          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7vOAaeQK5RS9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "corona_confirmed_df = corona_confirmed_df.melt(id_vars=['Province/State','Country/Region','Lat','Long'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fqQWBsjb8uCN",
        "colab_type": "code",
        "colab": {
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          "height": 204
        },
        "outputId": "e8dfc2ef-f2f1-4f44-b60c-3609f9729456"
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      "source": [
        "corona_confirmed_df.head()"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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            "text/plain": [
              "  Province/State Country/Region      Lat     Long variable  value\n",
              "0            NaN    Afghanistan  33.0000  65.0000  1/22/20      0\n",
              "1            NaN        Albania  41.1533  20.1683  1/22/20      0\n",
              "2            NaN        Algeria  28.0339   1.6596  1/22/20      0\n",
              "3            NaN        Andorra  42.5063   1.5218  1/22/20      0\n",
              "4            NaN         Angola -11.2027  17.8739  1/22/20      0"
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      "source": [
        "corona_confirmed_df = corona_confirmed_df.rename({'variable':'Date','value':'Confirmed'},axis = 'columns')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
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      "source": [
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            "text/plain": [
              "  Province/State Country/Region      Lat     Long     Date  Confirmed\n",
              "0            NaN    Afghanistan  33.0000  65.0000  1/22/20          0\n",
              "1            NaN        Albania  41.1533  20.1683  1/22/20          0\n",
              "2            NaN        Algeria  28.0339   1.6596  1/22/20          0\n",
              "3            NaN        Andorra  42.5063   1.5218  1/22/20          0\n",
              "4            NaN         Angola -11.2027  17.8739  1/22/20          0"
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      "source": [
        "corona_death_df = corona_death_df.melt(id_vars=['Province/State','Country/Region','Lat','Long'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vvvLb09B5SB9",
        "colab_type": "code",
        "colab": {}
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      "source": [
        "corona_death_df = corona_death_df.rename({'variable':'Date','value':'Death'},axis = 'columns')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
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        "outputId": "a49d9bd7-0e0d-44e6-faee-6b52245c042f"
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      "source": [
        "corona_death_df.head()"
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      "execution_count": null,
      "outputs": [
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            "text/plain": [
              "  Province/State Country/Region      Lat     Long     Date  Death\n",
              "0            NaN    Afghanistan  33.0000  65.0000  1/22/20      0\n",
              "1            NaN        Albania  41.1533  20.1683  1/22/20      0\n",
              "2            NaN        Algeria  28.0339   1.6596  1/22/20      0\n",
              "3            NaN        Andorra  42.5063   1.5218  1/22/20      0\n",
              "4            NaN         Angola -11.2027  17.8739  1/22/20      0"
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        "id": "m19yUF9Q5SIW",
        "colab_type": "code",
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      "source": [
        "corona_recovered_df = corona_recovered_df.melt(id_vars=['Province/State','Country/Region','Lat','Long'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t8kcNDiU5Ry1",
        "colab_type": "code",
        "colab": {}
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      "source": [
        "corona_recovered_df = corona_recovered_df.rename({'variable':'Date','value':'Recovered'},axis = 'columns')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
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      "source": [
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      "execution_count": null,
      "outputs": [
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              "      <th>4</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Angola</td>\n",
              "      <td>-11.2027</td>\n",
              "      <td>17.8739</td>\n",
              "      <td>1/22/20</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Province/State Country/Region      Lat     Long     Date  Recovered\n",
              "0            NaN    Afghanistan  33.0000  65.0000  1/22/20          0\n",
              "1            NaN        Albania  41.1533  20.1683  1/22/20          0\n",
              "2            NaN        Algeria  28.0339   1.6596  1/22/20          0\n",
              "3            NaN        Andorra  42.5063   1.5218  1/22/20          0\n",
              "4            NaN         Angola -11.2027  17.8739  1/22/20          0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4mAYqPfs7kna",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "combined_df = [corona_confirmed_df, corona_death_df, corona_recovered_df]\n",
        "combined_df = [df.set_index(['Province/State','Country/Region','Lat','Long','Date']) for df in combined_df]\n",
        "combined_df = combined_df[0].join(combined_df[1:])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ENNBBlkS7kqS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "outputId": "d962a5e2-1de7-458c-a3cf-07c0b9b097d7"
      },
      "source": [
        "combined_df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>Confirmed</th>\n",
              "      <th>Death</th>\n",
              "      <th>Recovered</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Province/State</th>\n",
              "      <th>Country/Region</th>\n",
              "      <th>Lat</th>\n",
              "      <th>Long</th>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"5\" valign=\"top\">NaN</th>\n",
              "      <th>Afghanistan</th>\n",
              "      <th>33.0000</th>\n",
              "      <th>65.0000</th>\n",
              "      <th>1/22/20</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Albania</th>\n",
              "      <th>41.1533</th>\n",
              "      <th>20.1683</th>\n",
              "      <th>1/22/20</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Algeria</th>\n",
              "      <th>28.0339</th>\n",
              "      <th>1.6596</th>\n",
              "      <th>1/22/20</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Andorra</th>\n",
              "      <th>42.5063</th>\n",
              "      <th>1.5218</th>\n",
              "      <th>1/22/20</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Angola</th>\n",
              "      <th>-11.2027</th>\n",
              "      <th>17.8739</th>\n",
              "      <th>1/22/20</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                        Confirmed  ...  Recovered\n",
              "Province/State Country/Region Lat      Long    Date                ...           \n",
              "NaN            Afghanistan     33.0000 65.0000 1/22/20        0.0  ...        0.0\n",
              "               Albania         41.1533 20.1683 1/22/20        0.0  ...        0.0\n",
              "               Algeria         28.0339 1.6596  1/22/20        0.0  ...        0.0\n",
              "               Andorra         42.5063 1.5218  1/22/20        0.0  ...        0.0\n",
              "               Angola         -11.2027 17.8739 1/22/20        0.0  ...        0.0\n",
              "\n",
              "[5 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R6GsRaIm7kvj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "combined_df = combined_df.reset_index()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AjPrkTKT7ky8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "d62a0375-11ef-4c9b-d18d-1ef9609de2f2"
      },
      "source": [
        "combined_df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <th></th>\n",
              "      <th>Province/State</th>\n",
              "      <th>Country/Region</th>\n",
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              "      <th>1</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Albania</td>\n",
              "      <td>41.1533</td>\n",
              "      <td>20.1683</td>\n",
              "      <td>1/22/20</td>\n",
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              "      <td>1.6596</td>\n",
              "      <td>1/22/20</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>NaN</td>\n",
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              "      <td>42.5063</td>\n",
              "      <td>1.5218</td>\n",
              "      <td>1/22/20</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Angola</td>\n",
              "      <td>-11.2027</td>\n",
              "      <td>17.8739</td>\n",
              "      <td>1/22/20</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Province/State Country/Region      Lat  ...  Confirmed Death  Recovered\n",
              "0            NaN    Afghanistan  33.0000  ...        0.0   0.0        0.0\n",
              "1            NaN        Albania  41.1533  ...        0.0   0.0        0.0\n",
              "2            NaN        Algeria  28.0339  ...        0.0   0.0        0.0\n",
              "3            NaN        Andorra  42.5063  ...        0.0   0.0        0.0\n",
              "4            NaN         Angola -11.2027  ...        0.0   0.0        0.0\n",
              "\n",
              "[5 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fixMLzNR7k17",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "combined_df[['Lat','Long','Confirmed','Death','Recovered']] = combined_df[['Lat','Long','Confirmed','Death','Recovered']].apply(pd.to_numeric)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x6GYzbAt7k72",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "combined_df[['Date']] = combined_df[['Date']].apply(pd.to_datetime)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ITgwMPJGEt3d",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "combined_df.Lat = combined_df.Lat.astype(int)\n",
        "combined_df.Long = combined_df.Long.astype(int)\n",
        "combined_df.Confirmed = combined_df.Confirmed.astype(int)\n",
        "combined_df.Death = combined_df.Death.astype(int)\n",
        "combined_df.Recovered = combined_df.Recovered.fillna(0.0).astype(int)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Szr37Aw47k_N",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170
        },
        "outputId": "63b1e1d4-41ac-457a-efcb-ca46d8a0061a"
      },
      "source": [
        "combined_df.dtypes"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Province/State            object\n",
              "Country/Region            object\n",
              "Lat                        int64\n",
              "Long                       int64\n",
              "Date              datetime64[ns]\n",
              "Confirmed                  int64\n",
              "Death                      int64\n",
              "Recovered                  int64\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u3NhwO8c7k53",
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          "height": 204
        },
        "outputId": "dc91bd6f-417a-4607-91ac-a4df6b90f425"
      },
      "source": [
        "combined_df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <th></th>\n",
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              "      <td>1</td>\n",
              "      <td>2020-01-22</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Andorra</td>\n",
              "      <td>42</td>\n",
              "      <td>1</td>\n",
              "      <td>2020-01-22</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Angola</td>\n",
              "      <td>-11</td>\n",
              "      <td>17</td>\n",
              "      <td>2020-01-22</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Province/State Country/Region  Lat  ...  Confirmed Death  Recovered\n",
              "0            NaN    Afghanistan   33  ...          0     0          0\n",
              "1            NaN        Albania   41  ...          0     0          0\n",
              "2            NaN        Algeria   28  ...          0     0          0\n",
              "3            NaN        Andorra   42  ...          0     0          0\n",
              "4            NaN         Angola  -11  ...          0     0          0\n",
              "\n",
              "[5 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mRaSNb75HjFm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "combined_df = combined_df.rename({'Province/State':'State','Country/Region':'Country'},axis = 'columns')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IlA7DigaHjfJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "ece2c2a0-b2c0-4d0c-b1fa-fe9686d557ba"
      },
      "source": [
        "combined_df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <th>Country</th>\n",
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              "    <tr>\n",
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              "      <td>NaN</td>\n",
              "      <td>Albania</td>\n",
              "      <td>41</td>\n",
              "      <td>20</td>\n",
              "      <td>2020-01-22</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Algeria</td>\n",
              "      <td>28</td>\n",
              "      <td>1</td>\n",
              "      <td>2020-01-22</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Andorra</td>\n",
              "      <td>42</td>\n",
              "      <td>1</td>\n",
              "      <td>2020-01-22</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Angola</td>\n",
              "      <td>-11</td>\n",
              "      <td>17</td>\n",
              "      <td>2020-01-22</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  State      Country  Lat  Long       Date  Confirmed  Death  Recovered\n",
              "0   NaN  Afghanistan   33    65 2020-01-22          0      0          0\n",
              "1   NaN      Albania   41    20 2020-01-22          0      0          0\n",
              "2   NaN      Algeria   28     1 2020-01-22          0      0          0\n",
              "3   NaN      Andorra   42     1 2020-01-22          0      0          0\n",
              "4   NaN       Angola  -11    17 2020-01-22          0      0          0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4z4PhL2nHjiS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170
        },
        "outputId": "09b76f16-95a0-40ca-9472-62e8daaf9ab0"
      },
      "source": [
        "combined_df.isnull().any()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "State         True\n",
              "Country      False\n",
              "Lat          False\n",
              "Long         False\n",
              "Date         False\n",
              "Confirmed    False\n",
              "Death        False\n",
              "Recovered    False\n",
              "dtype: bool"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NZff6vEeHjli",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170
        },
        "outputId": "6614fe5d-275b-4435-ea49-8bbd93698f53"
      },
      "source": [
        "combined_df.count()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "State        12798\n",
              "Country      42028\n",
              "Lat          42028\n",
              "Long         42028\n",
              "Date         42028\n",
              "Confirmed    42028\n",
              "Death        42028\n",
              "Recovered    42028\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TTV5nvOtHjou",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450
        },
        "outputId": "b553423d-2c64-4522-cb75-f9ebd53ee51b"
      },
      "source": [
        "combined_df.set_index('Country').filter(like=\"India\",axis=0)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>State</th>\n",
              "      <th>Lat</th>\n",
              "      <th>Long</th>\n",
              "      <th>Date</th>\n",
              "      <th>Confirmed</th>\n",
              "      <th>Death</th>\n",
              "      <th>Recovered</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Country</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>India</th>\n",
              "      <td>NaN</td>\n",
              "      <td>21</td>\n",
              "      <td>78</td>\n",
              "      <td>2020-01-22</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>India</th>\n",
              "      <td>NaN</td>\n",
              "      <td>21</td>\n",
              "      <td>78</td>\n",
              "      <td>2020-01-23</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>India</th>\n",
              "      <td>NaN</td>\n",
              "      <td>21</td>\n",
              "      <td>78</td>\n",
              "      <td>2020-01-24</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>India</th>\n",
              "      <td>NaN</td>\n",
              "      <td>21</td>\n",
              "      <td>78</td>\n",
              "      <td>2020-01-25</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>India</th>\n",
              "      <td>NaN</td>\n",
              "      <td>21</td>\n",
              "      <td>78</td>\n",
              "      <td>2020-01-26</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>India</th>\n",
              "      <td>NaN</td>\n",
              "      <td>21</td>\n",
              "      <td>78</td>\n",
              "      <td>2020-06-23</td>\n",
              "      <td>456183</td>\n",
              "      <td>14476</td>\n",
              "      <td>258685</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>India</th>\n",
              "      <td>NaN</td>\n",
              "      <td>21</td>\n",
              "      <td>78</td>\n",
              "      <td>2020-06-24</td>\n",
              "      <td>473105</td>\n",
              "      <td>14894</td>\n",
              "      <td>271697</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>India</th>\n",
              "      <td>NaN</td>\n",
              "      <td>21</td>\n",
              "      <td>78</td>\n",
              "      <td>2020-06-25</td>\n",
              "      <td>490401</td>\n",
              "      <td>15301</td>\n",
              "      <td>285637</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>India</th>\n",
              "      <td>NaN</td>\n",
              "      <td>21</td>\n",
              "      <td>78</td>\n",
              "      <td>2020-06-26</td>\n",
              "      <td>508953</td>\n",
              "      <td>15685</td>\n",
              "      <td>295881</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>India</th>\n",
              "      <td>NaN</td>\n",
              "      <td>21</td>\n",
              "      <td>78</td>\n",
              "      <td>2020-06-27</td>\n",
              "      <td>528859</td>\n",
              "      <td>16095</td>\n",
              "      <td>309713</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>158 rows × 7 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "        State  Lat  Long       Date  Confirmed  Death  Recovered\n",
              "Country                                                         \n",
              "India     NaN   21    78 2020-01-22          0      0          0\n",
              "India     NaN   21    78 2020-01-23          0      0          0\n",
              "India     NaN   21    78 2020-01-24          0      0          0\n",
              "India     NaN   21    78 2020-01-25          0      0          0\n",
              "India     NaN   21    78 2020-01-26          0      0          0\n",
              "...       ...  ...   ...        ...        ...    ...        ...\n",
              "India     NaN   21    78 2020-06-23     456183  14476     258685\n",
              "India     NaN   21    78 2020-06-24     473105  14894     271697\n",
              "India     NaN   21    78 2020-06-25     490401  15301     285637\n",
              "India     NaN   21    78 2020-06-26     508953  15685     295881\n",
              "India     NaN   21    78 2020-06-27     528859  16095     309713\n",
              "\n",
              "[158 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zMHgrZNyKJNr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        },
        "outputId": "809a5c1a-6718-48c5-e2a3-d5b6edefd160"
      },
      "source": [
        "combined_df[combined_df.State.isnull()]['Country'].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "San Marino                  158\n",
              "Korea, South                158\n",
              "Burundi                     158\n",
              "Qatar                       158\n",
              "Sao Tome and Principe       158\n",
              "                           ... \n",
              "Central African Republic    158\n",
              "Angola                      158\n",
              "Namibia                     158\n",
              "Malaysia                    158\n",
              "Taiwan*                     158\n",
              "Name: Country, Length: 185, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s8_EkGv-KJRV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        },
        "outputId": "6acb5815-bae9-489f-e686-9053c84647eb"
      },
      "source": [
        "!pip install -U googlemaps"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already up-to-date: googlemaps in /usr/local/lib/python3.6/dist-packages (4.4.1)\n",
            "Requirement already satisfied, skipping upgrade: requests<3.0,>=2.20.0 in /usr/local/lib/python3.6/dist-packages (from googlemaps) (2.23.0)\n",
            "Requirement already satisfied, skipping upgrade: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests<3.0,>=2.20.0->googlemaps) (1.24.3)\n",
            "Requirement already satisfied, skipping upgrade: chardet<4,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests<3.0,>=2.20.0->googlemaps) (3.0.4)\n",
            "Requirement already satisfied, skipping upgrade: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests<3.0,>=2.20.0->googlemaps) (2020.6.20)\n",
            "Requirement already satisfied, skipping upgrade: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests<3.0,>=2.20.0->googlemaps) (2.9)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xuv8mZ7xKJU5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import googlemaps"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "41yOKzkjKJa0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 231
        },
        "outputId": "d6389ada-9229-49cc-a227-64151e6109b5"
      },
      "source": [
        "gmaps = googlemaps.Client(key=config.get('twitter','googleapikey'))\n",
        "geocode_result = gmaps.reverse_geocode((-24.4425, 58.4438))\n",
        "print(geocode_result[0])\n",
        "print(geocode_result[0]['formated_address'])\n",
        "print(geocode_result[0]['formated_address'].split(\",\")[-2].strip(\" \"))\n"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-11-c395ed6945a5>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mgmaps\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgooglemaps\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mClient\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'twitter'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'googleapikey'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mgeocode_result\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgmaps\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreverse_geocode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m24.4425\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m58.4438\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgeocode_result\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgeocode_result\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'formated_address'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgeocode_result\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'formated_address'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\",\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstrip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\" \"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'config' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "82JcDOuLKJjA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "plbCCy5zKJmI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tt9ZXy_8KJo_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3Bzmxd2XKJrv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "txbN8tSUKJx9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mwPkglXiKJve",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QiRqN-OzKJgc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZcctWNzCKJe5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "534p2JTiKJYS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}