EDA.ipynb

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      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8.5"
    },
    "colab": {
      "name": "EDA.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/AndreaGriffiths11/24pullrequests/blob/master/EDA.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jSJeWwT7dInf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3d4a23a2-65ba-4071-ad19-6072167fc14b"
      },
      "source": [
        "%pylab inline\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "pd.set_option('display.max_rows', 300)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Populating the interactive namespace from numpy and matplotlib\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hdz4y_RldInm"
      },
      "source": [
        "east_asia_pacific=['Australia','Brunei','Cambodia','China','East Timor (Timor-Leste)','Fiji','Indonesia','Japan','Kiribati','Korea, North', \n",
        "                   'Korea, South','Laos','Malaysia','Micronesia, Federated States of','Myanmar (Burma)','Nauru','New Zealand',\n",
        "                   'Philippines','Singapore','Solomon Islands','Taiwan','Thailand','Vietnam']\n",
        "europe_central_asia=['Albania','Andorra','Armenia','Austria','Azerbaijan','Belarus','Belgium','Bosnia and Herzegovina',\n",
        "                     'Bulgaria','Croatia','Cyprus','Czech Republic','Denmark','Estonia','Finland','France',\n",
        "                     'Georgia','Germany','Greece','Hungary','Iceland','Ireland','Italy','Kazakhstan','Kosovo',\n",
        "                     'Kyrgyzstan','Latvia','Liechtenstein','Lithuania','Luxembourg','Moldova','Monaco','Montenegro',\n",
        "                     'Netherlands','North Macedonia','Norway','Poland','Portugal','Romania','Russia','San Marino',\n",
        "                     'Serbia','Slovakia','Slovenia','Spain','Sweden','Switzerland','Tajikistan','Turkey','Turkmenistan',\n",
        "                     'Ukraine','United Kingdom','Uzbekistan']\n",
        "south_asia=['Afghanistan','Bangladesh','Bhutan','India','Maldives','Nepal','Pakistan','Sri Lanka']\n",
        "sub_saharan=['Angola','Benin','Burkina Faso','Burundi','Cabo Verde','Cameroon','Comoros',\n",
        "             'Congo, Democratic Republic of the','Congo, Republic of the','Côte d’Ivoire',\n",
        "            'Eritrea','Ethiopia','Gabon','Ghana','Guinea','Guinea-Bissau','Kenya','Lesotho',\n",
        "             'Madagascar','Malawi','Mali','Mauritius','Mauritania','Mozambique','Namibia','Niger',\n",
        "             'Nigeria','Rwanda','Sao Tome and Principe','Senegal','Seychelles','Somalia','South Africa',\n",
        "             'Sudan','Sudan, South','The Gambia','Tanzania','Uganda','Zambia','Zimbabwe']\n",
        "\n",
        "\n",
        "latin_america_caribbean = ['Antigua and Barbuda', 'Argentina', 'Aruba', 'Bahamas', 'The Bahamas', 'Barbados', 'The Barbados', \n",
        "                           'Belize', 'Bolivia', 'Brazil', 'British Virgin Islands', 'Cayman Islands', 'Chile', 'Colombia', 'Costa Rica',\n",
        "                                    'Cuba', 'Curacao', 'Dominica', 'Dominican Republic', 'Ecuador', 'El Salvador', \n",
        "                                    'Grenada', 'Guatemala', 'Guyana', 'Haiti', 'Honduras', 'Jamaica', 'Mexico', 'Nicaragua',\n",
        "                                    'Panama', 'Paraguay', 'Peru', 'Puerto Rico', 'Sint Maarten', 'Saint Lucia', 'St. Martin', \n",
        "                                    'Saint Vincent and the Grenadines', 'Suriname', 'Trinidad and Tobago', 'Turks and Caicos Islands', \n",
        "                                    'Uruguay', 'Venezuela', 'Virgin Islands']\n",
        "north_america=['Bermuda', \"United States\", 'Canada']\n",
        "middle_east_north_africa=['Algeria', 'Bahrain', 'Djibouti', 'Egypt','Iran','Iraq','Israel','Jordan','Kuwait','Lebanon','Libya','Malta',\n",
        "                         'Morocco','Oman','Qatar','Saudi Arabia','Tunisia','United Arab Emirates']\n",
        "#country_array=np.concatenate((east_asia_pacific,europe_central_asia,south_asia,sub_saharan,\n",
        "#             latin_america_caribbean,north_america,middle_east_north_africa))\n",
        "#country_list=[east_asia_pacific,europe_central_asia,south_asia,sub_saharan,\n",
        "#             latin_america_caribbean,north_america,middle_east_north_africa]\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BOIMzCYPpLVW",
        "outputId": "5d785b27-5c9e-4259-ca0f-65504651862c"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pTYg0RcJd5cE"
      },
      "source": [
        "data=pd.read_csv(\"/content/drive/MyDrive/DS4A-Team10/COVIDistress_data_with_region.csv\",engine='python')\n",
        "data['Country Income Level']=data['Country Income Level'].replace(['Low income'],'Low Income').replace(['High income'],'High Income')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "feNNmQkTd-un",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 746
        },
        "outputId": "f30579cc-406b-4985-c960-e1b66a129393"
      },
      "source": [
        "data.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ID</th>\n",
              "      <th>answered_all</th>\n",
              "      <th>Duration..in.seconds.</th>\n",
              "      <th>RecordedDate</th>\n",
              "      <th>UserLanguage</th>\n",
              "      <th>Dem_age</th>\n",
              "      <th>Dem_gender</th>\n",
              "      <th>Dem_edu</th>\n",
              "      <th>Dem_edu_mom</th>\n",
              "      <th>Dem_employment</th>\n",
              "      <th>Country</th>\n",
              "      <th>Country Income Level</th>\n",
              "      <th>Region</th>\n",
              "      <th>Dem_Expat</th>\n",
              "      <th>Dem_state</th>\n",
              "      <th>Dem_maritalstatus</th>\n",
              "      <th>Dem_dependents</th>\n",
              "      <th>Dem_riskgroup</th>\n",
              "      <th>Dem_isolation</th>\n",
              "      <th>Dem_isolation_adults</th>\n",
              "      <th>Dem_isolation_kids</th>\n",
              "      <th>AD_gain</th>\n",
              "      <th>AD_loss</th>\n",
              "      <th>AD_check</th>\n",
              "      <th>Scale_PSS10_UCLA_1</th>\n",
              "      <th>Scale_PSS10_UCLA_2</th>\n",
              "      <th>Scale_PSS10_UCLA_3</th>\n",
              "      <th>Scale_PSS10_UCLA_4</th>\n",
              "      <th>Scale_PSS10_UCLA_5</th>\n",
              "      <th>Scale_PSS10_UCLA_6</th>\n",
              "      <th>Scale_PSS10_UCLA_7</th>\n",
              "      <th>Scale_PSS10_UCLA_8</th>\n",
              "      <th>Scale_PSS10_UCLA_9</th>\n",
              "      <th>Scale_PSS10_UCLA_10</th>\n",
              "      <th>Scale_SLON_1</th>\n",
              "      <th>Scale_SLON_2</th>\n",
              "      <th>Scale_SLON_3</th>\n",
              "      <th>OECD_people_1</th>\n",
              "      <th>OECD_people_2</th>\n",
              "      <th>OECD_insititutions_1</th>\n",
              "      <th>...</th>\n",
              "      <th>SPS_4</th>\n",
              "      <th>SPS_5</th>\n",
              "      <th>SPS_6</th>\n",
              "      <th>SPS_7</th>\n",
              "      <th>SPS_8</th>\n",
              "      <th>SPS_9</th>\n",
              "      <th>SPS_10</th>\n",
              "      <th>Expl_Coping_1</th>\n",
              "      <th>Expl_Coping_2</th>\n",
              "      <th>Expl_Coping_3</th>\n",
              "      <th>Expl_Coping_4</th>\n",
              "      <th>Expl_Coping_5</th>\n",
              "      <th>Expl_Coping_6</th>\n",
              "      <th>Expl_Coping_7</th>\n",
              "      <th>Expl_Coping_8</th>\n",
              "      <th>Expl_Coping_9</th>\n",
              "      <th>Expl_Coping_10</th>\n",
              "      <th>Expl_Coping_11</th>\n",
              "      <th>Expl_Coping_12</th>\n",
              "      <th>Expl_Coping_13</th>\n",
              "      <th>Expl_Coping_14</th>\n",
              "      <th>Expl_Coping_15</th>\n",
              "      <th>Expl_Coping_16</th>\n",
              "      <th>Expl_coping_txt</th>\n",
              "      <th>Expl_media_1</th>\n",
              "      <th>Expl_media_2</th>\n",
              "      <th>Expl_media_3</th>\n",
              "      <th>Expl_media_4</th>\n",
              "      <th>Expl_media_5</th>\n",
              "      <th>Expl_media_6</th>\n",
              "      <th>Final_open</th>\n",
              "      <th>PSS10_avg</th>\n",
              "      <th>SLON3_avg</th>\n",
              "      <th>neu</th>\n",
              "      <th>ext</th>\n",
              "      <th>ope</th>\n",
              "      <th>agr</th>\n",
              "      <th>con</th>\n",
              "      <th>SPS_avg</th>\n",
              "      <th>Scale_UCLA_TRI_avg</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>4069</td>\n",
              "      <td>No</td>\n",
              "      <td>856</td>\n",
              "      <td>5/12/20 1:34</td>\n",
              "      <td>FA</td>\n",
              "      <td>30</td>\n",
              "      <td>Male</td>\n",
              "      <td>College degree, bachelor, master</td>\n",
              "      <td>Up to 6 years of school</td>\n",
              "      <td>Full time employed</td>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>Low Income</td>\n",
              "      <td>South Asia</td>\n",
              "      <td>yes</td>\n",
              "      <td>&lt;U+06A9&gt;&lt;U+0627&gt;&lt;U+0628&gt;&lt;U+0644&gt;</td>\n",
              "      <td>Married/cohabiting</td>\n",
              "      <td>3.0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Life carries on with minor changes</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>· If Program D is adopted there is 1/3 probabi...</td>\n",
              "      <td>No</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.2</td>\n",
              "      <td>1.333333</td>\n",
              "      <td>2.333333</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>6.0</td>\n",
              "      <td>4.666667</td>\n",
              "      <td>5.666667</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4460</td>\n",
              "      <td>No</td>\n",
              "      <td>89</td>\n",
              "      <td>5/12/20 0:28</td>\n",
              "      <td>KO</td>\n",
              "      <td>26</td>\n",
              "      <td>Other/would rather not say</td>\n",
              "      <td>PhD/Doctorate</td>\n",
              "      <td>PhD/Doctorate</td>\n",
              "      <td>Student</td>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>Low Income</td>\n",
              "      <td>South Asia</td>\n",
              "      <td>yes</td>\n",
              "      <td>&lt;U+3147&gt;&lt;U+314E&gt;</td>\n",
              "      <td>Other or would rather not say</td>\n",
              "      <td>0.0</td>\n",
              "      <td>No</td>\n",
              "      <td>Life carries on as usual</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>· If Program C is adopted 400 people will die.</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>...</td>\n",
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              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>9383</td>\n",
              "      <td>No</td>\n",
              "      <td>305</td>\n",
              "      <td>4/30/20 0:53</td>\n",
              "      <td>EN</td>\n",
              "      <td>26</td>\n",
              "      <td>Male</td>\n",
              "      <td>PhD/Doctorate</td>\n",
              "      <td>College degree</td>\n",
              "      <td>Full time employed</td>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>Low Income</td>\n",
              "      <td>South Asia</td>\n",
              "      <td>yes</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Isolated</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>· If Program B is adopted, there is 1/3 probab...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>10249</td>\n",
              "      <td>No</td>\n",
              "      <td>48</td>\n",
              "      <td>4/27/20 7:32</td>\n",
              "      <td>HR</td>\n",
              "      <td>18</td>\n",
              "      <td>Female</td>\n",
              "      <td>College degree, bachelor, master</td>\n",
              "      <td>PhD/Doctorate</td>\n",
              "      <td>Full time employed</td>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>Low Income</td>\n",
              "      <td>South Asia</td>\n",
              "      <td>yes</td>\n",
              "      <td>Grad Zagreb</td>\n",
              "      <td>Single</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Life carries on with minor changes</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>12234</td>\n",
              "      <td>Yes</td>\n",
              "      <td>4443</td>\n",
              "      <td>4/20/20 19:46</td>\n",
              "      <td>FA</td>\n",
              "      <td>22</td>\n",
              "      <td>Female</td>\n",
              "      <td>Some College, short continuing education or eq...</td>\n",
              "      <td>None</td>\n",
              "      <td>Student</td>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>Low Income</td>\n",
              "      <td>South Asia</td>\n",
              "      <td>yes</td>\n",
              "      <td>&lt;U+06A9&gt;&lt;U+0627&gt;&lt;U+0628&gt;&lt;U+0644&gt;</td>\n",
              "      <td>Single</td>\n",
              "      <td>0.0</td>\n",
              "      <td>No</td>\n",
              "      <td>Life carries on with minor changes</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>· If Program A is adopted, 200 people will be ...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Yes</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>...</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>&lt;U+0627&gt;&lt;U+06CC&gt;&lt;U+0646&gt; &lt;U+0648&gt;&lt;U+06CC&gt;&lt;U+06...</td>\n",
              "      <td>2.8</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.666667</td>\n",
              "      <td>5.666667</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.666667</td>\n",
              "      <td>5.666667</td>\n",
              "      <td>4.6</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 156 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      ID answered_all  ...  SPS_avg Scale_UCLA_TRI_avg\n",
              "0   4069           No  ...      NaN                NaN\n",
              "1   4460           No  ...      NaN                NaN\n",
              "2   9383           No  ...      NaN                NaN\n",
              "3  10249           No  ...      NaN                NaN\n",
              "4  12234          Yes  ...      4.6                NaN\n",
              "\n",
              "[5 rows x 156 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xZ2w-3kzpbCZ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450
        },
        "outputId": "e2f5eea2-b6eb-4106-f887-608647d6ce5d"
      },
      "source": [
        "data.groupby([\"Region\",\"Country\"]).count().sort_values(\"ID\",ascending=False)[\"ID\"].to_frame()\n"
      ],
      "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></th>\n",
              "      <th>ID</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Region</th>\n",
              "      <th>Country</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">Europe and Central Asia</th>\n",
              "      <th>Finland</th>\n",
              "      <td>22933</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>France</th>\n",
              "      <td>13475</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Denmark</th>\n",
              "      <td>10891</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Latin America and Caribbean</th>\n",
              "      <th>Mexico</th>\n",
              "      <td>9169</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Europe and Central Asia</th>\n",
              "      <th>Lithuania</th>\n",
              "      <td>8255</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Middle East and North Africa</th>\n",
              "      <th>Libya</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sub-Saharan Africa</th>\n",
              "      <th>Eritrea</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>East Asia and Pacific</th>\n",
              "      <th>East Timor (Timor-Leste)</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sub-Saharan Africa</th>\n",
              "      <th>Congo, Democratic Republic of the</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Europe and Central Asia</th>\n",
              "      <th>Tajikistan</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>177 rows × 1 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                                   ID\n",
              "Region                       Country                                 \n",
              "Europe and Central Asia      Finland                            22933\n",
              "                             France                             13475\n",
              "                             Denmark                            10891\n",
              "Latin America and Caribbean  Mexico                              9169\n",
              "Europe and Central Asia      Lithuania                           8255\n",
              "...                                                               ...\n",
              "Middle East and North Africa Libya                                  1\n",
              "Sub-Saharan Africa           Eritrea                                1\n",
              "East Asia and Pacific        East Timor (Timor-Leste)               1\n",
              "Sub-Saharan Africa           Congo, Democratic Republic of the      1\n",
              "Europe and Central Asia      Tajikistan                             1\n",
              "\n",
              "[177 rows x 1 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uvF6i04OgMRX",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 793
        },
        "outputId": "41d00510-2d70-43fa-857c-4f35b70f47e5"
      },
      "source": [
        "data.sort_values(['Country Income Level']).groupby([\"Region\",'Country Income Level']).count()[\"ID\"].to_frame()"
      ],
      "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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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>ID</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Region</th>\n",
              "      <th>Country Income Level</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"4\" valign=\"top\">East Asia and Pacific</th>\n",
              "      <th>High Income</th>\n",
              "      <td>5727</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Low Income</th>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Lower Middle Income</th>\n",
              "      <td>2326</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Upper Middle Income</th>\n",
              "      <td>3732</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"4\" valign=\"top\">Europe and Central Asia</th>\n",
              "      <th>High Income</th>\n",
              "      <td>80287</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Low Income</th>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Lower Middle Income</th>\n",
              "      <td>202</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Upper Middle Income</th>\n",
              "      <td>10729</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"4\" valign=\"top\">Latin America and Caribbean</th>\n",
              "      <th>High Income</th>\n",
              "      <td>1330</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Lower Middle Income</th>\n",
              "      <td>457</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Not Classified</th>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Upper Middle Income</th>\n",
              "      <td>15322</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">Middle East and North Africa</th>\n",
              "      <th>High Income</th>\n",
              "      <td>228</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Lower Middle Income</th>\n",
              "      <td>108</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Upper Middle Income</th>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>North America</th>\n",
              "      <th>High Income</th>\n",
              "      <td>2784</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">South Asia</th>\n",
              "      <th>Low Income</th>\n",
              "      <td>114</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Lower Middle Income</th>\n",
              "      <td>864</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Upper Middle Income</th>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"4\" valign=\"top\">Sub-Saharan Africa</th>\n",
              "      <th>High Income</th>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Low Income</th>\n",
              "      <td>40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Lower Middle Income</th>\n",
              "      <td>81</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Upper Middle Income</th>\n",
              "      <td>141</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                      ID\n",
              "Region                       Country Income Level       \n",
              "East Asia and Pacific        High Income            5727\n",
              "                             Low Income                2\n",
              "                             Lower Middle Income    2326\n",
              "                             Upper Middle Income    3732\n",
              "Europe and Central Asia      High Income           80287\n",
              "                             Low Income                9\n",
              "                             Lower Middle Income     202\n",
              "                             Upper Middle Income   10729\n",
              "Latin America and Caribbean  High Income            1330\n",
              "                             Lower Middle Income     457\n",
              "                             Not Classified           11\n",
              "                             Upper Middle Income   15322\n",
              "Middle East and North Africa High Income             228\n",
              "                             Lower Middle Income     108\n",
              "                             Upper Middle Income      20\n",
              "North America                High Income            2784\n",
              "South Asia                   Low Income              114\n",
              "                             Lower Middle Income     864\n",
              "                             Upper Middle Income       4\n",
              "Sub-Saharan Africa           High Income              20\n",
              "                             Low Income               40\n",
              "                             Lower Middle Income      81\n",
              "                             Upper Middle Income     141"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8PFf7Hs3g7NU",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "efef3ec5-a84e-4cb9-b535-7e77e878024c"
      },
      "source": [
        "data.pivot_table(values='ID',columns='Country Income Level',index=\"Region\",aggfunc= len)[[\"High Income\",\"Upper Middle Income\",\"Lower Middle Income\",\"Low Income\",\"Not Classified\"]]"
      ],
      "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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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>Country Income Level</th>\n",
              "      <th>High Income</th>\n",
              "      <th>Upper Middle Income</th>\n",
              "      <th>Lower Middle Income</th>\n",
              "      <th>Low Income</th>\n",
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              "    <tr>\n",
              "      <th>Region</th>\n",
              "      <th></th>\n",
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              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>East Asia and Pacific</th>\n",
              "      <td>5727.0</td>\n",
              "      <td>3732.0</td>\n",
              "      <td>2326.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Europe and Central Asia</th>\n",
              "      <td>80287.0</td>\n",
              "      <td>10729.0</td>\n",
              "      <td>202.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Latin America and Caribbean</th>\n",
              "      <td>1330.0</td>\n",
              "      <td>15322.0</td>\n",
              "      <td>457.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>11.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Middle East and North Africa</th>\n",
              "      <td>228.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>108.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>North America</th>\n",
              "      <td>2784.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>South Asia</th>\n",
              "      <td>NaN</td>\n",
              "      <td>4.0</td>\n",
              "      <td>864.0</td>\n",
              "      <td>114.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sub-Saharan Africa</th>\n",
              "      <td>20.0</td>\n",
              "      <td>141.0</td>\n",
              "      <td>81.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Country Income Level          High Income  ...  Not Classified\n",
              "Region                                     ...                \n",
              "East Asia and Pacific              5727.0  ...             NaN\n",
              "Europe and Central Asia           80287.0  ...             NaN\n",
              "Latin America and Caribbean        1330.0  ...            11.0\n",
              "Middle East and North Africa        228.0  ...             NaN\n",
              "North America                      2784.0  ...             NaN\n",
              "South Asia                            NaN  ...             NaN\n",
              "Sub-Saharan Africa                   20.0  ...             NaN\n",
              "\n",
              "[7 rows x 5 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ST4zgGg4iR2A",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 390
        },
        "outputId": "69604c12-b0e9-4a96-b346-3c014e0c7d07"
      },
      "source": [
        "data[[\"Country\",\"Region\",'Country Income Level']].loc[data['Country Income Level']=='Not Classified']"
      ],
      "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>Country</th>\n",
              "      <th>Region</th>\n",
              "      <th>Country Income Level</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>125114</th>\n",
              "      <td>Venezuela</td>\n",
              "      <td>Latin America and Caribbean</td>\n",
              "      <td>Not Classified</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>125115</th>\n",
              "      <td>Venezuela</td>\n",
              "      <td>Latin America and Caribbean</td>\n",
              "      <td>Not Classified</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>125116</th>\n",
              "      <td>Venezuela</td>\n",
              "      <td>Latin America and Caribbean</td>\n",
              "      <td>Not Classified</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>125117</th>\n",
              "      <td>Venezuela</td>\n",
              "      <td>Latin America and Caribbean</td>\n",
              "      <td>Not Classified</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>125118</th>\n",
              "      <td>Venezuela</td>\n",
              "      <td>Latin America and Caribbean</td>\n",
              "      <td>Not Classified</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>125119</th>\n",
              "      <td>Venezuela</td>\n",
              "      <td>Latin America and Caribbean</td>\n",
              "      <td>Not Classified</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>125120</th>\n",
              "      <td>Venezuela</td>\n",
              "      <td>Latin America and Caribbean</td>\n",
              "      <td>Not Classified</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>125121</th>\n",
              "      <td>Venezuela</td>\n",
              "      <td>Latin America and Caribbean</td>\n",
              "      <td>Not Classified</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>125122</th>\n",
              "      <td>Venezuela</td>\n",
              "      <td>Latin America and Caribbean</td>\n",
              "      <td>Not Classified</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>125123</th>\n",
              "      <td>Venezuela</td>\n",
              "      <td>Latin America and Caribbean</td>\n",
              "      <td>Not Classified</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>125124</th>\n",
              "      <td>Venezuela</td>\n",
              "      <td>Latin America and Caribbean</td>\n",
              "      <td>Not Classified</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          Country                       Region Country Income Level\n",
              "125114  Venezuela  Latin America and Caribbean       Not Classified\n",
              "125115  Venezuela  Latin America and Caribbean       Not Classified\n",
              "125116  Venezuela  Latin America and Caribbean       Not Classified\n",
              "125117  Venezuela  Latin America and Caribbean       Not Classified\n",
              "125118  Venezuela  Latin America and Caribbean       Not Classified\n",
              "125119  Venezuela  Latin America and Caribbean       Not Classified\n",
              "125120  Venezuela  Latin America and Caribbean       Not Classified\n",
              "125121  Venezuela  Latin America and Caribbean       Not Classified\n",
              "125122  Venezuela  Latin America and Caribbean       Not Classified\n",
              "125123  Venezuela  Latin America and Caribbean       Not Classified\n",
              "125124  Venezuela  Latin America and Caribbean       Not Classified"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "04KM-LhytzOb"
      },
      "source": [
        "# From google: Venezuela, previously classified as an upper-middle income country, is now unclassified due to a lack of available data in the recent period. "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "laOriIqZZiuZ"
      },
      "source": [
        "## Creating Food Insecurity DataFrame - FI dataframe"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9k3KoW1Xa4cS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d11d13e0-85c2-498b-ce9f-d5f97681d6f9"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lPUA88cSaOaV"
      },
      "source": [
        "import pandas as pd"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X2XKMCaWf9Oe"
      },
      "source": [
        "# path_csv_FI = '/content/drive/MyDrive/DS4A_women/Country_Food_Insecurity.csv'\n",
        "path_csv_FI = \"/content/drive/MyDrive/DS4A-Team10/Country_Food_Insecurity.csv\""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N1v5x_WFlBYm",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 400
        },
        "outputId": "544f40d8-6f88-4fef-a023-d26fb855a1c1"
      },
      "source": [
        "FI = pd.read_csv(path_csv_FI,header = 4, engine='python')\n",
        "print(FI.shape)\n",
        "FI.head(3)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(266, 65)\n"
          ]
        },
        {
          "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 Name</th>\n",
              "      <th>Country Code</th>\n",
              "      <th>Indicator Name</th>\n",
              "      <th>Indicator Code</th>\n",
              "      <th>1960</th>\n",
              "      <th>1961</th>\n",
              "      <th>1962</th>\n",
              "      <th>1963</th>\n",
              "      <th>1964</th>\n",
              "      <th>1965</th>\n",
              "      <th>1966</th>\n",
              "      <th>1967</th>\n",
              "      <th>1968</th>\n",
              "      <th>1969</th>\n",
              "      <th>1970</th>\n",
              "      <th>1971</th>\n",
              "      <th>1972</th>\n",
              "      <th>1973</th>\n",
              "      <th>1974</th>\n",
              "      <th>1975</th>\n",
              "      <th>1976</th>\n",
              "      <th>1977</th>\n",
              "      <th>1978</th>\n",
              "      <th>1979</th>\n",
              "      <th>1980</th>\n",
              "      <th>1981</th>\n",
              "      <th>1982</th>\n",
              "      <th>1983</th>\n",
              "      <th>1984</th>\n",
              "      <th>1985</th>\n",
              "      <th>1986</th>\n",
              "      <th>1987</th>\n",
              "      <th>1988</th>\n",
              "      <th>1989</th>\n",
              "      <th>1990</th>\n",
              "      <th>1991</th>\n",
              "      <th>1992</th>\n",
              "      <th>1993</th>\n",
              "      <th>1994</th>\n",
              "      <th>1995</th>\n",
              "      <th>1996</th>\n",
              "      <th>1997</th>\n",
              "      <th>1998</th>\n",
              "      <th>1999</th>\n",
              "      <th>2000</th>\n",
              "      <th>2001</th>\n",
              "      <th>2002</th>\n",
              "      <th>2003</th>\n",
              "      <th>2004</th>\n",
              "      <th>2005</th>\n",
              "      <th>2006</th>\n",
              "      <th>2007</th>\n",
              "      <th>2008</th>\n",
              "      <th>2009</th>\n",
              "      <th>2010</th>\n",
              "      <th>2011</th>\n",
              "      <th>2012</th>\n",
              "      <th>2013</th>\n",
              "      <th>2014</th>\n",
              "      <th>2015</th>\n",
              "      <th>2016</th>\n",
              "      <th>2017</th>\n",
              "      <th>2018</th>\n",
              "      <th>2019</th>\n",
              "      <th>2020</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Aruba</td>\n",
              "      <td>ABW</td>\n",
              "      <td>Prevalence of moderate or severe food insecuri...</td>\n",
              "      <td>SN.ITK.MSFI.ZS</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Africa Eastern and Southern</td>\n",
              "      <td>AFE</td>\n",
              "      <td>Prevalence of moderate or severe food insecuri...</td>\n",
              "      <td>SN.ITK.MSFI.ZS</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>AFG</td>\n",
              "      <td>Prevalence of moderate or severe food insecuri...</td>\n",
              "      <td>SN.ITK.MSFI.ZS</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>45.1</td>\n",
              "      <td>49.6</td>\n",
              "      <td>54.3</td>\n",
              "      <td>57.5</td>\n",
              "      <td>63.1</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  Country Name Country Code  ...  2019 2020\n",
              "0                        Aruba          ABW  ...   NaN  NaN\n",
              "1  Africa Eastern and Southern          AFE  ...   NaN  NaN\n",
              "2                  Afghanistan          AFG  ...  63.1  NaN\n",
              "\n",
              "[3 rows x 65 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y2L9BuPilYw4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dc5752dc-a55b-4711-f0ef-e3224e0c6ede"
      },
      "source": [
        "FI['Indicator Name'].unique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Prevalence of moderate or severe food insecurity in the population (%)'],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y2D-WA6plh-O",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "35afbd8a-eb23-481c-d033-81efcc89bcd9"
      },
      "source": [
        "# Dataset does not show any Food Insecurity value for year 2020\n",
        "FI['2020'].notnull().count()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "266"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "juKiJdYEmn1_",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "outputId": "ce72fad7-6099-4260-e2b7-690a16a25cd4"
      },
      "source": [
        "# Filtering to include latest year value\n",
        "FI = FI.filter(['Country Name','2019'], axis=1)\n",
        "FI.head(3)"
      ],
      "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>Country Name</th>\n",
              "      <th>2019</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Aruba</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Africa Eastern and Southern</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>63.1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  Country Name  2019\n",
              "0                        Aruba   NaN\n",
              "1  Africa Eastern and Southern   NaN\n",
              "2                  Afghanistan  63.1"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "78Hz_d6-m2Uy",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "outputId": "5fdd99bd-a7ee-4ff7-f6c8-590b770c8396"
      },
      "source": [
        "# 'Prevalence of moderate or severe food insecurity in the population (%)'\n",
        "FI.columns = [\"Country Name\", '2019_Prevalence of moderate or severe food insecurity in the population (%)']\n",
        "FI.head(3)"
      ],
      "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>Country Name</th>\n",
              "      <th>2019_Prevalence of moderate or severe food insecurity in the population (%)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Aruba</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Africa Eastern and Southern</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>63.1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  Country Name  2019_Prevalence of moderate or severe food insecurity in the population (%)\n",
              "0                        Aruba                                                NaN                          \n",
              "1  Africa Eastern and Southern                                                NaN                          \n",
              "2                  Afghanistan                                               63.1                          "
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n-Qq0abhoBSH"
      },
      "source": [
        "## Creating Country Poverty Dataframe - PR dataframe"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kEbYrNaOoLUs"
      },
      "source": [
        "# path_csv_PR = '/content/drive/MyDrive/DS4A_women/Country_Poverty_Rates.csv'\n",
        "path_csv_PR = '/content/drive/MyDrive/DS4A-Team10/Country_Poverty_Rates.csv'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xqLCoEGYoDup",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "927c4943-868c-432d-d3e9-7e0f76627bae"
      },
      "source": [
        "PR =pd.read_csv(path_csv_PR, header = 4)\n",
        "PR.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(7714, 65)"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B05oKi8DodqK",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7fd9e515-ea10-4b9a-c1f8-eb7735f8a93e"
      },
      "source": [
        "PR['Indicator Name'].unique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Annualized average growth rate in per capita real survey mean consumption or income, total population (%)',\n",
              "       'Survey mean consumption or income per capita, total population (2011 PPP $ per day)',\n",
              "       'Annualized average growth rate in per capita real survey mean consumption or income, bottom 40% of population (%)',\n",
              "       'Survey mean consumption or income per capita, bottom 40% of population (2011 PPP $ per day)',\n",
              "       'Poverty gap at $5.50 a day (2011 PPP) (%)',\n",
              "       'Poverty headcount ratio at $5.50 a day (2011 PPP) (% of population)',\n",
              "       'Poverty headcount ratio at national poverty lines (% of population)',\n",
              "       'Multidimensional poverty index (scale 0-1)',\n",
              "       'Multidimensional poverty headcount ratio, male (% of male population)',\n",
              "       'Multidimensional poverty intensity (average share of deprivations experienced by the poor)',\n",
              "       'Multidimensional poverty headcount ratio, household (% of total households)',\n",
              "       'Multidimensional poverty headcount ratio, female (% of female population)',\n",
              "       'Multidimensional poverty index, children (population ages 0-17) (scale 0-1)',\n",
              "       'Multidimensional poverty headcount ratio, children (% of population ages 0-17)',\n",
              "       'Multidimensional poverty headcount ratio (% of total population)',\n",
              "       'Poverty gap at $3.20 a day (2011 PPP) (%)',\n",
              "       'Poverty headcount ratio at $3.20 a day (2011 PPP) (% of population)',\n",
              "       'Gini index (World Bank estimate)',\n",
              "       'Poverty gap at $1.90 a day (2011 PPP) (%)',\n",
              "       'Poverty headcount ratio at $1.90 a day (2011 PPP) (% of population)',\n",
              "       'Income share held by lowest 20%',\n",
              "       'Income share held by lowest 10%',\n",
              "       'Proportion of people living below 50 percent of median income (%)',\n",
              "       'Income share held by highest 10%',\n",
              "       'Income share held by highest 20%',\n",
              "       'Income share held by fourth 20%',\n",
              "       'Income share held by third 20%',\n",
              "       'Income share held by second 20%',\n",
              "       'Population living in slums (% of urban population)'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JyNV5HFpokmC"
      },
      "source": [
        "# Selecting the indicator tu use for analysis: \n",
        "# 'Poverty headcount ratio at national poverty lines (% of population)' - using Indicator https://data.worldbank.org/indicator/SI.POV.NAHC?locations=CO&view=map&year_high_desc=true\n",
        "PR = PR[(PR['Indicator Name'] == 'Poverty headcount ratio at national poverty lines (% of population)')][['Country Name','2017','2018','2019','2020']]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HJzC69ufopC4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 159
        },
        "outputId": "ee8d3d6a-4dc3-48a4-aff4-0f435dff39f1"
      },
      "source": [
        "print(PR.shape)\n",
        "PR.head(3)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(266, 5)\n"
          ]
        },
        {
          "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 Name</th>\n",
              "      <th>2017</th>\n",
              "      <th>2018</th>\n",
              "      <th>2019</th>\n",
              "      <th>2020</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Aruba</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>Africa Eastern and Southern</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>64</th>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                   Country Name  2017  2018  2019  2020\n",
              "6                         Aruba   NaN   NaN   NaN   NaN\n",
              "35  Africa Eastern and Southern   NaN   NaN   NaN   NaN\n",
              "64                  Afghanistan   NaN   NaN   NaN   NaN"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0air-fmYotMU"
      },
      "source": [
        "# Setting index to country name to facilitate selection of most recent year value from dataset\n",
        "PR = PR.set_index('Country Name')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9OeKbt69oxqb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "outputId": "1f4c385a-23b1-498a-87b0-f7dcd351e73f"
      },
      "source": [
        "PR.head(3)"
      ],
      "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>2017</th>\n",
              "      <th>2018</th>\n",
              "      <th>2019</th>\n",
              "      <th>2020</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Country Name</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Aruba</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Africa Eastern and Southern</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Afghanistan</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                             2017  2018  2019  2020\n",
              "Country Name                                       \n",
              "Aruba                         NaN   NaN   NaN   NaN\n",
              "Africa Eastern and Southern   NaN   NaN   NaN   NaN\n",
              "Afghanistan                   NaN   NaN   NaN   NaN"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5IeD3hWWo0M9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 207
        },
        "outputId": "7af890e6-4be5-4bbf-db69-6fa5fb0d53ab"
      },
      "source": [
        "# using last non nan value from the years between (2017-2020)\n",
        "PR['Most recent year value (2017-2020)_Poverty headcount ratio at national poverty lines (% of population)'] = PR.ffill(axis=1).iloc[:, -1]\n",
        "PR.head(3)"
      ],
      "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>2017</th>\n",
              "      <th>2018</th>\n",
              "      <th>2019</th>\n",
              "      <th>2020</th>\n",
              "      <th>Most recent year value (2017-2020)_Poverty headcount ratio at national poverty lines (% of population)</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Country Name</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>Aruba</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Africa Eastern and Southern</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Afghanistan</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                             2017  ...  Most recent year value (2017-2020)_Poverty headcount ratio at national poverty lines (% of population)\n",
              "Country Name                       ...                                                                                                        \n",
              "Aruba                         NaN  ...                                                NaN                                                     \n",
              "Africa Eastern and Southern   NaN  ...                                                NaN                                                     \n",
              "Afghanistan                   NaN  ...                                                NaN                                                     \n",
              "\n",
              "[3 rows x 5 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xHy8ykrYo4Eq",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "outputId": "12531e36-d8c6-4ec2-9fd1-6ccbb431deac"
      },
      "source": [
        "# Resetting index\n",
        "PR = PR.reset_index()\n",
        "PR.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",
              "        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 Name</th>\n",
              "      <th>2017</th>\n",
              "      <th>2018</th>\n",
              "      <th>2019</th>\n",
              "      <th>2020</th>\n",
              "      <th>Most recent year value (2017-2020)_Poverty headcount ratio at national poverty lines (% of population)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Aruba</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Africa Eastern and Southern</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Africa Western and Central</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Angola</td>\n",
              "      <td>NaN</td>\n",
              "      <td>32.3</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>32.3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  Country Name  ...  Most recent year value (2017-2020)_Poverty headcount ratio at national poverty lines (% of population)\n",
              "0                        Aruba  ...                                                NaN                                                     \n",
              "1  Africa Eastern and Southern  ...                                                NaN                                                     \n",
              "2                  Afghanistan  ...                                                NaN                                                     \n",
              "3   Africa Western and Central  ...                                                NaN                                                     \n",
              "4                       Angola  ...                                               32.3                                                     \n",
              "\n",
              "[5 rows x 6 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ki_8QJGMo95K",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "outputId": "3a79c4b9-cacd-4916-98bc-5225583adeec"
      },
      "source": [
        "# Selecting only columns to be used in final dataframe (to merge with regions data)\n",
        "PR = PR.filter(['Country Name', 'Most recent year value (2017-2020)_Poverty headcount ratio at national poverty lines (% of population)'], axis=1)\n",
        "PR.head(3)"
      ],
      "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>Country Name</th>\n",
              "      <th>Most recent year value (2017-2020)_Poverty headcount ratio at national poverty lines (% of population)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Aruba</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Africa Eastern and Southern</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  Country Name  Most recent year value (2017-2020)_Poverty headcount ratio at national poverty lines (% of population)\n",
              "0                        Aruba                                                NaN                                                     \n",
              "1  Africa Eastern and Southern                                                NaN                                                     \n",
              "2                  Afghanistan                                                NaN                                                     "
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l9SnZlNVpDz2"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mkka9EpqpGjb"
      },
      "source": [
        "## Creating Country Maternal Mortality Rate Dataframe -  mmr dataframe\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IOB_OTQ2pL2N"
      },
      "source": [
        "# path_csv_mmr = '/content/drive/MyDrive/DS4A_women/Country_Maternal_Mortality_Rates.csv'\n",
        "path_csv_mmr = '/content/drive/MyDrive/DS4A-Team10/Country Maternal Mortality Rates.csv'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iZlXeKUjpHUJ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 451
        },
        "outputId": "97ecd78c-1022-4643-df3c-7f700e4223f4"
      },
      "source": [
        "mmr = pd.read_csv(path_csv_mmr,header = 4)\n",
        "print(mmr.shape)\n",
        "mmr.head(3)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(266, 65)\n"
          ]
        },
        {
          "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 Name</th>\n",
              "      <th>Country Code</th>\n",
              "      <th>Indicator Name</th>\n",
              "      <th>Indicator Code</th>\n",
              "      <th>1960</th>\n",
              "      <th>1961</th>\n",
              "      <th>1962</th>\n",
              "      <th>1963</th>\n",
              "      <th>1964</th>\n",
              "      <th>1965</th>\n",
              "      <th>1966</th>\n",
              "      <th>1967</th>\n",
              "      <th>1968</th>\n",
              "      <th>1969</th>\n",
              "      <th>1970</th>\n",
              "      <th>1971</th>\n",
              "      <th>1972</th>\n",
              "      <th>1973</th>\n",
              "      <th>1974</th>\n",
              "      <th>1975</th>\n",
              "      <th>1976</th>\n",
              "      <th>1977</th>\n",
              "      <th>1978</th>\n",
              "      <th>1979</th>\n",
              "      <th>1980</th>\n",
              "      <th>1981</th>\n",
              "      <th>1982</th>\n",
              "      <th>1983</th>\n",
              "      <th>1984</th>\n",
              "      <th>1985</th>\n",
              "      <th>1986</th>\n",
              "      <th>1987</th>\n",
              "      <th>1988</th>\n",
              "      <th>1989</th>\n",
              "      <th>1990</th>\n",
              "      <th>1991</th>\n",
              "      <th>1992</th>\n",
              "      <th>1993</th>\n",
              "      <th>1994</th>\n",
              "      <th>1995</th>\n",
              "      <th>1996</th>\n",
              "      <th>1997</th>\n",
              "      <th>1998</th>\n",
              "      <th>1999</th>\n",
              "      <th>2000</th>\n",
              "      <th>2001</th>\n",
              "      <th>2002</th>\n",
              "      <th>2003</th>\n",
              "      <th>2004</th>\n",
              "      <th>2005</th>\n",
              "      <th>2006</th>\n",
              "      <th>2007</th>\n",
              "      <th>2008</th>\n",
              "      <th>2009</th>\n",
              "      <th>2010</th>\n",
              "      <th>2011</th>\n",
              "      <th>2012</th>\n",
              "      <th>2013</th>\n",
              "      <th>2014</th>\n",
              "      <th>2015</th>\n",
              "      <th>2016</th>\n",
              "      <th>2017</th>\n",
              "      <th>2018</th>\n",
              "      <th>2019</th>\n",
              "      <th>2020</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Aruba</td>\n",
              "      <td>ABW</td>\n",
              "      <td>Maternal mortality ratio (modeled estimate, pe...</td>\n",
              "      <td>SH.STA.MMRT</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Africa Eastern and Southern</td>\n",
              "      <td>AFE</td>\n",
              "      <td>Maternal mortality ratio (modeled estimate, pe...</td>\n",
              "      <td>SH.STA.MMRT</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>777.0</td>\n",
              "      <td>751.0</td>\n",
              "      <td>732.0</td>\n",
              "      <td>710.0</td>\n",
              "      <td>680.0</td>\n",
              "      <td>643.0</td>\n",
              "      <td>608.0</td>\n",
              "      <td>577.0</td>\n",
              "      <td>551.0</td>\n",
              "      <td>526.0</td>\n",
              "      <td>501.0</td>\n",
              "      <td>480.0</td>\n",
              "      <td>462.0</td>\n",
              "      <td>446.0</td>\n",
              "      <td>433.0</td>\n",
              "      <td>419.0</td>\n",
              "      <td>408.0</td>\n",
              "      <td>398.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>AFG</td>\n",
              "      <td>Maternal mortality ratio (modeled estimate, pe...</td>\n",
              "      <td>SH.STA.MMRT</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1450.0</td>\n",
              "      <td>1390.0</td>\n",
              "      <td>1300.0</td>\n",
              "      <td>1240.0</td>\n",
              "      <td>1180.0</td>\n",
              "      <td>1140.0</td>\n",
              "      <td>1120.0</td>\n",
              "      <td>1090.0</td>\n",
              "      <td>1030.0</td>\n",
              "      <td>993.0</td>\n",
              "      <td>954.0</td>\n",
              "      <td>905.0</td>\n",
              "      <td>858.0</td>\n",
              "      <td>810.0</td>\n",
              "      <td>786.0</td>\n",
              "      <td>701.0</td>\n",
              "      <td>673.0</td>\n",
              "      <td>638.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  Country Name Country Code  ... 2019 2020\n",
              "0                        Aruba          ABW  ...  NaN  NaN\n",
              "1  Africa Eastern and Southern          AFE  ...  NaN  NaN\n",
              "2                  Afghanistan          AFG  ...  NaN  NaN\n",
              "\n",
              "[3 rows x 65 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0EGhjmAdpbbt",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cc8eb06c-c774-4b73-df83-49e5f3b11816"
      },
      "source": [
        "mmr.columns"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Country Name', 'Country Code', 'Indicator Name', 'Indicator Code',\n",
              "       '1960', '1961', '1962', '1963', '1964', '1965', '1966', '1967', '1968',\n",
              "       '1969', '1970', '1971', '1972', '1973', '1974', '1975', '1976', '1977',\n",
              "       '1978', '1979', '1980', '1981', '1982', '1983', '1984', '1985', '1986',\n",
              "       '1987', '1988', '1989', '1990', '1991', '1992', '1993', '1994', '1995',\n",
              "       '1996', '1997', '1998', '1999', '2000', '2001', '2002', '2003', '2004',\n",
              "       '2005', '2006', '2007', '2008', '2009', '2010', '2011', '2012', '2013',\n",
              "       '2014', '2015', '2016', '2017', '2018', '2019', '2020'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lUBpXNWMpkx6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "13173980-aa10-4917-9c70-0f818c811446"
      },
      "source": [
        "# Selecting feature name\n",
        "mmr['Indicator Name'].unique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Maternal mortality ratio (modeled estimate, per 100,000 live births)'],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GiYVJuUHpoIv",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "outputId": "6676043a-9ec3-44ea-f96a-79d5db0a60f2"
      },
      "source": [
        "# latest values are already set to 2017\n",
        "mmr = mmr.filter(['Country Name','2017'], axis=1)\n",
        "mmr.head(3)"
      ],
      "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>Country Name</th>\n",
              "      <th>2017</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Aruba</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Africa Eastern and Southern</td>\n",
              "      <td>398.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>638.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  Country Name   2017\n",
              "0                        Aruba    NaN\n",
              "1  Africa Eastern and Southern  398.0\n",
              "2                  Afghanistan  638.0"
            ]
          },
          "metadata": {},
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zHo6BCWCpsbY",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 159
        },
        "outputId": "ee3d0f33-000e-4b77-93af-6f7826d0f9bb"
      },
      "source": [
        "# Changing Column Name\n",
        "mmr.columns = [\"Country Name\", '2017_Maternal mortality ratio (modeled estimate, per 100,000 live births)']\n",
        "print(mmr.shape)\n",
        "mmr.head(3)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(266, 2)\n"
          ]
        },
        {
          "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 Name</th>\n",
              "      <th>2017_Maternal mortality ratio (modeled estimate, per 100,000 live births)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Aruba</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Africa Eastern and Southern</td>\n",
              "      <td>398.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>638.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  Country Name  2017_Maternal mortality ratio (modeled estimate, per 100,000 live births)\n",
              "0                        Aruba                                                NaN                        \n",
              "1  Africa Eastern and Southern                                              398.0                        \n",
              "2                  Afghanistan                                              638.0                        "
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EeOYmzBIp3nY"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kAZrbUakqDLL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6592ec2c-9320-4a95-bb42-8fb8f2f95591"
      },
      "source": [
        "path_csv_regions = \"/content/drive/MyDrive/DS4A_women/COVIDistress_data_with_region.csv\"\n",
        "path_csv_regions = \"/content/drive/MyDrive/DS4A-Team10/COVIDistress_data_with_fi_pr_mmr.csv\"\n",
        "data = pd.read_csv(path_csv_regions)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/IPython/core/interactiveshell.py:2718: DtypeWarning: Columns (61,62,63,64,65,66,67) have mixed types.Specify dtype option on import or set low_memory=False.\n",
            "  interactivity=interactivity, compiler=compiler, result=result)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9eQ094WopzyO",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "60fc4cbd-bbbd-4e5b-d56f-7d5e6926ab1e"
      },
      "source": [
        "# Checking shape of dataframes\n",
        "print(FI.shape)\n",
        "print(PR.shape)\n",
        "print(mmr.shape)\n",
        "print(data.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(266, 2)\n",
            "(266, 2)\n",
            "(266, 2)\n",
            "(125306, 160)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gnajbrdgqNqc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "outputId": "fcd3f5c6-eaac-499e-e808-c8f174ac528d"
      },
      "source": [
        "# Merging Food Insecurity and Poverty Rate Dataframes\n",
        "FI_PR = pd.merge(FI,PR, how='left', left_on='Country Name', right_on='Country Name' )\n",
        "FI_PR.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",
              "        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 Name</th>\n",
              "      <th>2019_Prevalence of moderate or severe food insecurity in the population (%)</th>\n",
              "      <th>Most recent year value (2017-2020)_Poverty headcount ratio at national poverty lines (% of population)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Aruba</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Africa Eastern and Southern</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>63.1</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Africa Western and Central</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Angola</td>\n",
              "      <td>73.5</td>\n",
              "      <td>32.3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  Country Name  ...  Most recent year value (2017-2020)_Poverty headcount ratio at national poverty lines (% of population)\n",
              "0                        Aruba  ...                                                NaN                                                     \n",
              "1  Africa Eastern and Southern  ...                                                NaN                                                     \n",
              "2                  Afghanistan  ...                                                NaN                                                     \n",
              "3   Africa Western and Central  ...                                                NaN                                                     \n",
              "4                       Angola  ...                                               32.3                                                     \n",
              "\n",
              "[5 rows x 3 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0JNo6-NPqfeU",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 193
        },
        "outputId": "2859f97b-65f8-43b4-a58a-eb7499029bda"
      },
      "source": [
        "# Merging Food Insecurity, Poverty Rate, and Maternal Mortality Rate into one dataframe\n",
        "FI_PR_mmr = pd.merge(FI_PR, mmr, how='left', left_on='Country Name', right_on='Country Name')\n",
        "FI_PR_mmr.head(3)"
      ],
      "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>Country Name</th>\n",
              "      <th>2019_Prevalence of moderate or severe food insecurity in the population (%)</th>\n",
              "      <th>Most recent year value (2017-2020)_Poverty headcount ratio at national poverty lines (% of population)</th>\n",
              "      <th>2017_Maternal mortality ratio (modeled estimate, per 100,000 live births)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Aruba</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Africa Eastern and Southern</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>398.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>63.1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>638.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  Country Name  ...  2017_Maternal mortality ratio (modeled estimate, per 100,000 live births)\n",
              "0                        Aruba  ...                                                NaN                        \n",
              "1  Africa Eastern and Southern  ...                                              398.0                        \n",
              "2                  Afghanistan  ...                                              638.0                        \n",
              "\n",
              "[3 rows x 4 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Hyu5KMXHqjlN"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yu65_E-BqnYk"
      },
      "source": [
        "## Adding Food Insecurity, Poverty Rate and Maternal Mortality to survey Dataframe"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "czEq0ijDqoH-"
      },
      "source": [
        "# Merging FI, PR and mmr with our regions_survey dataframe, and creating new dataframe\n",
        "data2 = pd.merge(data, FI_PR_mmr, how=\"left\", left_on=\"Country\", right_on=\"Country Name\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T2kqQCUhqsgX",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 534
        },
        "outputId": "365366bb-d7fd-44a5-de99-3549bfbec150"
      },
      "source": [
        "print(data2.shape)\n",
        "data2.head(2)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(125306, 164)\n"
          ]
        },
        {
          "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>Unnamed: 0</th>\n",
              "      <th>ID</th>\n",
              "      <th>answered_all</th>\n",
              "      <th>Duration..in.seconds.</th>\n",
              "      <th>RecordedDate</th>\n",
              "      <th>UserLanguage</th>\n",
              "      <th>Dem_age</th>\n",
              "      <th>Dem_gender</th>\n",
              "      <th>Dem_edu</th>\n",
              "      <th>Dem_edu_mom</th>\n",
              "      <th>Dem_employment</th>\n",
              "      <th>Country</th>\n",
              "      <th>Country Income Level</th>\n",
              "      <th>Region</th>\n",
              "      <th>Food_insecurity_2019</th>\n",
              "      <th>Poverty_ratio_2017_2019</th>\n",
              "      <th>Maternal_mortality_ratio_2017</th>\n",
              "      <th>Dem_Expat</th>\n",
              "      <th>Dem_state</th>\n",
              "      <th>Dem_maritalstatus</th>\n",
              "      <th>Dem_dependents</th>\n",
              "      <th>Dem_riskgroup</th>\n",
              "      <th>Dem_isolation</th>\n",
              "      <th>Dem_isolation_adults</th>\n",
              "      <th>Dem_isolation_kids</th>\n",
              "      <th>AD_gain</th>\n",
              "      <th>AD_loss</th>\n",
              "      <th>AD_check</th>\n",
              "      <th>Scale_PSS10_UCLA_1</th>\n",
              "      <th>Scale_PSS10_UCLA_2</th>\n",
              "      <th>Scale_PSS10_UCLA_3</th>\n",
              "      <th>Scale_PSS10_UCLA_4</th>\n",
              "      <th>Scale_PSS10_UCLA_5</th>\n",
              "      <th>Scale_PSS10_UCLA_6</th>\n",
              "      <th>Scale_PSS10_UCLA_7</th>\n",
              "      <th>Scale_PSS10_UCLA_8</th>\n",
              "      <th>Scale_PSS10_UCLA_9</th>\n",
              "      <th>Scale_PSS10_UCLA_10</th>\n",
              "      <th>Scale_SLON_1</th>\n",
              "      <th>Scale_SLON_2</th>\n",
              "      <th>...</th>\n",
              "      <th>SPS_8</th>\n",
              "      <th>SPS_9</th>\n",
              "      <th>SPS_10</th>\n",
              "      <th>Expl_Coping_1</th>\n",
              "      <th>Expl_Coping_2</th>\n",
              "      <th>Expl_Coping_3</th>\n",
              "      <th>Expl_Coping_4</th>\n",
              "      <th>Expl_Coping_5</th>\n",
              "      <th>Expl_Coping_6</th>\n",
              "      <th>Expl_Coping_7</th>\n",
              "      <th>Expl_Coping_8</th>\n",
              "      <th>Expl_Coping_9</th>\n",
              "      <th>Expl_Coping_10</th>\n",
              "      <th>Expl_Coping_11</th>\n",
              "      <th>Expl_Coping_12</th>\n",
              "      <th>Expl_Coping_13</th>\n",
              "      <th>Expl_Coping_14</th>\n",
              "      <th>Expl_Coping_15</th>\n",
              "      <th>Expl_Coping_16</th>\n",
              "      <th>Expl_coping_txt</th>\n",
              "      <th>Expl_media_1</th>\n",
              "      <th>Expl_media_2</th>\n",
              "      <th>Expl_media_3</th>\n",
              "      <th>Expl_media_4</th>\n",
              "      <th>Expl_media_5</th>\n",
              "      <th>Expl_media_6</th>\n",
              "      <th>Final_open</th>\n",
              "      <th>PSS10_avg</th>\n",
              "      <th>SLON3_avg</th>\n",
              "      <th>neu</th>\n",
              "      <th>ext</th>\n",
              "      <th>ope</th>\n",
              "      <th>agr</th>\n",
              "      <th>con</th>\n",
              "      <th>SPS_avg</th>\n",
              "      <th>Scale_UCLA_TRI_avg</th>\n",
              "      <th>Country Name</th>\n",
              "      <th>2019_Prevalence of moderate or severe food insecurity in the population (%)</th>\n",
              "      <th>Most recent year value (2017-2020)_Poverty headcount ratio at national poverty lines (% of population)</th>\n",
              "      <th>2017_Maternal mortality ratio (modeled estimate, per 100,000 live births)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>4069</td>\n",
              "      <td>No</td>\n",
              "      <td>856</td>\n",
              "      <td>5/12/20 1:34</td>\n",
              "      <td>FA</td>\n",
              "      <td>30</td>\n",
              "      <td>Male</td>\n",
              "      <td>College degree, bachelor, master</td>\n",
              "      <td>Up to 6 years of school</td>\n",
              "      <td>Full time employed</td>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>Low Income</td>\n",
              "      <td>South Asia</td>\n",
              "      <td>63.1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>638.0</td>\n",
              "      <td>yes</td>\n",
              "      <td>&lt;U+06A9&gt;&lt;U+0627&gt;&lt;U+0628&gt;&lt;U+0644&gt;</td>\n",
              "      <td>Married/cohabiting</td>\n",
              "      <td>3.0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Life carries on with minor changes</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>· If Program D is adopted there is 1/3 probabi...</td>\n",
              "      <td>No</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.2</td>\n",
              "      <td>1.333333</td>\n",
              "      <td>2.333333</td>\n",
              "      <td>6.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>4.666667</td>\n",
              "      <td>5.666667</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>63.1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>638.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>4460</td>\n",
              "      <td>No</td>\n",
              "      <td>89</td>\n",
              "      <td>5/12/20 0:28</td>\n",
              "      <td>KO</td>\n",
              "      <td>26</td>\n",
              "      <td>Other/would rather not say</td>\n",
              "      <td>PhD/Doctorate</td>\n",
              "      <td>PhD/Doctorate</td>\n",
              "      <td>Student</td>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>Low Income</td>\n",
              "      <td>South Asia</td>\n",
              "      <td>63.1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>638.0</td>\n",
              "      <td>yes</td>\n",
              "      <td>&lt;U+3147&gt;&lt;U+314E&gt;</td>\n",
              "      <td>Other or would rather not say</td>\n",
              "      <td>0.0</td>\n",
              "      <td>No</td>\n",
              "      <td>Life carries on as usual</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>· If Program C is adopted 400 people will die.</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>63.1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>638.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2 rows × 164 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "   Unnamed: 0  ...  2017_Maternal mortality ratio (modeled estimate, per 100,000 live births)\n",
              "0           0  ...                                              638.0                        \n",
              "1           1  ...                                              638.0                        \n",
              "\n",
              "[2 rows x 164 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KCNpV96uqwYH"
      },
      "source": [
        "data2 = data2.drop(['Country Name'], axis=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LeR8Z3Gaq1Ix",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a1c35084-81a2-4cc3-ae13-b572c099d7a0"
      },
      "source": [
        "# Comparing shape of dataframes before and after merge\n",
        "print(data.shape)\n",
        "print(data2.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(125306, 160)\n",
            "(125306, 163)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y4ScI5L0rBFz"
      },
      "source": [
        "# Moving column positions to have demographic information grouped together\n",
        "cols = data2.columns.tolist()\n",
        "column_to_move = \"2019_Prevalence of moderate or severe food insecurity in the population (%)\"\n",
        "new_position = 13\n",
        "\n",
        "cols.insert(new_position, cols.pop(cols.index(column_to_move)))\n",
        "data2 = data2[cols]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GfxZmxrZrD_w"
      },
      "source": [
        "# Moving column positions to have demographic information grouped together\n",
        "cols = data2.columns.tolist()\n",
        "column_to_move = \"Most recent year value (2017-2020)_Poverty headcount ratio at national poverty lines (% of population)\"\n",
        "new_position = 14\n",
        "\n",
        "cols.insert(new_position, cols.pop(cols.index(column_to_move)))\n",
        "data2 = data2[cols]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c6rny5W1rHsa"
      },
      "source": [
        "cols = data2.columns.tolist()\n",
        "column_to_move = \"2017_Maternal mortality ratio (modeled estimate, per 100,000 live births)\"\n",
        "new_position = 15\n",
        "\n",
        "cols.insert(new_position, cols.pop(cols.index(column_to_move)))\n",
        "data2 = data2[cols]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lbPamjd1ragK"
      },
      "source": [
        "## Dataframe including Food Insecurity, Poverty, and Maternal Mortality Rates"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8fJYeqHDrPWV",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 650
        },
        "outputId": "c1a76a6c-b021-4d4e-8d0f-432c5a60246b"
      },
      "source": [
        "data2.head(3)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>ID</th>\n",
              "      <th>answered_all</th>\n",
              "      <th>Duration..in.seconds.</th>\n",
              "      <th>RecordedDate</th>\n",
              "      <th>UserLanguage</th>\n",
              "      <th>Dem_age</th>\n",
              "      <th>Dem_gender</th>\n",
              "      <th>Dem_edu</th>\n",
              "      <th>Dem_edu_mom</th>\n",
              "      <th>Dem_employment</th>\n",
              "      <th>Country</th>\n",
              "      <th>Country Income Level</th>\n",
              "      <th>2019_Prevalence of moderate or severe food insecurity in the population (%)</th>\n",
              "      <th>Most recent year value (2017-2020)_Poverty headcount ratio at national poverty lines (% of population)</th>\n",
              "      <th>2017_Maternal mortality ratio (modeled estimate, per 100,000 live births)</th>\n",
              "      <th>Region</th>\n",
              "      <th>Food_insecurity_2019</th>\n",
              "      <th>Poverty_ratio_2017_2019</th>\n",
              "      <th>Maternal_mortality_ratio_2017</th>\n",
              "      <th>Dem_Expat</th>\n",
              "      <th>Dem_state</th>\n",
              "      <th>Dem_maritalstatus</th>\n",
              "      <th>Dem_dependents</th>\n",
              "      <th>Dem_riskgroup</th>\n",
              "      <th>Dem_isolation</th>\n",
              "      <th>Dem_isolation_adults</th>\n",
              "      <th>Dem_isolation_kids</th>\n",
              "      <th>AD_gain</th>\n",
              "      <th>AD_loss</th>\n",
              "      <th>AD_check</th>\n",
              "      <th>Scale_PSS10_UCLA_1</th>\n",
              "      <th>Scale_PSS10_UCLA_2</th>\n",
              "      <th>Scale_PSS10_UCLA_3</th>\n",
              "      <th>Scale_PSS10_UCLA_4</th>\n",
              "      <th>Scale_PSS10_UCLA_5</th>\n",
              "      <th>Scale_PSS10_UCLA_6</th>\n",
              "      <th>Scale_PSS10_UCLA_7</th>\n",
              "      <th>Scale_PSS10_UCLA_8</th>\n",
              "      <th>Scale_PSS10_UCLA_9</th>\n",
              "      <th>...</th>\n",
              "      <th>SPS_4</th>\n",
              "      <th>SPS_5</th>\n",
              "      <th>SPS_6</th>\n",
              "      <th>SPS_7</th>\n",
              "      <th>SPS_8</th>\n",
              "      <th>SPS_9</th>\n",
              "      <th>SPS_10</th>\n",
              "      <th>Expl_Coping_1</th>\n",
              "      <th>Expl_Coping_2</th>\n",
              "      <th>Expl_Coping_3</th>\n",
              "      <th>Expl_Coping_4</th>\n",
              "      <th>Expl_Coping_5</th>\n",
              "      <th>Expl_Coping_6</th>\n",
              "      <th>Expl_Coping_7</th>\n",
              "      <th>Expl_Coping_8</th>\n",
              "      <th>Expl_Coping_9</th>\n",
              "      <th>Expl_Coping_10</th>\n",
              "      <th>Expl_Coping_11</th>\n",
              "      <th>Expl_Coping_12</th>\n",
              "      <th>Expl_Coping_13</th>\n",
              "      <th>Expl_Coping_14</th>\n",
              "      <th>Expl_Coping_15</th>\n",
              "      <th>Expl_Coping_16</th>\n",
              "      <th>Expl_coping_txt</th>\n",
              "      <th>Expl_media_1</th>\n",
              "      <th>Expl_media_2</th>\n",
              "      <th>Expl_media_3</th>\n",
              "      <th>Expl_media_4</th>\n",
              "      <th>Expl_media_5</th>\n",
              "      <th>Expl_media_6</th>\n",
              "      <th>Final_open</th>\n",
              "      <th>PSS10_avg</th>\n",
              "      <th>SLON3_avg</th>\n",
              "      <th>neu</th>\n",
              "      <th>ext</th>\n",
              "      <th>ope</th>\n",
              "      <th>agr</th>\n",
              "      <th>con</th>\n",
              "      <th>SPS_avg</th>\n",
              "      <th>Scale_UCLA_TRI_avg</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>4069</td>\n",
              "      <td>No</td>\n",
              "      <td>856</td>\n",
              "      <td>5/12/20 1:34</td>\n",
              "      <td>FA</td>\n",
              "      <td>30</td>\n",
              "      <td>Male</td>\n",
              "      <td>College degree, bachelor, master</td>\n",
              "      <td>Up to 6 years of school</td>\n",
              "      <td>Full time employed</td>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>Low Income</td>\n",
              "      <td>63.1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>638.0</td>\n",
              "      <td>South Asia</td>\n",
              "      <td>63.1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>638.0</td>\n",
              "      <td>yes</td>\n",
              "      <td>&lt;U+06A9&gt;&lt;U+0627&gt;&lt;U+0628&gt;&lt;U+0644&gt;</td>\n",
              "      <td>Married/cohabiting</td>\n",
              "      <td>3.0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Life carries on with minor changes</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>· If Program D is adopted there is 1/3 probabi...</td>\n",
              "      <td>No</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.2</td>\n",
              "      <td>1.333333</td>\n",
              "      <td>2.333333</td>\n",
              "      <td>6.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>4.666667</td>\n",
              "      <td>5.666667</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>4460</td>\n",
              "      <td>No</td>\n",
              "      <td>89</td>\n",
              "      <td>5/12/20 0:28</td>\n",
              "      <td>KO</td>\n",
              "      <td>26</td>\n",
              "      <td>Other/would rather not say</td>\n",
              "      <td>PhD/Doctorate</td>\n",
              "      <td>PhD/Doctorate</td>\n",
              "      <td>Student</td>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>Low Income</td>\n",
              "      <td>63.1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>638.0</td>\n",
              "      <td>South Asia</td>\n",
              "      <td>63.1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>638.0</td>\n",
              "      <td>yes</td>\n",
              "      <td>&lt;U+3147&gt;&lt;U+314E&gt;</td>\n",
              "      <td>Other or would rather not say</td>\n",
              "      <td>0.0</td>\n",
              "      <td>No</td>\n",
              "      <td>Life carries on as usual</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>· If Program C is adopted 400 people will die.</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>9383</td>\n",
              "      <td>No</td>\n",
              "      <td>305</td>\n",
              "      <td>4/30/20 0:53</td>\n",
              "      <td>EN</td>\n",
              "      <td>26</td>\n",
              "      <td>Male</td>\n",
              "      <td>PhD/Doctorate</td>\n",
              "      <td>College degree</td>\n",
              "      <td>Full time employed</td>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>Low Income</td>\n",
              "      <td>63.1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>638.0</td>\n",
              "      <td>South Asia</td>\n",
              "      <td>63.1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>638.0</td>\n",
              "      <td>yes</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Isolated</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>· If Program B is adopted, there is 1/3 probab...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3 rows × 163 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "   Unnamed: 0    ID answered_all  ...       con SPS_avg Scale_UCLA_TRI_avg\n",
              "0           0  4069           No  ...  5.666667     NaN                NaN\n",
              "1           1  4460           No  ...       NaN     NaN                NaN\n",
              "2           2  9383           No  ...       NaN     NaN                NaN\n",
              "\n",
              "[3 rows x 163 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VnuSBrtCrYfY",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 398
        },
        "outputId": "ae8dbfde-f173-4d2b-d68e-5f01d8c46cff"
      },
      "source": [
        "# Renaming column features for easier manipulation\n",
        "data2 = data2.rename(columns={\"2019_Prevalence of moderate or severe food insecurity in the population (%)\": \"Food_insecurity_2019\",\n",
        "                   \"Most recent year value (2017-2020)_Poverty headcount ratio at national poverty lines (% of population)\": \"Poverty_ratio_2017_2019\",\n",
        "                   \"2017_Maternal mortality ratio (modeled estimate, per 100,000 live births)\": \"Maternal_mortality_ratio_2017\"})\n",
        "data2.head(2)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
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              "      <th>ID</th>\n",
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              "      <th>Duration..in.seconds.</th>\n",
              "      <th>RecordedDate</th>\n",
              "      <th>UserLanguage</th>\n",
              "      <th>Dem_age</th>\n",
              "      <th>Dem_gender</th>\n",
              "      <th>Dem_edu</th>\n",
              "      <th>Dem_edu_mom</th>\n",
              "      <th>Dem_employment</th>\n",
              "      <th>Country</th>\n",
              "      <th>Country Income Level</th>\n",
              "      <th>Food_insecurity_2019</th>\n",
              "      <th>Poverty_ratio_2017_2019</th>\n",
              "      <th>Maternal_mortality_ratio_2017</th>\n",
              "      <th>Region</th>\n",
              "      <th>Food_insecurity_2019</th>\n",
              "      <th>Poverty_ratio_2017_2019</th>\n",
              "      <th>Maternal_mortality_ratio_2017</th>\n",
              "      <th>Dem_Expat</th>\n",
              "      <th>Dem_state</th>\n",
              "      <th>Dem_maritalstatus</th>\n",
              "      <th>Dem_dependents</th>\n",
              "      <th>Dem_riskgroup</th>\n",
              "      <th>Dem_isolation</th>\n",
              "      <th>Dem_isolation_adults</th>\n",
              "      <th>Dem_isolation_kids</th>\n",
              "      <th>AD_gain</th>\n",
              "      <th>AD_loss</th>\n",
              "      <th>AD_check</th>\n",
              "      <th>Scale_PSS10_UCLA_1</th>\n",
              "      <th>Scale_PSS10_UCLA_2</th>\n",
              "      <th>Scale_PSS10_UCLA_3</th>\n",
              "      <th>Scale_PSS10_UCLA_4</th>\n",
              "      <th>Scale_PSS10_UCLA_5</th>\n",
              "      <th>Scale_PSS10_UCLA_6</th>\n",
              "      <th>Scale_PSS10_UCLA_7</th>\n",
              "      <th>Scale_PSS10_UCLA_8</th>\n",
              "      <th>Scale_PSS10_UCLA_9</th>\n",
              "      <th>...</th>\n",
              "      <th>SPS_4</th>\n",
              "      <th>SPS_5</th>\n",
              "      <th>SPS_6</th>\n",
              "      <th>SPS_7</th>\n",
              "      <th>SPS_8</th>\n",
              "      <th>SPS_9</th>\n",
              "      <th>SPS_10</th>\n",
              "      <th>Expl_Coping_1</th>\n",
              "      <th>Expl_Coping_2</th>\n",
              "      <th>Expl_Coping_3</th>\n",
              "      <th>Expl_Coping_4</th>\n",
              "      <th>Expl_Coping_5</th>\n",
              "      <th>Expl_Coping_6</th>\n",
              "      <th>Expl_Coping_7</th>\n",
              "      <th>Expl_Coping_8</th>\n",
              "      <th>Expl_Coping_9</th>\n",
              "      <th>Expl_Coping_10</th>\n",
              "      <th>Expl_Coping_11</th>\n",
              "      <th>Expl_Coping_12</th>\n",
              "      <th>Expl_Coping_13</th>\n",
              "      <th>Expl_Coping_14</th>\n",
              "      <th>Expl_Coping_15</th>\n",
              "      <th>Expl_Coping_16</th>\n",
              "      <th>Expl_coping_txt</th>\n",
              "      <th>Expl_media_1</th>\n",
              "      <th>Expl_media_2</th>\n",
              "      <th>Expl_media_3</th>\n",
              "      <th>Expl_media_4</th>\n",
              "      <th>Expl_media_5</th>\n",
              "      <th>Expl_media_6</th>\n",
              "      <th>Final_open</th>\n",
              "      <th>PSS10_avg</th>\n",
              "      <th>SLON3_avg</th>\n",
              "      <th>neu</th>\n",
              "      <th>ext</th>\n",
              "      <th>ope</th>\n",
              "      <th>agr</th>\n",
              "      <th>con</th>\n",
              "      <th>SPS_avg</th>\n",
              "      <th>Scale_UCLA_TRI_avg</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>4069</td>\n",
              "      <td>No</td>\n",
              "      <td>856</td>\n",
              "      <td>5/12/20 1:34</td>\n",
              "      <td>FA</td>\n",
              "      <td>30</td>\n",
              "      <td>Male</td>\n",
              "      <td>College degree, bachelor, master</td>\n",
              "      <td>Up to 6 years of school</td>\n",
              "      <td>Full time employed</td>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>Low Income</td>\n",
              "      <td>63.1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>638.0</td>\n",
              "      <td>South Asia</td>\n",
              "      <td>63.1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>638.0</td>\n",
              "      <td>yes</td>\n",
              "      <td>&lt;U+06A9&gt;&lt;U+0627&gt;&lt;U+0628&gt;&lt;U+0644&gt;</td>\n",
              "      <td>Married/cohabiting</td>\n",
              "      <td>3.0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Life carries on with minor changes</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>· If Program D is adopted there is 1/3 probabi...</td>\n",
              "      <td>No</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.2</td>\n",
              "      <td>1.333333</td>\n",
              "      <td>2.333333</td>\n",
              "      <td>6.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>4.666667</td>\n",
              "      <td>5.666667</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>4460</td>\n",
              "      <td>No</td>\n",
              "      <td>89</td>\n",
              "      <td>5/12/20 0:28</td>\n",
              "      <td>KO</td>\n",
              "      <td>26</td>\n",
              "      <td>Other/would rather not say</td>\n",
              "      <td>PhD/Doctorate</td>\n",
              "      <td>PhD/Doctorate</td>\n",
              "      <td>Student</td>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>Low Income</td>\n",
              "      <td>63.1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>638.0</td>\n",
              "      <td>South Asia</td>\n",
              "      <td>63.1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>638.0</td>\n",
              "      <td>yes</td>\n",
              "      <td>&lt;U+3147&gt;&lt;U+314E&gt;</td>\n",
              "      <td>Other or would rather not say</td>\n",
              "      <td>0.0</td>\n",
              "      <td>No</td>\n",
              "      <td>Life carries on as usual</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>· If Program C is adopted 400 people will die.</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2 rows × 163 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "   Unnamed: 0    ID answered_all  ...       con SPS_avg Scale_UCLA_TRI_avg\n",
              "0           0  4069           No  ...  5.666667     NaN                NaN\n",
              "1           1  4460           No  ...       NaN     NaN                NaN\n",
              "\n",
              "[2 rows x 163 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l8Dz0sysrhPs"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IUqlyjWPendt"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g5HIJzWHewb2"
      },
      "source": [
        "## Missing values per column\n",
        "Will allow us to see which survey questions can be dropped from the data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "odU9NVudene4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1aa8e559-e358-4c68-af5c-a7de35ce37ee"
      },
      "source": [
        "# Looking at the count of missing values per column\n",
        "columns_with_nan = data2.isnull().sum()\n",
        "columns_with_nan"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Unnamed: 0                    0\n",
              "ID                            0\n",
              "answered_all                  0\n",
              "Duration..in.seconds.         0\n",
              "RecordedDate                  0\n",
              "                          ...  \n",
              "ope                       16987\n",
              "agr                       16976\n",
              "con                       16984\n",
              "SPS_avg                   30917\n",
              "Scale_UCLA_TRI_avg       125255\n",
              "Length: 163, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "220JjHTHfClH",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7de9d2e0-6da7-4263-8a64-77ce501be0a6"
      },
      "source": [
        "# looking at the % of missing values per column, sorted most to least\n",
        "pct_columns_with_nan = data2.isnull().mean().round(4)* 100\n",
        "pct_columns_with_nan.sort_values(ascending=False)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "time_spent_in_war_TXT    100.00\n",
              "experience_war_TXT       100.00\n",
              "Scale_UCLA_TRI_avg        99.96\n",
              "loss_during_war           99.96\n",
              "Scale_UCLA_TRI_1          99.96\n",
              "                          ...  \n",
              "RecordedDate               0.00\n",
              "Duration..in.seconds.      0.00\n",
              "answered_all               0.00\n",
              "ID                         0.00\n",
              "Unnamed: 0                 0.00\n",
              "Length: 163, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "59ttChNEfL7V",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 544
        },
        "outputId": "e5152827-cb33-47d4-c647-e66f910e28d3"
      },
      "source": [
        "# Looking at % missing values by region\n",
        "reg_pct_null = data.isnull().groupby(data2['Region']).mean().round(4)* 100\n",
        "reg_pct_null"
      ],
      "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>Unnamed: 0</th>\n",
              "      <th>ID</th>\n",
              "      <th>answered_all</th>\n",
              "      <th>Duration..in.seconds.</th>\n",
              "      <th>RecordedDate</th>\n",
              "      <th>UserLanguage</th>\n",
              "      <th>Dem_age</th>\n",
              "      <th>Dem_gender</th>\n",
              "      <th>Dem_edu</th>\n",
              "      <th>Dem_edu_mom</th>\n",
              "      <th>Dem_employment</th>\n",
              "      <th>Country</th>\n",
              "      <th>Country Income Level</th>\n",
              "      <th>Region</th>\n",
              "      <th>Food_insecurity_2019</th>\n",
              "      <th>Poverty_ratio_2017_2019</th>\n",
              "      <th>Maternal_mortality_ratio_2017</th>\n",
              "      <th>Dem_Expat</th>\n",
              "      <th>Dem_state</th>\n",
              "      <th>Dem_maritalstatus</th>\n",
              "      <th>Dem_dependents</th>\n",
              "      <th>Dem_riskgroup</th>\n",
              "      <th>Dem_isolation</th>\n",
              "      <th>Dem_isolation_adults</th>\n",
              "      <th>Dem_isolation_kids</th>\n",
              "      <th>AD_gain</th>\n",
              "      <th>AD_loss</th>\n",
              "      <th>AD_check</th>\n",
              "      <th>Scale_PSS10_UCLA_1</th>\n",
              "      <th>Scale_PSS10_UCLA_2</th>\n",
              "      <th>Scale_PSS10_UCLA_3</th>\n",
              "      <th>Scale_PSS10_UCLA_4</th>\n",
              "      <th>Scale_PSS10_UCLA_5</th>\n",
              "      <th>Scale_PSS10_UCLA_6</th>\n",
              "      <th>Scale_PSS10_UCLA_7</th>\n",
              "      <th>Scale_PSS10_UCLA_8</th>\n",
              "      <th>Scale_PSS10_UCLA_9</th>\n",
              "      <th>Scale_PSS10_UCLA_10</th>\n",
              "      <th>Scale_SLON_1</th>\n",
              "      <th>Scale_SLON_2</th>\n",
              "      <th>...</th>\n",
              "      <th>SPS_4</th>\n",
              "      <th>SPS_5</th>\n",
              "      <th>SPS_6</th>\n",
              "      <th>SPS_7</th>\n",
              "      <th>SPS_8</th>\n",
              "      <th>SPS_9</th>\n",
              "      <th>SPS_10</th>\n",
              "      <th>Expl_Coping_1</th>\n",
              "      <th>Expl_Coping_2</th>\n",
              "      <th>Expl_Coping_3</th>\n",
              "      <th>Expl_Coping_4</th>\n",
              "      <th>Expl_Coping_5</th>\n",
              "      <th>Expl_Coping_6</th>\n",
              "      <th>Expl_Coping_7</th>\n",
              "      <th>Expl_Coping_8</th>\n",
              "      <th>Expl_Coping_9</th>\n",
              "      <th>Expl_Coping_10</th>\n",
              "      <th>Expl_Coping_11</th>\n",
              "      <th>Expl_Coping_12</th>\n",
              "      <th>Expl_Coping_13</th>\n",
              "      <th>Expl_Coping_14</th>\n",
              "      <th>Expl_Coping_15</th>\n",
              "      <th>Expl_Coping_16</th>\n",
              "      <th>Expl_coping_txt</th>\n",
              "      <th>Expl_media_1</th>\n",
              "      <th>Expl_media_2</th>\n",
              "      <th>Expl_media_3</th>\n",
              "      <th>Expl_media_4</th>\n",
              "      <th>Expl_media_5</th>\n",
              "      <th>Expl_media_6</th>\n",
              "      <th>Final_open</th>\n",
              "      <th>PSS10_avg</th>\n",
              "      <th>SLON3_avg</th>\n",
              "      <th>neu</th>\n",
              "      <th>ext</th>\n",
              "      <th>ope</th>\n",
              "      <th>agr</th>\n",
              "      <th>con</th>\n",
              "      <th>SPS_avg</th>\n",
              "      <th>Scale_UCLA_TRI_avg</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>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>East Asia and Pacific</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.16</td>\n",
              "      <td>0.20</td>\n",
              "      <td>0.71</td>\n",
              "      <td>0.67</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>28.23</td>\n",
              "      <td>74.74</td>\n",
              "      <td>27.60</td>\n",
              "      <td>0.34</td>\n",
              "      <td>5.89</td>\n",
              "      <td>0.68</td>\n",
              "      <td>3.03</td>\n",
              "      <td>0.31</td>\n",
              "      <td>0.37</td>\n",
              "      <td>23.96</td>\n",
              "      <td>25.05</td>\n",
              "      <td>52.88</td>\n",
              "      <td>53.20</td>\n",
              "      <td>17.54</td>\n",
              "      <td>8.09</td>\n",
              "      <td>8.05</td>\n",
              "      <td>8.12</td>\n",
              "      <td>8.04</td>\n",
              "      <td>8.07</td>\n",
              "      <td>8.02</td>\n",
              "      <td>8.03</td>\n",
              "      <td>8.11</td>\n",
              "      <td>7.95</td>\n",
              "      <td>8.00</td>\n",
              "      <td>8.05</td>\n",
              "      <td>8.09</td>\n",
              "      <td>...</td>\n",
              "      <td>20.61</td>\n",
              "      <td>20.61</td>\n",
              "      <td>20.60</td>\n",
              "      <td>20.60</td>\n",
              "      <td>20.59</td>\n",
              "      <td>20.61</td>\n",
              "      <td>20.54</td>\n",
              "      <td>20.70</td>\n",
              "      <td>20.67</td>\n",
              "      <td>20.68</td>\n",
              "      <td>21.03</td>\n",
              "      <td>20.84</td>\n",
              "      <td>20.80</td>\n",
              "      <td>20.89</td>\n",
              "      <td>20.94</td>\n",
              "      <td>20.70</td>\n",
              "      <td>20.82</td>\n",
              "      <td>20.68</td>\n",
              "      <td>20.75</td>\n",
              "      <td>20.94</td>\n",
              "      <td>20.71</td>\n",
              "      <td>20.89</td>\n",
              "      <td>20.77</td>\n",
              "      <td>96.22</td>\n",
              "      <td>21.30</td>\n",
              "      <td>21.29</td>\n",
              "      <td>21.21</td>\n",
              "      <td>21.26</td>\n",
              "      <td>21.19</td>\n",
              "      <td>21.23</td>\n",
              "      <td>77.59</td>\n",
              "      <td>7.55</td>\n",
              "      <td>7.65</td>\n",
              "      <td>12.02</td>\n",
              "      <td>12.03</td>\n",
              "      <td>12.02</td>\n",
              "      <td>12.02</td>\n",
              "      <td>12.00</td>\n",
              "      <td>20.27</td>\n",
              "      <td>100.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Europe and Central Asia</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.20</td>\n",
              "      <td>0.61</td>\n",
              "      <td>1.68</td>\n",
              "      <td>0.80</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5.66</td>\n",
              "      <td>5.75</td>\n",
              "      <td>4.23</td>\n",
              "      <td>0.42</td>\n",
              "      <td>12.07</td>\n",
              "      <td>0.74</td>\n",
              "      <td>3.10</td>\n",
              "      <td>0.41</td>\n",
              "      <td>1.40</td>\n",
              "      <td>18.82</td>\n",
              "      <td>20.25</td>\n",
              "      <td>54.20</td>\n",
              "      <td>54.69</td>\n",
              "      <td>13.25</td>\n",
              "      <td>7.77</td>\n",
              "      <td>7.83</td>\n",
              "      <td>7.76</td>\n",
              "      <td>7.89</td>\n",
              "      <td>8.02</td>\n",
              "      <td>7.79</td>\n",
              "      <td>7.93</td>\n",
              "      <td>7.98</td>\n",
              "      <td>7.75</td>\n",
              "      <td>7.81</td>\n",
              "      <td>7.84</td>\n",
              "      <td>7.92</td>\n",
              "      <td>...</td>\n",
              "      <td>24.06</td>\n",
              "      <td>24.07</td>\n",
              "      <td>24.14</td>\n",
              "      <td>24.06</td>\n",
              "      <td>24.17</td>\n",
              "      <td>24.29</td>\n",
              "      <td>24.06</td>\n",
              "      <td>24.44</td>\n",
              "      <td>24.78</td>\n",
              "      <td>24.35</td>\n",
              "      <td>26.16</td>\n",
              "      <td>25.39</td>\n",
              "      <td>24.42</td>\n",
              "      <td>25.21</td>\n",
              "      <td>25.30</td>\n",
              "      <td>24.32</td>\n",
              "      <td>24.87</td>\n",
              "      <td>24.67</td>\n",
              "      <td>24.49</td>\n",
              "      <td>25.55</td>\n",
              "      <td>24.40</td>\n",
              "      <td>24.91</td>\n",
              "      <td>24.42</td>\n",
              "      <td>95.04</td>\n",
              "      <td>25.10</td>\n",
              "      <td>25.13</td>\n",
              "      <td>25.09</td>\n",
              "      <td>25.08</td>\n",
              "      <td>25.08</td>\n",
              "      <td>25.08</td>\n",
              "      <td>80.48</td>\n",
              "      <td>7.16</td>\n",
              "      <td>7.36</td>\n",
              "      <td>13.03</td>\n",
              "      <td>13.07</td>\n",
              "      <td>13.07</td>\n",
              "      <td>13.07</td>\n",
              "      <td>13.08</td>\n",
              "      <td>23.72</td>\n",
              "      <td>99.94</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Latin America and Caribbean</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.40</td>\n",
              "      <td>0.70</td>\n",
              "      <td>1.59</td>\n",
              "      <td>0.69</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6.16</td>\n",
              "      <td>9.40</td>\n",
              "      <td>0.09</td>\n",
              "      <td>0.86</td>\n",
              "      <td>6.03</td>\n",
              "      <td>0.58</td>\n",
              "      <td>2.08</td>\n",
              "      <td>0.48</td>\n",
              "      <td>1.58</td>\n",
              "      <td>4.50</td>\n",
              "      <td>5.75</td>\n",
              "      <td>55.27</td>\n",
              "      <td>56.36</td>\n",
              "      <td>14.67</td>\n",
              "      <td>7.82</td>\n",
              "      <td>7.75</td>\n",
              "      <td>7.83</td>\n",
              "      <td>7.81</td>\n",
              "      <td>7.80</td>\n",
              "      <td>7.79</td>\n",
              "      <td>8.01</td>\n",
              "      <td>7.91</td>\n",
              "      <td>7.73</td>\n",
              "      <td>7.61</td>\n",
              "      <td>7.86</td>\n",
              "      <td>7.83</td>\n",
              "      <td>...</td>\n",
              "      <td>31.18</td>\n",
              "      <td>31.11</td>\n",
              "      <td>31.23</td>\n",
              "      <td>31.16</td>\n",
              "      <td>31.20</td>\n",
              "      <td>31.16</td>\n",
              "      <td>31.17</td>\n",
              "      <td>31.41</td>\n",
              "      <td>31.55</td>\n",
              "      <td>31.41</td>\n",
              "      <td>31.82</td>\n",
              "      <td>31.48</td>\n",
              "      <td>31.37</td>\n",
              "      <td>31.71</td>\n",
              "      <td>31.78</td>\n",
              "      <td>31.34</td>\n",
              "      <td>31.54</td>\n",
              "      <td>31.49</td>\n",
              "      <td>31.20</td>\n",
              "      <td>31.55</td>\n",
              "      <td>31.31</td>\n",
              "      <td>31.48</td>\n",
              "      <td>31.27</td>\n",
              "      <td>86.99</td>\n",
              "      <td>32.26</td>\n",
              "      <td>32.23</td>\n",
              "      <td>32.36</td>\n",
              "      <td>32.25</td>\n",
              "      <td>32.34</td>\n",
              "      <td>32.18</td>\n",
              "      <td>73.76</td>\n",
              "      <td>6.96</td>\n",
              "      <td>7.13</td>\n",
              "      <td>15.25</td>\n",
              "      <td>15.29</td>\n",
              "      <td>15.35</td>\n",
              "      <td>15.27</td>\n",
              "      <td>15.30</td>\n",
              "      <td>30.71</td>\n",
              "      <td>100.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Middle East and North Africa</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.28</td>\n",
              "      <td>0.56</td>\n",
              "      <td>1.69</td>\n",
              "      <td>2.53</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>60.39</td>\n",
              "      <td>87.64</td>\n",
              "      <td>23.03</td>\n",
              "      <td>0.28</td>\n",
              "      <td>29.78</td>\n",
              "      <td>1.12</td>\n",
              "      <td>4.78</td>\n",
              "      <td>0.56</td>\n",
              "      <td>1.69</td>\n",
              "      <td>8.43</td>\n",
              "      <td>9.27</td>\n",
              "      <td>53.09</td>\n",
              "      <td>60.67</td>\n",
              "      <td>17.98</td>\n",
              "      <td>8.99</td>\n",
              "      <td>8.99</td>\n",
              "      <td>8.71</td>\n",
              "      <td>8.43</td>\n",
              "      <td>8.99</td>\n",
              "      <td>8.43</td>\n",
              "      <td>8.71</td>\n",
              "      <td>8.43</td>\n",
              "      <td>8.71</td>\n",
              "      <td>8.43</td>\n",
              "      <td>8.43</td>\n",
              "      <td>8.43</td>\n",
              "      <td>...</td>\n",
              "      <td>30.90</td>\n",
              "      <td>31.46</td>\n",
              "      <td>32.02</td>\n",
              "      <td>31.46</td>\n",
              "      <td>31.18</td>\n",
              "      <td>31.74</td>\n",
              "      <td>31.18</td>\n",
              "      <td>31.74</td>\n",
              "      <td>32.30</td>\n",
              "      <td>31.46</td>\n",
              "      <td>32.02</td>\n",
              "      <td>32.30</td>\n",
              "      <td>31.46</td>\n",
              "      <td>32.02</td>\n",
              "      <td>32.30</td>\n",
              "      <td>32.02</td>\n",
              "      <td>32.58</td>\n",
              "      <td>32.30</td>\n",
              "      <td>31.46</td>\n",
              "      <td>32.30</td>\n",
              "      <td>31.74</td>\n",
              "      <td>32.02</td>\n",
              "      <td>31.46</td>\n",
              "      <td>97.19</td>\n",
              "      <td>32.02</td>\n",
              "      <td>32.30</td>\n",
              "      <td>32.02</td>\n",
              "      <td>32.30</td>\n",
              "      <td>32.02</td>\n",
              "      <td>32.30</td>\n",
              "      <td>82.30</td>\n",
              "      <td>8.15</td>\n",
              "      <td>8.15</td>\n",
              "      <td>16.29</td>\n",
              "      <td>16.29</td>\n",
              "      <td>16.29</td>\n",
              "      <td>16.29</td>\n",
              "      <td>16.29</td>\n",
              "      <td>30.62</td>\n",
              "      <td>100.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>North America</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.22</td>\n",
              "      <td>0.14</td>\n",
              "      <td>0.47</td>\n",
              "      <td>0.32</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>100.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.43</td>\n",
              "      <td>6.86</td>\n",
              "      <td>0.32</td>\n",
              "      <td>1.15</td>\n",
              "      <td>0.22</td>\n",
              "      <td>0.50</td>\n",
              "      <td>6.43</td>\n",
              "      <td>7.26</td>\n",
              "      <td>54.81</td>\n",
              "      <td>53.12</td>\n",
              "      <td>12.72</td>\n",
              "      <td>7.47</td>\n",
              "      <td>7.51</td>\n",
              "      <td>7.47</td>\n",
              "      <td>7.44</td>\n",
              "      <td>7.40</td>\n",
              "      <td>7.33</td>\n",
              "      <td>7.40</td>\n",
              "      <td>7.36</td>\n",
              "      <td>7.44</td>\n",
              "      <td>7.40</td>\n",
              "      <td>7.54</td>\n",
              "      <td>7.58</td>\n",
              "      <td>...</td>\n",
              "      <td>21.41</td>\n",
              "      <td>21.41</td>\n",
              "      <td>21.52</td>\n",
              "      <td>21.44</td>\n",
              "      <td>21.30</td>\n",
              "      <td>21.55</td>\n",
              "      <td>21.41</td>\n",
              "      <td>21.84</td>\n",
              "      <td>21.84</td>\n",
              "      <td>21.55</td>\n",
              "      <td>22.59</td>\n",
              "      <td>22.13</td>\n",
              "      <td>21.62</td>\n",
              "      <td>22.38</td>\n",
              "      <td>22.31</td>\n",
              "      <td>21.66</td>\n",
              "      <td>21.88</td>\n",
              "      <td>21.91</td>\n",
              "      <td>21.66</td>\n",
              "      <td>22.45</td>\n",
              "      <td>21.52</td>\n",
              "      <td>21.77</td>\n",
              "      <td>21.66</td>\n",
              "      <td>93.25</td>\n",
              "      <td>21.62</td>\n",
              "      <td>21.66</td>\n",
              "      <td>21.73</td>\n",
              "      <td>21.73</td>\n",
              "      <td>21.77</td>\n",
              "      <td>21.98</td>\n",
              "      <td>76.44</td>\n",
              "      <td>7.15</td>\n",
              "      <td>7.11</td>\n",
              "      <td>12.72</td>\n",
              "      <td>12.75</td>\n",
              "      <td>12.72</td>\n",
              "      <td>12.79</td>\n",
              "      <td>12.75</td>\n",
              "      <td>21.23</td>\n",
              "      <td>100.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Other</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>2.69</td>\n",
              "      <td>4.48</td>\n",
              "      <td>4.93</td>\n",
              "      <td>0.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>100.00</td>\n",
              "      <td>100.00</td>\n",
              "      <td>100.00</td>\n",
              "      <td>1.79</td>\n",
              "      <td>20.18</td>\n",
              "      <td>1.79</td>\n",
              "      <td>8.52</td>\n",
              "      <td>1.79</td>\n",
              "      <td>4.93</td>\n",
              "      <td>19.73</td>\n",
              "      <td>22.42</td>\n",
              "      <td>62.33</td>\n",
              "      <td>54.71</td>\n",
              "      <td>27.35</td>\n",
              "      <td>15.70</td>\n",
              "      <td>15.70</td>\n",
              "      <td>15.25</td>\n",
              "      <td>14.80</td>\n",
              "      <td>15.25</td>\n",
              "      <td>15.70</td>\n",
              "      <td>15.25</td>\n",
              "      <td>14.35</td>\n",
              "      <td>16.14</td>\n",
              "      <td>15.25</td>\n",
              "      <td>16.59</td>\n",
              "      <td>15.70</td>\n",
              "      <td>...</td>\n",
              "      <td>41.70</td>\n",
              "      <td>40.36</td>\n",
              "      <td>40.81</td>\n",
              "      <td>40.36</td>\n",
              "      <td>40.36</td>\n",
              "      <td>40.81</td>\n",
              "      <td>40.36</td>\n",
              "      <td>41.26</td>\n",
              "      <td>40.81</td>\n",
              "      <td>41.26</td>\n",
              "      <td>42.15</td>\n",
              "      <td>41.70</td>\n",
              "      <td>41.70</td>\n",
              "      <td>43.05</td>\n",
              "      <td>42.60</td>\n",
              "      <td>41.70</td>\n",
              "      <td>41.70</td>\n",
              "      <td>41.26</td>\n",
              "      <td>42.15</td>\n",
              "      <td>43.05</td>\n",
              "      <td>41.26</td>\n",
              "      <td>41.70</td>\n",
              "      <td>40.81</td>\n",
              "      <td>95.07</td>\n",
              "      <td>42.60</td>\n",
              "      <td>42.15</td>\n",
              "      <td>42.15</td>\n",
              "      <td>42.60</td>\n",
              "      <td>40.81</td>\n",
              "      <td>42.15</td>\n",
              "      <td>79.82</td>\n",
              "      <td>13.45</td>\n",
              "      <td>13.90</td>\n",
              "      <td>24.66</td>\n",
              "      <td>25.56</td>\n",
              "      <td>25.11</td>\n",
              "      <td>25.11</td>\n",
              "      <td>25.11</td>\n",
              "      <td>39.46</td>\n",
              "      <td>100.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>South Asia</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.31</td>\n",
              "      <td>1.22</td>\n",
              "      <td>2.24</td>\n",
              "      <td>1.93</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>44.81</td>\n",
              "      <td>99.90</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.71</td>\n",
              "      <td>14.66</td>\n",
              "      <td>0.71</td>\n",
              "      <td>5.80</td>\n",
              "      <td>0.51</td>\n",
              "      <td>0.51</td>\n",
              "      <td>11.10</td>\n",
              "      <td>12.42</td>\n",
              "      <td>57.94</td>\n",
              "      <td>59.16</td>\n",
              "      <td>25.25</td>\n",
              "      <td>16.50</td>\n",
              "      <td>16.60</td>\n",
              "      <td>16.09</td>\n",
              "      <td>16.50</td>\n",
              "      <td>16.50</td>\n",
              "      <td>16.29</td>\n",
              "      <td>16.70</td>\n",
              "      <td>16.40</td>\n",
              "      <td>16.80</td>\n",
              "      <td>17.31</td>\n",
              "      <td>17.11</td>\n",
              "      <td>17.21</td>\n",
              "      <td>...</td>\n",
              "      <td>43.28</td>\n",
              "      <td>43.18</td>\n",
              "      <td>43.48</td>\n",
              "      <td>43.28</td>\n",
              "      <td>43.69</td>\n",
              "      <td>43.38</td>\n",
              "      <td>43.28</td>\n",
              "      <td>44.60</td>\n",
              "      <td>44.20</td>\n",
              "      <td>43.89</td>\n",
              "      <td>44.50</td>\n",
              "      <td>44.30</td>\n",
              "      <td>43.99</td>\n",
              "      <td>44.20</td>\n",
              "      <td>44.91</td>\n",
              "      <td>43.89</td>\n",
              "      <td>44.20</td>\n",
              "      <td>44.30</td>\n",
              "      <td>44.40</td>\n",
              "      <td>44.30</td>\n",
              "      <td>44.09</td>\n",
              "      <td>44.30</td>\n",
              "      <td>44.20</td>\n",
              "      <td>95.42</td>\n",
              "      <td>45.82</td>\n",
              "      <td>45.93</td>\n",
              "      <td>45.42</td>\n",
              "      <td>45.72</td>\n",
              "      <td>45.72</td>\n",
              "      <td>46.03</td>\n",
              "      <td>83.30</td>\n",
              "      <td>14.05</td>\n",
              "      <td>15.38</td>\n",
              "      <td>25.97</td>\n",
              "      <td>26.07</td>\n",
              "      <td>25.97</td>\n",
              "      <td>25.66</td>\n",
              "      <td>25.97</td>\n",
              "      <td>42.57</td>\n",
              "      <td>100.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sub-Saharan Africa</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.35</td>\n",
              "      <td>1.06</td>\n",
              "      <td>1.42</td>\n",
              "      <td>0.71</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>11.70</td>\n",
              "      <td>77.30</td>\n",
              "      <td>3.90</td>\n",
              "      <td>1.06</td>\n",
              "      <td>20.57</td>\n",
              "      <td>1.42</td>\n",
              "      <td>3.90</td>\n",
              "      <td>1.06</td>\n",
              "      <td>1.42</td>\n",
              "      <td>12.06</td>\n",
              "      <td>13.83</td>\n",
              "      <td>60.28</td>\n",
              "      <td>53.55</td>\n",
              "      <td>18.44</td>\n",
              "      <td>15.25</td>\n",
              "      <td>12.77</td>\n",
              "      <td>13.48</td>\n",
              "      <td>13.83</td>\n",
              "      <td>13.48</td>\n",
              "      <td>13.83</td>\n",
              "      <td>13.12</td>\n",
              "      <td>13.83</td>\n",
              "      <td>13.48</td>\n",
              "      <td>12.77</td>\n",
              "      <td>15.60</td>\n",
              "      <td>14.89</td>\n",
              "      <td>...</td>\n",
              "      <td>37.23</td>\n",
              "      <td>36.52</td>\n",
              "      <td>37.59</td>\n",
              "      <td>38.30</td>\n",
              "      <td>37.23</td>\n",
              "      <td>36.88</td>\n",
              "      <td>37.59</td>\n",
              "      <td>36.17</td>\n",
              "      <td>37.23</td>\n",
              "      <td>36.88</td>\n",
              "      <td>36.88</td>\n",
              "      <td>37.94</td>\n",
              "      <td>36.88</td>\n",
              "      <td>36.88</td>\n",
              "      <td>37.59</td>\n",
              "      <td>37.23</td>\n",
              "      <td>37.23</td>\n",
              "      <td>36.88</td>\n",
              "      <td>36.88</td>\n",
              "      <td>36.52</td>\n",
              "      <td>36.52</td>\n",
              "      <td>37.23</td>\n",
              "      <td>36.88</td>\n",
              "      <td>95.04</td>\n",
              "      <td>37.59</td>\n",
              "      <td>36.88</td>\n",
              "      <td>36.52</td>\n",
              "      <td>38.30</td>\n",
              "      <td>37.59</td>\n",
              "      <td>37.59</td>\n",
              "      <td>80.50</td>\n",
              "      <td>10.28</td>\n",
              "      <td>12.06</td>\n",
              "      <td>21.28</td>\n",
              "      <td>21.28</td>\n",
              "      <td>21.28</td>\n",
              "      <td>21.63</td>\n",
              "      <td>20.92</td>\n",
              "      <td>35.46</td>\n",
              "      <td>100.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Unrecognizable</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>3.67</td>\n",
              "      <td>20.37</td>\n",
              "      <td>35.60</td>\n",
              "      <td>28.99</td>\n",
              "      <td>100.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>100.00</td>\n",
              "      <td>100.00</td>\n",
              "      <td>100.00</td>\n",
              "      <td>20.73</td>\n",
              "      <td>42.75</td>\n",
              "      <td>24.22</td>\n",
              "      <td>32.29</td>\n",
              "      <td>19.08</td>\n",
              "      <td>27.34</td>\n",
              "      <td>37.80</td>\n",
              "      <td>40.92</td>\n",
              "      <td>65.69</td>\n",
              "      <td>71.74</td>\n",
              "      <td>45.32</td>\n",
              "      <td>34.31</td>\n",
              "      <td>34.50</td>\n",
              "      <td>33.94</td>\n",
              "      <td>34.13</td>\n",
              "      <td>34.68</td>\n",
              "      <td>34.68</td>\n",
              "      <td>33.76</td>\n",
              "      <td>34.68</td>\n",
              "      <td>33.76</td>\n",
              "      <td>33.21</td>\n",
              "      <td>34.13</td>\n",
              "      <td>34.68</td>\n",
              "      <td>...</td>\n",
              "      <td>59.63</td>\n",
              "      <td>60.18</td>\n",
              "      <td>60.18</td>\n",
              "      <td>59.82</td>\n",
              "      <td>60.00</td>\n",
              "      <td>59.82</td>\n",
              "      <td>59.82</td>\n",
              "      <td>61.28</td>\n",
              "      <td>61.47</td>\n",
              "      <td>60.55</td>\n",
              "      <td>61.83</td>\n",
              "      <td>60.92</td>\n",
              "      <td>60.73</td>\n",
              "      <td>61.65</td>\n",
              "      <td>62.02</td>\n",
              "      <td>60.73</td>\n",
              "      <td>61.28</td>\n",
              "      <td>61.28</td>\n",
              "      <td>61.83</td>\n",
              "      <td>62.20</td>\n",
              "      <td>61.10</td>\n",
              "      <td>61.10</td>\n",
              "      <td>61.65</td>\n",
              "      <td>97.61</td>\n",
              "      <td>61.65</td>\n",
              "      <td>60.92</td>\n",
              "      <td>62.20</td>\n",
              "      <td>61.65</td>\n",
              "      <td>61.47</td>\n",
              "      <td>61.10</td>\n",
              "      <td>88.07</td>\n",
              "      <td>31.38</td>\n",
              "      <td>32.29</td>\n",
              "      <td>43.85</td>\n",
              "      <td>44.04</td>\n",
              "      <td>43.67</td>\n",
              "      <td>43.49</td>\n",
              "      <td>43.67</td>\n",
              "      <td>59.08</td>\n",
              "      <td>100.00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>9 rows × 160 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                              Unnamed: 0  ID  ...  SPS_avg  Scale_UCLA_TRI_avg\n",
              "Region                                        ...                             \n",
              "East Asia and Pacific                  0   0  ...    20.27              100.00\n",
              "Europe and Central Asia                0   0  ...    23.72               99.94\n",
              "Latin America and Caribbean            0   0  ...    30.71              100.00\n",
              "Middle East and North Africa           0   0  ...    30.62              100.00\n",
              "North America                          0   0  ...    21.23              100.00\n",
              "Other                                  0   0  ...    39.46              100.00\n",
              "South Asia                             0   0  ...    42.57              100.00\n",
              "Sub-Saharan Africa                     0   0  ...    35.46              100.00\n",
              "Unrecognizable                         0   0  ...    59.08              100.00\n",
              "\n",
              "[9 rows x 160 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Qga-jObfqK92"
      },
      "source": [
        "## Check Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OKM_h1BXqORx"
      },
      "source": [
        "data=pd.read_csv(\"/content/drive/MyDrive/DS4A-Team10/COVIDistress_data_with_fi_pr_mmr.csv\",engine='python')\n",
        "data=pd.read_csv(\"/content/drive/MyDrive/DS4A-Team10/COVIDistress_data_with_fi_pr_mmr.csv\",engine='python')\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "dfcWMFcIqS6S",
        "outputId": "5e81700c-8cbf-4bc3-b316-e6b35a6e9c87"
      },
      "source": [
        ""
      ],
      "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",
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              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>ID</th>\n",
              "      <th>answered_all</th>\n",
              "      <th>Duration..in.seconds.</th>\n",
              "      <th>RecordedDate</th>\n",
              "      <th>UserLanguage</th>\n",
              "      <th>Dem_age</th>\n",
              "      <th>Dem_gender</th>\n",
              "      <th>Dem_edu</th>\n",
              "      <th>Dem_edu_mom</th>\n",
              "      <th>Dem_employment</th>\n",
              "      <th>Country</th>\n",
              "      <th>Country Income Level</th>\n",
              "      <th>Region</th>\n",
              "      <th>Food_insecurity_2019</th>\n",
              "      <th>Poverty_ratio_2017_2019</th>\n",
              "      <th>Maternal_mortality_ratio_2017</th>\n",
              "      <th>Dem_Expat</th>\n",
              "      <th>Dem_state</th>\n",
              "      <th>Dem_maritalstatus</th>\n",
              "      <th>Dem_dependents</th>\n",
              "      <th>Dem_riskgroup</th>\n",
              "      <th>Dem_isolation</th>\n",
              "      <th>Dem_isolation_adults</th>\n",
              "      <th>Dem_isolation_kids</th>\n",
              "      <th>AD_gain</th>\n",
              "      <th>AD_loss</th>\n",
              "      <th>AD_check</th>\n",
              "      <th>Scale_PSS10_UCLA_1</th>\n",
              "      <th>Scale_PSS10_UCLA_2</th>\n",
              "      <th>Scale_PSS10_UCLA_3</th>\n",
              "      <th>Scale_PSS10_UCLA_4</th>\n",
              "      <th>Scale_PSS10_UCLA_5</th>\n",
              "      <th>Scale_PSS10_UCLA_6</th>\n",
              "      <th>Scale_PSS10_UCLA_7</th>\n",
              "      <th>Scale_PSS10_UCLA_8</th>\n",
              "      <th>Scale_PSS10_UCLA_9</th>\n",
              "      <th>Scale_PSS10_UCLA_10</th>\n",
              "      <th>Scale_SLON_1</th>\n",
              "      <th>Scale_SLON_2</th>\n",
              "      <th>...</th>\n",
              "      <th>SPS_4</th>\n",
              "      <th>SPS_5</th>\n",
              "      <th>SPS_6</th>\n",
              "      <th>SPS_7</th>\n",
              "      <th>SPS_8</th>\n",
              "      <th>SPS_9</th>\n",
              "      <th>SPS_10</th>\n",
              "      <th>Expl_Coping_1</th>\n",
              "      <th>Expl_Coping_2</th>\n",
              "      <th>Expl_Coping_3</th>\n",
              "      <th>Expl_Coping_4</th>\n",
              "      <th>Expl_Coping_5</th>\n",
              "      <th>Expl_Coping_6</th>\n",
              "      <th>Expl_Coping_7</th>\n",
              "      <th>Expl_Coping_8</th>\n",
              "      <th>Expl_Coping_9</th>\n",
              "      <th>Expl_Coping_10</th>\n",
              "      <th>Expl_Coping_11</th>\n",
              "      <th>Expl_Coping_12</th>\n",
              "      <th>Expl_Coping_13</th>\n",
              "      <th>Expl_Coping_14</th>\n",
              "      <th>Expl_Coping_15</th>\n",
              "      <th>Expl_Coping_16</th>\n",
              "      <th>Expl_coping_txt</th>\n",
              "      <th>Expl_media_1</th>\n",
              "      <th>Expl_media_2</th>\n",
              "      <th>Expl_media_3</th>\n",
              "      <th>Expl_media_4</th>\n",
              "      <th>Expl_media_5</th>\n",
              "      <th>Expl_media_6</th>\n",
              "      <th>Final_open</th>\n",
              "      <th>PSS10_avg</th>\n",
              "      <th>SLON3_avg</th>\n",
              "      <th>neu</th>\n",
              "      <th>ext</th>\n",
              "      <th>ope</th>\n",
              "      <th>agr</th>\n",
              "      <th>con</th>\n",
              "      <th>SPS_avg</th>\n",
              "      <th>Scale_UCLA_TRI_avg</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>4069</td>\n",
              "      <td>No</td>\n",
              "      <td>856</td>\n",
              "      <td>5/12/20 1:34</td>\n",
              "      <td>FA</td>\n",
              "      <td>30</td>\n",
              "      <td>Male</td>\n",
              "      <td>College degree, bachelor, master</td>\n",
              "      <td>Up to 6 years of school</td>\n",
              "      <td>Full time employed</td>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>Low Income</td>\n",
              "      <td>South Asia</td>\n",
              "      <td>63.1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>638.0</td>\n",
              "      <td>yes</td>\n",
              "      <td>&lt;U+06A9&gt;&lt;U+0627&gt;&lt;U+0628&gt;&lt;U+0644&gt;</td>\n",
              "      <td>Married/cohabiting</td>\n",
              "      <td>3.0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Life carries on with minor changes</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>· If Program D is adopted there is 1/3 probabi...</td>\n",
              "      <td>No</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.2</td>\n",
              "      <td>1.333333</td>\n",
              "      <td>2.333333</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>4.666667</td>\n",
              "      <td>5.666667</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>4460</td>\n",
              "      <td>No</td>\n",
              "      <td>89</td>\n",
              "      <td>5/12/20 0:28</td>\n",
              "      <td>KO</td>\n",
              "      <td>26</td>\n",
              "      <td>Other/would rather not say</td>\n",
              "      <td>PhD/Doctorate</td>\n",
              "      <td>PhD/Doctorate</td>\n",
              "      <td>Student</td>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>Low Income</td>\n",
              "      <td>South Asia</td>\n",
              "      <td>63.1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>638.0</td>\n",
              "      <td>yes</td>\n",
              "      <td>&lt;U+3147&gt;&lt;U+314E&gt;</td>\n",
              "      <td>Other or would rather not say</td>\n",
              "      <td>0.0</td>\n",
              "      <td>No</td>\n",
              "      <td>Life carries on as usual</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>· If Program C is adopted 400 people will die.</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>9383</td>\n",
              "      <td>No</td>\n",
              "      <td>305</td>\n",
              "      <td>4/30/20 0:53</td>\n",
              "      <td>EN</td>\n",
              "      <td>26</td>\n",
              "      <td>Male</td>\n",
              "      <td>PhD/Doctorate</td>\n",
              "      <td>College degree</td>\n",
              "      <td>Full time employed</td>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>Low Income</td>\n",
              "      <td>South Asia</td>\n",
              "      <td>63.1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>638.0</td>\n",
              "      <td>yes</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Isolated</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>· If Program B is adopted, there is 1/3 probab...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>10249</td>\n",
              "      <td>No</td>\n",
              "      <td>48</td>\n",
              "      <td>4/27/20 7:32</td>\n",
              "      <td>HR</td>\n",
              "      <td>18</td>\n",
              "      <td>Female</td>\n",
              "      <td>College degree, bachelor, master</td>\n",
              "      <td>PhD/Doctorate</td>\n",
              "      <td>Full time employed</td>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>Low Income</td>\n",
              "      <td>South Asia</td>\n",
              "      <td>63.1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>638.0</td>\n",
              "      <td>yes</td>\n",
              "      <td>Grad Zagreb</td>\n",
              "      <td>Single</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Life carries on with minor changes</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>12234</td>\n",
              "      <td>Yes</td>\n",
              "      <td>4443</td>\n",
              "      <td>4/20/20 19:46</td>\n",
              "      <td>FA</td>\n",
              "      <td>22</td>\n",
              "      <td>Female</td>\n",
              "      <td>Some College, short continuing education or eq...</td>\n",
              "      <td>None</td>\n",
              "      <td>Student</td>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>Low Income</td>\n",
              "      <td>South Asia</td>\n",
              "      <td>63.1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>638.0</td>\n",
              "      <td>yes</td>\n",
              "      <td>&lt;U+06A9&gt;&lt;U+0627&gt;&lt;U+0628&gt;&lt;U+0644&gt;</td>\n",
              "      <td>Single</td>\n",
              "      <td>0.0</td>\n",
              "      <td>No</td>\n",
              "      <td>Life carries on with minor changes</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>· If Program A is adopted, 200 people will be ...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Yes</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>...</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>&lt;U+0627&gt;&lt;U+06CC&gt;&lt;U+0646&gt; &lt;U+0648&gt;&lt;U+06CC&gt;&lt;U+06...</td>\n",
              "      <td>2.8</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.666667</td>\n",
              "      <td>5.666667</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>4.666667</td>\n",
              "      <td>5.666667</td>\n",
              "      <td>4.6</td>\n",
              "      <td>NaN</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",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\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>125301</th>\n",
              "      <td>125301</td>\n",
              "      <td>68759</td>\n",
              "      <td>No</td>\n",
              "      <td>1177</td>\n",
              "      <td>4/3/20 8:25</td>\n",
              "      <td>DE</td>\n",
              "      <td>54</td>\n",
              "      <td>Female</td>\n",
              "      <td>College degree, bachelor, master</td>\n",
              "      <td>College degree</td>\n",
              "      <td>Self-employed</td>\n",
              "      <td>Zambia</td>\n",
              "      <td>Lower Middle Income</td>\n",
              "      <td>Sub-Saharan Africa</td>\n",
              "      <td>51.4</td>\n",
              "      <td>NaN</td>\n",
              "      <td>213.0</td>\n",
              "      <td>yes</td>\n",
              "      <td>Chisamba, Sambia, Afrika</td>\n",
              "      <td>Married/cohabiting</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Isolated</td>\n",
              "      <td>7.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>· If Program D is adopted there is 1/3 probabi...</td>\n",
              "      <td>No</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>...</td>\n",
              "      <td>6.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>Meditation, Kochen und Backen, lange Spaziergä...</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.9</td>\n",
              "      <td>1.666667</td>\n",
              "      <td>1.666667</td>\n",
              "      <td>5.333333</td>\n",
              "      <td>4.333333</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>5.6</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>125302</th>\n",
              "      <td>125302</td>\n",
              "      <td>45062</td>\n",
              "      <td>No</td>\n",
              "      <td>456</td>\n",
              "      <td>4/7/20 8:05</td>\n",
              "      <td>EN</td>\n",
              "      <td>20</td>\n",
              "      <td>Female</td>\n",
              "      <td>Some College, short continuing education or eq...</td>\n",
              "      <td>College degree</td>\n",
              "      <td>Student</td>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>Lower Middle Income</td>\n",
              "      <td>Sub-Saharan Africa</td>\n",
              "      <td>69.8</td>\n",
              "      <td>38.3</td>\n",
              "      <td>458.0</td>\n",
              "      <td>no</td>\n",
              "      <td>Marondera</td>\n",
              "      <td>Single</td>\n",
              "      <td>0.0</td>\n",
              "      <td>No</td>\n",
              "      <td>Life carries on with minor changes</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>· If Program C is adopted 400 people will die.</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>125303</th>\n",
              "      <td>125303</td>\n",
              "      <td>46177</td>\n",
              "      <td>No</td>\n",
              "      <td>456</td>\n",
              "      <td>4/7/20 4:14</td>\n",
              "      <td>EN</td>\n",
              "      <td>18</td>\n",
              "      <td>Female</td>\n",
              "      <td>College degree, bachelor, master</td>\n",
              "      <td>College degree</td>\n",
              "      <td>Student</td>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>Lower Middle Income</td>\n",
              "      <td>Sub-Saharan Africa</td>\n",
              "      <td>69.8</td>\n",
              "      <td>38.3</td>\n",
              "      <td>458.0</td>\n",
              "      <td>yes</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Single</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No</td>\n",
              "      <td>Isolated</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>· If Program C is adopted 400 people will die.</td>\n",
              "      <td>Not sure</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4.0</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>4.333333</td>\n",
              "      <td>5.333333</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>125304</th>\n",
              "      <td>125304</td>\n",
              "      <td>73176</td>\n",
              "      <td>No</td>\n",
              "      <td>780</td>\n",
              "      <td>4/4/20 1:19</td>\n",
              "      <td>EN</td>\n",
              "      <td>23</td>\n",
              "      <td>Female</td>\n",
              "      <td>College degree, bachelor, master</td>\n",
              "      <td>College degree</td>\n",
              "      <td>Student</td>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>Lower Middle Income</td>\n",
              "      <td>Sub-Saharan Africa</td>\n",
              "      <td>69.8</td>\n",
              "      <td>38.3</td>\n",
              "      <td>458.0</td>\n",
              "      <td>no</td>\n",
              "      <td>Harare</td>\n",
              "      <td>Single</td>\n",
              "      <td>0.0</td>\n",
              "      <td>No</td>\n",
              "      <td>Life carries on with minor changes</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>· If Program B is adopted, there is 1/3 probab...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No</td>\n",
              "      <td>4.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.8</td>\n",
              "      <td>1.333333</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>4.333333</td>\n",
              "      <td>4.333333</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.666667</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>125305</th>\n",
              "      <td>125305</td>\n",
              "      <td>76696</td>\n",
              "      <td>No</td>\n",
              "      <td>34283</td>\n",
              "      <td>4/2/20 19:07</td>\n",
              "      <td>EN</td>\n",
              "      <td>19</td>\n",
              "      <td>Female</td>\n",
              "      <td>College degree, bachelor, master</td>\n",
              "      <td>College degree</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>Lower Middle Income</td>\n",
              "      <td>Sub-Saharan Africa</td>\n",
              "      <td>69.8</td>\n",
              "      <td>38.3</td>\n",
              "      <td>458.0</td>\n",
              "      <td>yes</td>\n",
              "      <td>Poland</td>\n",
              "      <td>Single</td>\n",
              "      <td>0.0</td>\n",
              "      <td>No</td>\n",
              "      <td>Life carries on with minor changes</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>· If Program C is adopted 400 people will die.</td>\n",
              "      <td>Yes</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>...</td>\n",
              "      <td>4.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.6</td>\n",
              "      <td>2.333333</td>\n",
              "      <td>4.666667</td>\n",
              "      <td>3.666667</td>\n",
              "      <td>3.500000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3.500000</td>\n",
              "      <td>4.3</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>125306 rows × 160 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "        Unnamed: 0     ID answered_all  ...       con SPS_avg Scale_UCLA_TRI_avg\n",
              "0                0   4069           No  ...  5.666667     NaN                NaN\n",
              "1                1   4460           No  ...       NaN     NaN                NaN\n",
              "2                2   9383           No  ...       NaN     NaN                NaN\n",
              "3                3  10249           No  ...       NaN     NaN                NaN\n",
              "4                4  12234          Yes  ...  5.666667     4.6                NaN\n",
              "...            ...    ...          ...  ...       ...     ...                ...\n",
              "125301      125301  68759           No  ...  4.000000     5.6                NaN\n",
              "125302      125302  45062           No  ...       NaN     NaN                NaN\n",
              "125303      125303  46177           No  ...  5.333333     NaN                NaN\n",
              "125304      125304  73176           No  ...  5.666667     NaN                NaN\n",
              "125305      125305  76696           No  ...  3.500000     4.3                NaN\n",
              "\n",
              "[125306 rows x 160 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "8h4TvuJRjH7H",
        "outputId": "30693d91-7a75-4905-fe1b-af278914e2ee"
      },
      "source": [
        "pd.DataFrame(data.columns).head(160)"
      ],
      "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>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Unnamed: 0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>ID</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>answered_all</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Duration..in.seconds.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>RecordedDate</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>UserLanguage</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Dem_age</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Dem_gender</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Dem_edu</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Dem_edu_mom</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Dem_employment</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Country</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Country Income Level</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Region</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Food_insecurity_2019</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>Poverty_ratio_2017_2019</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>Maternal_mortality_ratio_2017</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>Dem_Expat</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>Dem_state</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>Dem_maritalstatus</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>Dem_dependents</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>Dem_riskgroup</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>Dem_isolation</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>Dem_isolation_adults</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>Dem_isolation_kids</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>AD_gain</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>AD_loss</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>AD_check</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>Scale_PSS10_UCLA_1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>Scale_PSS10_UCLA_2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>Scale_PSS10_UCLA_3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>Scale_PSS10_UCLA_4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>Scale_PSS10_UCLA_5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>Scale_PSS10_UCLA_6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>Scale_PSS10_UCLA_7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>Scale_PSS10_UCLA_8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>Scale_PSS10_UCLA_9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>Scale_PSS10_UCLA_10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>Scale_SLON_1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>Scale_SLON_2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>Scale_SLON_3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>OECD_people_1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>OECD_people_2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>OECD_insititutions_1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>OECD_insititutions_2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>OECD_insititutions_3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>OECD_insititutions_4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>OECD_insititutions_5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>OECD_insititutions_6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>Corona_concerns_1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50</th>\n",
              "      <td>Corona_concerns_2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>51</th>\n",
              "      <td>Corona_concerns_3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>52</th>\n",
              "      <td>Corona_concerns_4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>53</th>\n",
              "      <td>Corona_concerns_5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>54</th>\n",
              "      <td>Trust_countrymeasure</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>55</th>\n",
              "      <td>Compliance_1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>56</th>\n",
              "      <td>Compliance_2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>57</th>\n",
              "      <td>Compliance_3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>58</th>\n",
              "      <td>Compliance_4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59</th>\n",
              "      <td>Compliance_5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>60</th>\n",
              "      <td>Compliance_6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>61</th>\n",
              "      <td>born_92</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>62</th>\n",
              "      <td>experience_war</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>63</th>\n",
              "      <td>experience_war_TXT</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>64</th>\n",
              "      <td>war_injury</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>65</th>\n",
              "      <td>loss_during_war</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>66</th>\n",
              "      <td>time_spent_in_war</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>67</th>\n",
              "      <td>time_spent_in_war_TXT</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>68</th>\n",
              "      <td>Scale_UCLA_TRI_1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>69</th>\n",
              "      <td>Scale_UCLA_TRI_2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>70</th>\n",
              "      <td>Scale_UCLA_TRI_3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>71</th>\n",
              "      <td>Scale_UCLA_TRI_4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>72</th>\n",
              "      <td>PS_PTSD_1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>73</th>\n",
              "      <td>PS_PTSD_2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>74</th>\n",
              "      <td>PS_PTSD_3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75</th>\n",
              "      <td>PS_PTSD_4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>76</th>\n",
              "      <td>PS_PTSD_5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>77</th>\n",
              "      <td>BFF_15_1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>78</th>\n",
              "      <td>BFF_15_2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>79</th>\n",
              "      <td>BFF_15_3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>80</th>\n",
              "      <td>BFF_15_4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>81</th>\n",
              "      <td>BFF_15_5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>82</th>\n",
              "      <td>BFF_15_6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>83</th>\n",
              "      <td>BFF_15_7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>84</th>\n",
              "      <td>BFF_15_8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>85</th>\n",
              "      <td>BFF_15_9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>86</th>\n",
              "      <td>BFF_15_10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>87</th>\n",
              "      <td>BFF_15_11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>88</th>\n",
              "      <td>BFF_15_12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>89</th>\n",
              "      <td>BFF_15_13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>90</th>\n",
              "      <td>BFF_15_14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>91</th>\n",
              "      <td>BFF_15_15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>92</th>\n",
              "      <td>Expl_Distress_1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>93</th>\n",
              "      <td>Expl_Distress_2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>94</th>\n",
              "      <td>Expl_Distress_3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>95</th>\n",
              "      <td>Expl_Distress_4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>96</th>\n",
              "      <td>Expl_Distress_5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>97</th>\n",
              "      <td>Expl_Distress_6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>98</th>\n",
              "      <td>Expl_Distress_7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>99</th>\n",
              "      <td>Expl_Distress_8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>100</th>\n",
              "      <td>Expl_Distress_9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>101</th>\n",
              "      <td>Expl_Distress_10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>102</th>\n",
              "      <td>Expl_Distress_11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>103</th>\n",
              "      <td>Expl_Distress_12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>104</th>\n",
              "      <td>Expl_Distress_13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>105</th>\n",
              "      <td>Expl_Distress_14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>106</th>\n",
              "      <td>Expl_Distress_15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>107</th>\n",
              "      <td>Expl_Distress_16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>108</th>\n",
              "      <td>Expl_Distress_17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>109</th>\n",
              "      <td>Expl_Distress_18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>110</th>\n",
              "      <td>Expl_Distress_19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>111</th>\n",
              "      <td>Expl_Distress_20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>112</th>\n",
              "      <td>Expl_Distress_21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>113</th>\n",
              "      <td>Expl_Distress_22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>114</th>\n",
              "      <td>Expl_Distress_23</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>115</th>\n",
              "      <td>Expl_Distress_24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>116</th>\n",
              "      <td>Expl_Distress_txt</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>117</th>\n",
              "      <td>SPS_1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>118</th>\n",
              "      <td>SPS_2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>119</th>\n",
              "      <td>SPS_3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>120</th>\n",
              "      <td>SPS_4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>121</th>\n",
              "      <td>SPS_5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>122</th>\n",
              "      <td>SPS_6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>123</th>\n",
              "      <td>SPS_7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>124</th>\n",
              "      <td>SPS_8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>125</th>\n",
              "      <td>SPS_9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>126</th>\n",
              "      <td>SPS_10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>127</th>\n",
              "      <td>Expl_Coping_1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>128</th>\n",
              "      <td>Expl_Coping_2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>129</th>\n",
              "      <td>Expl_Coping_3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>130</th>\n",
              "      <td>Expl_Coping_4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>131</th>\n",
              "      <td>Expl_Coping_5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>132</th>\n",
              "      <td>Expl_Coping_6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>133</th>\n",
              "      <td>Expl_Coping_7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>134</th>\n",
              "      <td>Expl_Coping_8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>135</th>\n",
              "      <td>Expl_Coping_9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>136</th>\n",
              "      <td>Expl_Coping_10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>137</th>\n",
              "      <td>Expl_Coping_11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>138</th>\n",
              "      <td>Expl_Coping_12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>139</th>\n",
              "      <td>Expl_Coping_13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>140</th>\n",
              "      <td>Expl_Coping_14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>141</th>\n",
              "      <td>Expl_Coping_15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>142</th>\n",
              "      <td>Expl_Coping_16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>143</th>\n",
              "      <td>Expl_coping_txt</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>144</th>\n",
              "      <td>Expl_media_1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>145</th>\n",
              "      <td>Expl_media_2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>146</th>\n",
              "      <td>Expl_media_3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>147</th>\n",
              "      <td>Expl_media_4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>148</th>\n",
              "      <td>Expl_media_5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>149</th>\n",
              "      <td>Expl_media_6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>150</th>\n",
              "      <td>Final_open</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>151</th>\n",
              "      <td>PSS10_avg</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>152</th>\n",
              "      <td>SLON3_avg</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>153</th>\n",
              "      <td>neu</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>154</th>\n",
              "      <td>ext</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>155</th>\n",
              "      <td>ope</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>156</th>\n",
              "      <td>agr</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>157</th>\n",
              "      <td>con</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>158</th>\n",
              "      <td>SPS_avg</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>159</th>\n",
              "      <td>Scale_UCLA_TRI_avg</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                 0\n",
              "0                       Unnamed: 0\n",
              "1                               ID\n",
              "2                     answered_all\n",
              "3            Duration..in.seconds.\n",
              "4                     RecordedDate\n",
              "5                     UserLanguage\n",
              "6                          Dem_age\n",
              "7                       Dem_gender\n",
              "8                          Dem_edu\n",
              "9                      Dem_edu_mom\n",
              "10                  Dem_employment\n",
              "11                         Country\n",
              "12            Country Income Level\n",
              "13                          Region\n",
              "14            Food_insecurity_2019\n",
              "15         Poverty_ratio_2017_2019\n",
              "16   Maternal_mortality_ratio_2017\n",
              "17                       Dem_Expat\n",
              "18                       Dem_state\n",
              "19               Dem_maritalstatus\n",
              "20                  Dem_dependents\n",
              "21                   Dem_riskgroup\n",
              "22                   Dem_isolation\n",
              "23            Dem_isolation_adults\n",
              "24              Dem_isolation_kids\n",
              "25                         AD_gain\n",
              "26                         AD_loss\n",
              "27                        AD_check\n",
              "28              Scale_PSS10_UCLA_1\n",
              "29              Scale_PSS10_UCLA_2\n",
              "30              Scale_PSS10_UCLA_3\n",
              "31              Scale_PSS10_UCLA_4\n",
              "32              Scale_PSS10_UCLA_5\n",
              "33              Scale_PSS10_UCLA_6\n",
              "34              Scale_PSS10_UCLA_7\n",
              "35              Scale_PSS10_UCLA_8\n",
              "36              Scale_PSS10_UCLA_9\n",
              "37             Scale_PSS10_UCLA_10\n",
              "38                    Scale_SLON_1\n",
              "39                    Scale_SLON_2\n",
              "40                    Scale_SLON_3\n",
              "41                   OECD_people_1\n",
              "42                   OECD_people_2\n",
              "43            OECD_insititutions_1\n",
              "44            OECD_insititutions_2\n",
              "45            OECD_insititutions_3\n",
              "46            OECD_insititutions_4\n",
              "47            OECD_insititutions_5\n",
              "48            OECD_insititutions_6\n",
              "49               Corona_concerns_1\n",
              "50               Corona_concerns_2\n",
              "51               Corona_concerns_3\n",
              "52               Corona_concerns_4\n",
              "53               Corona_concerns_5\n",
              "54            Trust_countrymeasure\n",
              "55                    Compliance_1\n",
              "56                    Compliance_2\n",
              "57                    Compliance_3\n",
              "58                    Compliance_4\n",
              "59                    Compliance_5\n",
              "60                    Compliance_6\n",
              "61                         born_92\n",
              "62                  experience_war\n",
              "63              experience_war_TXT\n",
              "64                      war_injury\n",
              "65                 loss_during_war\n",
              "66               time_spent_in_war\n",
              "67           time_spent_in_war_TXT\n",
              "68                Scale_UCLA_TRI_1\n",
              "69                Scale_UCLA_TRI_2\n",
              "70                Scale_UCLA_TRI_3\n",
              "71                Scale_UCLA_TRI_4\n",
              "72                       PS_PTSD_1\n",
              "73                       PS_PTSD_2\n",
              "74                       PS_PTSD_3\n",
              "75                       PS_PTSD_4\n",
              "76                       PS_PTSD_5\n",
              "77                        BFF_15_1\n",
              "78                        BFF_15_2\n",
              "79                        BFF_15_3\n",
              "80                        BFF_15_4\n",
              "81                        BFF_15_5\n",
              "82                        BFF_15_6\n",
              "83                        BFF_15_7\n",
              "84                        BFF_15_8\n",
              "85                        BFF_15_9\n",
              "86                       BFF_15_10\n",
              "87                       BFF_15_11\n",
              "88                       BFF_15_12\n",
              "89                       BFF_15_13\n",
              "90                       BFF_15_14\n",
              "91                       BFF_15_15\n",
              "92                 Expl_Distress_1\n",
              "93                 Expl_Distress_2\n",
              "94                 Expl_Distress_3\n",
              "95                 Expl_Distress_4\n",
              "96                 Expl_Distress_5\n",
              "97                 Expl_Distress_6\n",
              "98                 Expl_Distress_7\n",
              "99                 Expl_Distress_8\n",
              "100                Expl_Distress_9\n",
              "101               Expl_Distress_10\n",
              "102               Expl_Distress_11\n",
              "103               Expl_Distress_12\n",
              "104               Expl_Distress_13\n",
              "105               Expl_Distress_14\n",
              "106               Expl_Distress_15\n",
              "107               Expl_Distress_16\n",
              "108               Expl_Distress_17\n",
              "109               Expl_Distress_18\n",
              "110               Expl_Distress_19\n",
              "111               Expl_Distress_20\n",
              "112               Expl_Distress_21\n",
              "113               Expl_Distress_22\n",
              "114               Expl_Distress_23\n",
              "115               Expl_Distress_24\n",
              "116              Expl_Distress_txt\n",
              "117                          SPS_1\n",
              "118                          SPS_2\n",
              "119                          SPS_3\n",
              "120                          SPS_4\n",
              "121                          SPS_5\n",
              "122                          SPS_6\n",
              "123                          SPS_7\n",
              "124                          SPS_8\n",
              "125                          SPS_9\n",
              "126                         SPS_10\n",
              "127                  Expl_Coping_1\n",
              "128                  Expl_Coping_2\n",
              "129                  Expl_Coping_3\n",
              "130                  Expl_Coping_4\n",
              "131                  Expl_Coping_5\n",
              "132                  Expl_Coping_6\n",
              "133                  Expl_Coping_7\n",
              "134                  Expl_Coping_8\n",
              "135                  Expl_Coping_9\n",
              "136                 Expl_Coping_10\n",
              "137                 Expl_Coping_11\n",
              "138                 Expl_Coping_12\n",
              "139                 Expl_Coping_13\n",
              "140                 Expl_Coping_14\n",
              "141                 Expl_Coping_15\n",
              "142                 Expl_Coping_16\n",
              "143                Expl_coping_txt\n",
              "144                   Expl_media_1\n",
              "145                   Expl_media_2\n",
              "146                   Expl_media_3\n",
              "147                   Expl_media_4\n",
              "148                   Expl_media_5\n",
              "149                   Expl_media_6\n",
              "150                     Final_open\n",
              "151                      PSS10_avg\n",
              "152                      SLON3_avg\n",
              "153                            neu\n",
              "154                            ext\n",
              "155                            ope\n",
              "156                            agr\n",
              "157                            con\n",
              "158                        SPS_avg\n",
              "159             Scale_UCLA_TRI_avg"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VuIhKokqqUsD",
        "outputId": "f3bc01f2-3007-4897-e4ef-6714e291e60a"
      },
      "source": [
        "data[\"Dem_isolation\"].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Life carries on with minor changes                  70102\n",
              "Isolated                                            46444\n",
              "Life carries on as usual                             6784\n",
              "Isolated in medical facility of similar location      180\n",
              "1                                                      13\n",
              "Name: Dem_isolation, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g-GOn5R5s5sQ",
        "outputId": "40f1bf7b-db80-4a49-edfa-2c4c83d4f1fc"
      },
      "source": [
        "data[\"Country Income Level\"].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "High Income            90376\n",
              "Upper Middle Income    29948\n",
              "Lower Middle Income     4038\n",
              "Low Income               165\n",
              "Not Classified            11\n",
              "Name: Country Income Level, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 496
        },
        "id": "1Z46SavLq3op",
        "outputId": "31feb0cd-8302-4e15-d71b-473ac72a9f60"
      },
      "source": [
        "data[\"Dem_isolation\"][data[\"Country Income Level\"]==\"High Income\"].value_counts().plot(kind=\"bar\",alpha=0.5,color=\"b\")\n",
        "data[\"Dem_isolation\"][data[\"Country Income Level\"]==\"Upper Middle Income\"].value_counts().plot(kind=\"bar\",alpha=0.5,color=\"g\")\n",
        "data[\"Dem_isolation\"][(data[\"Country Income Level\"]==\"Low Income\") | (data[\"Country Income Level\"]==\"Lower Middle Income\")].value_counts().plot(kind=\"bar\",alpha=0.5,color=\"r\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fca10f58f10>"
            ]
          },
          "metadata": {},
          "execution_count": 7
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 855
        },
        "id": "Qs-Qk_Wgsul7",
        "outputId": "1bff3f25-899f-4d54-e140-da3044f048f5"
      },
      "source": [
        "data.sort_values(['Scale_PSS10_UCLA_3'],ascending=False).groupby([\"Country Income Level\",\"Scale_PSS10_UCLA_3\"])['Scale_PSS10_UCLA_3'].count().to_frame()"
      ],
      "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></th>\n",
              "      <th>Scale_PSS10_UCLA_3</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Country Income Level</th>\n",
              "      <th>Scale_PSS10_UCLA_3</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"5\" valign=\"top\">High Income</th>\n",
              "      <th>1.0</th>\n",
              "      <td>7951</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2.0</th>\n",
              "      <td>18599</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3.0</th>\n",
              "      <td>28835</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4.0</th>\n",
              "      <td>19193</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5.0</th>\n",
              "      <td>9243</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"5\" valign=\"top\">Low Income</th>\n",
              "      <th>1.0</th>\n",
              "      <td>14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2.0</th>\n",
              "      <td>22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3.0</th>\n",
              "      <td>54</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4.0</th>\n",
              "      <td>27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5.0</th>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"5\" valign=\"top\">Lower Middle Income</th>\n",
              "      <th>1.0</th>\n",
              "      <td>272</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2.0</th>\n",
              "      <td>524</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3.0</th>\n",
              "      <td>1488</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4.0</th>\n",
              "      <td>917</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5.0</th>\n",
              "      <td>376</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"5\" valign=\"top\">Not Classified</th>\n",
              "      <th>1.0</th>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2.0</th>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3.0</th>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4.0</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5.0</th>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"5\" valign=\"top\">Upper Middle Income</th>\n",
              "      <th>1.0</th>\n",
              "      <td>1534</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2.0</th>\n",
              "      <td>4241</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3.0</th>\n",
              "      <td>10011</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4.0</th>\n",
              "      <td>6731</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5.0</th>\n",
              "      <td>4660</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                         Scale_PSS10_UCLA_3\n",
              "Country Income Level Scale_PSS10_UCLA_3                    \n",
              "High Income          1.0                               7951\n",
              "                     2.0                              18599\n",
              "                     3.0                              28835\n",
              "                     4.0                              19193\n",
              "                     5.0                               9243\n",
              "Low Income           1.0                                 14\n",
              "                     2.0                                 22\n",
              "                     3.0                                 54\n",
              "                     4.0                                 27\n",
              "                     5.0                                 20\n",
              "Lower Middle Income  1.0                                272\n",
              "                     2.0                                524\n",
              "                     3.0                               1488\n",
              "                     4.0                                917\n",
              "                     5.0                                376\n",
              "Not Classified       1.0                                  2\n",
              "                     2.0                                  2\n",
              "                     3.0                                  3\n",
              "                     4.0                                  1\n",
              "                     5.0                                  3\n",
              "Upper Middle Income  1.0                               1534\n",
              "                     2.0                               4241\n",
              "                     3.0                              10011\n",
              "                     4.0                               6731\n",
              "                     5.0                               4660"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 390
        },
        "id": "pfrBSKEEjGOc",
        "outputId": "9a3d05ee-3263-4cea-e79c-d2332a1cfc26"
      },
      "source": [
        "c_datapoint_summary = pd.DataFrame(data.groupby(['Country']).count()['ID'])\n",
        "c_datapoint_summary.head(10)"
      ],
      "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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              "    }\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>ID</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Country</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Afghanistan</th>\n",
              "      <td>114</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Albania</th>\n",
              "      <td>79</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Algeria</th>\n",
              "      <td>38</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Andorra</th>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Angola</th>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Antigua and Barbuda</th>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Argentina</th>\n",
              "      <td>5923</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Armenia</th>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Australia</th>\n",
              "      <td>327</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Austria</th>\n",
              "      <td>319</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                       ID\n",
              "Country                  \n",
              "Afghanistan           114\n",
              "Albania                79\n",
              "Algeria                38\n",
              "Andorra                15\n",
              "Angola                 11\n",
              "Antigua and Barbuda     5\n",
              "Argentina            5923\n",
              "Armenia                10\n",
              "Australia             327\n",
              "Austria               319"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 390
        },
        "id": "3pqw3OTijqYv",
        "outputId": "3907ea02-65a2-4025-c0ad-ede468af0111"
      },
      "source": [
        "c_allanswer_summary = pd.DataFrame(data[data['answered_all'] == 'Yes'].groupby(['Country']).count()['ID'])\n",
        "c_allanswer_summary.head(10)"
      ],
      "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>ID</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Country</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Afghanistan</th>\n",
              "      <td>25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Albania</th>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Algeria</th>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Andorra</th>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Angola</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Antigua and Barbuda</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Argentina</th>\n",
              "      <td>2074</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Armenia</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Australia</th>\n",
              "      <td>173</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Austria</th>\n",
              "      <td>132</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                       ID\n",
              "Country                  \n",
              "Afghanistan            25\n",
              "Albania                 9\n",
              "Algeria                10\n",
              "Andorra                 5\n",
              "Angola                  1\n",
              "Antigua and Barbuda     1\n",
              "Argentina            2074\n",
              "Armenia                 1\n",
              "Australia             173\n",
              "Austria               132"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "7nLOAuZukG3y",
        "outputId": "20689751-7f65-44bb-8779-5ddbaf9adb28"
      },
      "source": [
        "#Check the completeness of answer across countries\n",
        "c_complete_summary = pd.merge(c_datapoint_summary, c_allanswer_summary, on = 'Country', how = 'left')\n",
        "c_complete_summary = c_complete_summary.fillna(0)\n",
        "c_complete_summary['Percentage_complete'] = c_complete_summary['ID_y']/c_complete_summary['ID_x']\n",
        "c_complete_summary  "
      ],
      "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>ID_x</th>\n",
              "      <th>ID_y</th>\n",
              "      <th>Percentage_complete</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Country</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Afghanistan</th>\n",
              "      <td>114</td>\n",
              "      <td>25.0</td>\n",
              "      <td>0.219298</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Albania</th>\n",
              "      <td>79</td>\n",
              "      <td>9.0</td>\n",
              "      <td>0.113924</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Algeria</th>\n",
              "      <td>38</td>\n",
              "      <td>10.0</td>\n",
              "      <td>0.263158</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Andorra</th>\n",
              "      <td>15</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0.333333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Angola</th>\n",
              "      <td>11</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.090909</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Antigua and Barbuda</th>\n",
              "      <td>5</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.200000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Argentina</th>\n",
              "      <td>5923</td>\n",
              "      <td>2074.0</td>\n",
              "      <td>0.350160</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Armenia</th>\n",
              "      <td>10</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.100000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Australia</th>\n",
              "      <td>327</td>\n",
              "      <td>173.0</td>\n",
              "      <td>0.529052</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Austria</th>\n",
              "      <td>319</td>\n",
              "      <td>132.0</td>\n",
              "      <td>0.413793</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Azerbaijan</th>\n",
              "      <td>15</td>\n",
              "      <td>6.0</td>\n",
              "      <td>0.400000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Bahrain</th>\n",
              "      <td>7</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.142857</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Bangladesh</th>\n",
              "      <td>421</td>\n",
              "      <td>118.0</td>\n",
              "      <td>0.280285</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Barbados</th>\n",
              "      <td>3</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Belarus</th>\n",
              "      <td>13</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.307692</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Belgium</th>\n",
              "      <td>622</td>\n",
              "      <td>341.0</td>\n",
              "      <td>0.548232</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Belize</th>\n",
              "      <td>7</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.571429</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Benin</th>\n",
              "      <td>2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Bhutan</th>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Bolivia</th>\n",
              "      <td>7</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.571429</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Bosnia and Herzegovina</th>\n",
              "      <td>1288</td>\n",
              "      <td>334.0</td>\n",
              "      <td>0.259317</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Brazil</th>\n",
              "      <td>731</td>\n",
              "      <td>356.0</td>\n",
              "      <td>0.487004</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Brunei</th>\n",
              "      <td>8</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0.625000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Bulgaria</th>\n",
              "      <td>4785</td>\n",
              "      <td>1859.0</td>\n",
              "      <td>0.388506</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Burkina Faso</th>\n",
              "      <td>4</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Burundi</th>\n",
              "      <td>2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Cabo Verde</th>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Cambodia</th>\n",
              "      <td>3</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.666667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Cameroon</th>\n",
              "      <td>3</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Canada</th>\n",
              "      <td>470</td>\n",
              "      <td>259.0</td>\n",
              "      <td>0.551064</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Chile</th>\n",
              "      <td>72</td>\n",
              "      <td>27.0</td>\n",
              "      <td>0.375000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>China</th>\n",
              "      <td>74</td>\n",
              "      <td>20.0</td>\n",
              "      <td>0.270270</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Colombia</th>\n",
              "      <td>195</td>\n",
              "      <td>93.0</td>\n",
              "      <td>0.476923</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Comoros</th>\n",
              "      <td>2</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Congo, Democratic Republic of the</th>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Congo, Republic of the</th>\n",
              "      <td>2</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Costa Rica</th>\n",
              "      <td>26</td>\n",
              "      <td>16.0</td>\n",
              "      <td>0.615385</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Croatia</th>\n",
              "      <td>2965</td>\n",
              "      <td>906.0</td>\n",
              "      <td>0.305565</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Cuba</th>\n",
              "      <td>3</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Cyprus</th>\n",
              "      <td>52</td>\n",
              "      <td>22.0</td>\n",
              "      <td>0.423077</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Czech Republic</th>\n",
              "      <td>1995</td>\n",
              "      <td>465.0</td>\n",
              "      <td>0.233083</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Côte d’Ivoire</th>\n",
              "      <td>6</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Denmark</th>\n",
              "      <td>10891</td>\n",
              "      <td>4898.0</td>\n",
              "      <td>0.449729</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Djibouti</th>\n",
              "      <td>4</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Dominica</th>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Dominican Republic</th>\n",
              "      <td>4</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>East Timor (Timor-Leste)</th>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Ecuador</th>\n",
              "      <td>34</td>\n",
              "      <td>13.0</td>\n",
              "      <td>0.382353</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Egypt</th>\n",
              "      <td>74</td>\n",
              "      <td>34.0</td>\n",
              "      <td>0.459459</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>El Salvador</th>\n",
              "      <td>20</td>\n",
              "      <td>10.0</td>\n",
              "      <td>0.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Eritrea</th>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Estonia</th>\n",
              "      <td>39</td>\n",
              "      <td>18.0</td>\n",
              "      <td>0.461538</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Ethiopia</th>\n",
              "      <td>2</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Fiji</th>\n",
              "      <td>5</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.600000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Finland</th>\n",
              "      <td>22933</td>\n",
              "      <td>10533.0</td>\n",
              "      <td>0.459294</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>France</th>\n",
              "      <td>13475</td>\n",
              "      <td>7590.0</td>\n",
              "      <td>0.563265</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Gabon</th>\n",
              "      <td>5</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.600000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Georgia</th>\n",
              "      <td>19</td>\n",
              "      <td>6.0</td>\n",
              "      <td>0.315789</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Germany</th>\n",
              "      <td>1443</td>\n",
              "      <td>688.0</td>\n",
              "      <td>0.476784</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Ghana</th>\n",
              "      <td>9</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.111111</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Greece</th>\n",
              "      <td>642</td>\n",
              "      <td>262.0</td>\n",
              "      <td>0.408100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Grenada</th>\n",
              "      <td>25</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.160000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Guatemala</th>\n",
              "      <td>35</td>\n",
              "      <td>17.0</td>\n",
              "      <td>0.485714</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Guinea</th>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Guinea-Bissau</th>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Guyana</th>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Haiti</th>\n",
              "      <td>4</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.250000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Honduras</th>\n",
              "      <td>11</td>\n",
              "      <td>6.0</td>\n",
              "      <td>0.545455</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Hungary</th>\n",
              "      <td>1438</td>\n",
              "      <td>472.0</td>\n",
              "      <td>0.328234</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Iceland</th>\n",
              "      <td>25</td>\n",
              "      <td>7.0</td>\n",
              "      <td>0.280000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>India</th>\n",
              "      <td>72</td>\n",
              "      <td>27.0</td>\n",
              "      <td>0.375000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Indonesia</th>\n",
              "      <td>1569</td>\n",
              "      <td>521.0</td>\n",
              "      <td>0.332059</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Iran</th>\n",
              "      <td>8</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0.625000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Iraq</th>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Ireland</th>\n",
              "      <td>216</td>\n",
              "      <td>98.0</td>\n",
              "      <td>0.453704</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Israel</th>\n",
              "      <td>24</td>\n",
              "      <td>9.0</td>\n",
              "      <td>0.375000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Italy</th>\n",
              "      <td>1749</td>\n",
              "      <td>562.0</td>\n",
              "      <td>0.321326</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Jamaica</th>\n",
              "      <td>2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Japan</th>\n",
              "      <td>5072</td>\n",
              "      <td>2693.0</td>\n",
              "      <td>0.530954</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Jordan</th>\n",
              "      <td>18</td>\n",
              "      <td>11.0</td>\n",
              "      <td>0.611111</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Kazakhstan</th>\n",
              "      <td>4</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Kenya</th>\n",
              "      <td>15</td>\n",
              "      <td>7.0</td>\n",
              "      <td>0.466667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Kiribati</th>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Korea, North</th>\n",
              "      <td>2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Korea, South</th>\n",
              "      <td>487</td>\n",
              "      <td>59.0</td>\n",
              "      <td>0.121150</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Kosovo</th>\n",
              "      <td>2707</td>\n",
              "      <td>664.0</td>\n",
              "      <td>0.245290</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Kuwait</th>\n",
              "      <td>4</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.250000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Kyrgyzstan</th>\n",
              "      <td>35</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.085714</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Laos</th>\n",
              "      <td>5</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.600000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Latvia</th>\n",
              "      <td>22</td>\n",
              "      <td>6.0</td>\n",
              "      <td>0.272727</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Lebanon</th>\n",
              "      <td>10</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.400000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Lesotho</th>\n",
              "      <td>9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.333333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Libya</th>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Liechtenstein</th>\n",
              "      <td>5</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.200000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Lithuania</th>\n",
              "      <td>8255</td>\n",
              "      <td>3030.0</td>\n",
              "      <td>0.367050</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Luxembourg</th>\n",
              "      <td>62</td>\n",
              "      <td>29.0</td>\n",
              "      <td>0.467742</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Madagascar</th>\n",
              "      <td>4</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.250000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Malawi</th>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Malaysia</th>\n",
              "      <td>567</td>\n",
              "      <td>257.0</td>\n",
              "      <td>0.453263</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Maldives</th>\n",
              "      <td>4</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mali</th>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Malta</th>\n",
              "      <td>22</td>\n",
              "      <td>10.0</td>\n",
              "      <td>0.454545</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mauritania</th>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mauritius</th>\n",
              "      <td>3</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.666667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mexico</th>\n",
              "      <td>9169</td>\n",
              "      <td>4437.0</td>\n",
              "      <td>0.483913</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Micronesia, Federated States of</th>\n",
              "      <td>3</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.333333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Moldova</th>\n",
              "      <td>3</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.333333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Monaco</th>\n",
              "      <td>12</td>\n",
              "      <td>6.0</td>\n",
              "      <td>0.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Montenegro</th>\n",
              "      <td>16</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0.312500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Morocco</th>\n",
              "      <td>44</td>\n",
              "      <td>25.0</td>\n",
              "      <td>0.568182</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mozambique</th>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Myanmar (Burma)</th>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Namibia</th>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Nauru</th>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Nepal</th>\n",
              "      <td>7</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.428571</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Netherlands</th>\n",
              "      <td>1433</td>\n",
              "      <td>569.0</td>\n",
              "      <td>0.397069</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>New Zealand</th>\n",
              "      <td>119</td>\n",
              "      <td>72.0</td>\n",
              "      <td>0.605042</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Nicaragua</th>\n",
              "      <td>10</td>\n",
              "      <td>6.0</td>\n",
              "      <td>0.600000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Niger</th>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Nigeria</th>\n",
              "      <td>19</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.105263</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>North Macedonia</th>\n",
              "      <td>63</td>\n",
              "      <td>14.0</td>\n",
              "      <td>0.222222</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Norway</th>\n",
              "      <td>172</td>\n",
              "      <td>82.0</td>\n",
              "      <td>0.476744</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Oman</th>\n",
              "      <td>4</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.750000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Pakistan</th>\n",
              "      <td>360</td>\n",
              "      <td>101.0</td>\n",
              "      <td>0.280556</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Panama</th>\n",
              "      <td>759</td>\n",
              "      <td>242.0</td>\n",
              "      <td>0.318841</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Paraguay</th>\n",
              "      <td>4</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.750000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Peru</th>\n",
              "      <td>40</td>\n",
              "      <td>24.0</td>\n",
              "      <td>0.600000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Philippines</th>\n",
              "      <td>570</td>\n",
              "      <td>185.0</td>\n",
              "      <td>0.324561</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Poland</th>\n",
              "      <td>3088</td>\n",
              "      <td>1234.0</td>\n",
              "      <td>0.399611</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Portugal</th>\n",
              "      <td>1067</td>\n",
              "      <td>403.0</td>\n",
              "      <td>0.377694</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Qatar</th>\n",
              "      <td>20</td>\n",
              "      <td>9.0</td>\n",
              "      <td>0.450000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Romania</th>\n",
              "      <td>282</td>\n",
              "      <td>115.0</td>\n",
              "      <td>0.407801</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Russia</th>\n",
              "      <td>132</td>\n",
              "      <td>64.0</td>\n",
              "      <td>0.484848</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Rwanda</th>\n",
              "      <td>2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Saint Lucia</th>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Saint Vincent and the Grenadines</th>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>San Marino</th>\n",
              "      <td>14</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.285714</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sao Tome and Principe</th>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Saudi Arabia</th>\n",
              "      <td>13</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0.384615</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Senegal</th>\n",
              "      <td>3</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.333333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Serbia</th>\n",
              "      <td>266</td>\n",
              "      <td>92.0</td>\n",
              "      <td>0.345865</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Seychelles</th>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Singapore</th>\n",
              "      <td>34</td>\n",
              "      <td>14.0</td>\n",
              "      <td>0.411765</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Slovakia</th>\n",
              "      <td>942</td>\n",
              "      <td>365.0</td>\n",
              "      <td>0.387473</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Slovenia</th>\n",
              "      <td>23</td>\n",
              "      <td>9.0</td>\n",
              "      <td>0.391304</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Solomon Islands</th>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Somalia</th>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>South Africa</th>\n",
              "      <td>135</td>\n",
              "      <td>63.0</td>\n",
              "      <td>0.466667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Spain</th>\n",
              "      <td>615</td>\n",
              "      <td>254.0</td>\n",
              "      <td>0.413008</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sri Lanka</th>\n",
              "      <td>3</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sudan</th>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sudan, South</th>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Suriname</th>\n",
              "      <td>3</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.333333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sweden</th>\n",
              "      <td>3055</td>\n",
              "      <td>1245.0</td>\n",
              "      <td>0.407529</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Switzerland</th>\n",
              "      <td>1188</td>\n",
              "      <td>592.0</td>\n",
              "      <td>0.498316</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Taiwan</th>\n",
              "      <td>2745</td>\n",
              "      <td>647.0</td>\n",
              "      <td>0.235701</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Tajikistan</th>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Tanzania</th>\n",
              "      <td>2</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Thailand</th>\n",
              "      <td>19</td>\n",
              "      <td>11.0</td>\n",
              "      <td>0.578947</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>The Bahamas</th>\n",
              "      <td>2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>The Gambia</th>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Trinidad and Tobago</th>\n",
              "      <td>6</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Tunisia</th>\n",
              "      <td>8</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.375000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Turkey</th>\n",
              "      <td>1199</td>\n",
              "      <td>336.0</td>\n",
              "      <td>0.280234</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Turkmenistan</th>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Uganda</th>\n",
              "      <td>17</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.235294</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Ukraine</th>\n",
              "      <td>35</td>\n",
              "      <td>13.0</td>\n",
              "      <td>0.371429</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>United Arab Emirates</th>\n",
              "      <td>56</td>\n",
              "      <td>25.0</td>\n",
              "      <td>0.446429</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>United Kingdom</th>\n",
              "      <td>1500</td>\n",
              "      <td>680.0</td>\n",
              "      <td>0.453333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>United States</th>\n",
              "      <td>2314</td>\n",
              "      <td>1235.0</td>\n",
              "      <td>0.533708</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Uruguay</th>\n",
              "      <td>5</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.600000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Uzbekistan</th>\n",
              "      <td>2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Venezuela</th>\n",
              "      <td>11</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0.454545</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Vietnam</th>\n",
              "      <td>173</td>\n",
              "      <td>95.0</td>\n",
              "      <td>0.549133</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Zambia</th>\n",
              "      <td>4</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Zimbabwe</th>\n",
              "      <td>4</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>other</th>\n",
              "      <td>223</td>\n",
              "      <td>48.0</td>\n",
              "      <td>0.215247</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                    ID_x     ID_y  Percentage_complete\n",
              "Country                                                               \n",
              "Afghanistan                          114     25.0             0.219298\n",
              "Albania                               79      9.0             0.113924\n",
              "Algeria                               38     10.0             0.263158\n",
              "Andorra                               15      5.0             0.333333\n",
              "Angola                                11      1.0             0.090909\n",
              "Antigua and Barbuda                    5      1.0             0.200000\n",
              "Argentina                           5923   2074.0             0.350160\n",
              "Armenia                               10      1.0             0.100000\n",
              "Australia                            327    173.0             0.529052\n",
              "Austria                              319    132.0             0.413793\n",
              "Azerbaijan                            15      6.0             0.400000\n",
              "Bahrain                                7      1.0             0.142857\n",
              "Bangladesh                           421    118.0             0.280285\n",
              "Barbados                               3      0.0             0.000000\n",
              "Belarus                               13      4.0             0.307692\n",
              "Belgium                              622    341.0             0.548232\n",
              "Belize                                 7      4.0             0.571429\n",
              "Benin                                  2      0.0             0.000000\n",
              "Bhutan                                 1      0.0             0.000000\n",
              "Bolivia                                7      4.0             0.571429\n",
              "Bosnia and Herzegovina              1288    334.0             0.259317\n",
              "Brazil                               731    356.0             0.487004\n",
              "Brunei                                 8      5.0             0.625000\n",
              "Bulgaria                            4785   1859.0             0.388506\n",
              "Burkina Faso                           4      2.0             0.500000\n",
              "Burundi                                2      0.0             0.000000\n",
              "Cabo Verde                             1      0.0             0.000000\n",
              "Cambodia                               3      2.0             0.666667\n",
              "Cameroon                               3      0.0             0.000000\n",
              "Canada                               470    259.0             0.551064\n",
              "Chile                                 72     27.0             0.375000\n",
              "China                                 74     20.0             0.270270\n",
              "Colombia                             195     93.0             0.476923\n",
              "Comoros                                2      1.0             0.500000\n",
              "Congo, Democratic Republic of the      1      0.0             0.000000\n",
              "Congo, Republic of the                 2      1.0             0.500000\n",
              "Costa Rica                            26     16.0             0.615385\n",
              "Croatia                             2965    906.0             0.305565\n",
              "Cuba                                   3      0.0             0.000000\n",
              "Cyprus                                52     22.0             0.423077\n",
              "Czech Republic                      1995    465.0             0.233083\n",
              "Côte d’Ivoire                          6      3.0             0.500000\n",
              "Denmark                            10891   4898.0             0.449729\n",
              "Djibouti                               4      2.0             0.500000\n",
              "Dominica                               1      0.0             0.000000\n",
              "Dominican Republic                     4      2.0             0.500000\n",
              "East Timor (Timor-Leste)               1      0.0             0.000000\n",
              "Ecuador                               34     13.0             0.382353\n",
              "Egypt                                 74     34.0             0.459459\n",
              "El Salvador                           20     10.0             0.500000\n",
              "Eritrea                                1      0.0             0.000000\n",
              "Estonia                               39     18.0             0.461538\n",
              "Ethiopia                               2      1.0             0.500000\n",
              "Fiji                                   5      3.0             0.600000\n",
              "Finland                            22933  10533.0             0.459294\n",
              "France                             13475   7590.0             0.563265\n",
              "Gabon                                  5      3.0             0.600000\n",
              "Georgia                               19      6.0             0.315789\n",
              "Germany                             1443    688.0             0.476784\n",
              "Ghana                                  9      1.0             0.111111\n",
              "Greece                               642    262.0             0.408100\n",
              "Grenada                               25      4.0             0.160000\n",
              "Guatemala                             35     17.0             0.485714\n",
              "Guinea                                 1      0.0             0.000000\n",
              "Guinea-Bissau                          1      1.0             1.000000\n",
              "Guyana                                 1      1.0             1.000000\n",
              "Haiti                                  4      1.0             0.250000\n",
              "Honduras                              11      6.0             0.545455\n",
              "Hungary                             1438    472.0             0.328234\n",
              "Iceland                               25      7.0             0.280000\n",
              "India                                 72     27.0             0.375000\n",
              "Indonesia                           1569    521.0             0.332059\n",
              "Iran                                   8      5.0             0.625000\n",
              "Iraq                                   1      0.0             0.000000\n",
              "Ireland                              216     98.0             0.453704\n",
              "Israel                                24      9.0             0.375000\n",
              "Italy                               1749    562.0             0.321326\n",
              "Jamaica                                2      0.0             0.000000\n",
              "Japan                               5072   2693.0             0.530954\n",
              "Jordan                                18     11.0             0.611111\n",
              "Kazakhstan                             4      2.0             0.500000\n",
              "Kenya                                 15      7.0             0.466667\n",
              "Kiribati                               1      1.0             1.000000\n",
              "Korea, North                           2      0.0             0.000000\n",
              "Korea, South                         487     59.0             0.121150\n",
              "Kosovo                              2707    664.0             0.245290\n",
              "Kuwait                                 4      1.0             0.250000\n",
              "Kyrgyzstan                            35      3.0             0.085714\n",
              "Laos                                   5      3.0             0.600000\n",
              "Latvia                                22      6.0             0.272727\n",
              "Lebanon                               10      4.0             0.400000\n",
              "Lesotho                                9      3.0             0.333333\n",
              "Libya                                  1      0.0             0.000000\n",
              "Liechtenstein                          5      1.0             0.200000\n",
              "Lithuania                           8255   3030.0             0.367050\n",
              "Luxembourg                            62     29.0             0.467742\n",
              "Madagascar                             4      1.0             0.250000\n",
              "Malawi                                 1      0.0             0.000000\n",
              "Malaysia                             567    257.0             0.453263\n",
              "Maldives                               4      2.0             0.500000\n",
              "Mali                                   1      0.0             0.000000\n",
              "Malta                                 22     10.0             0.454545\n",
              "Mauritania                             1      0.0             0.000000\n",
              "Mauritius                              3      2.0             0.666667\n",
              "Mexico                              9169   4437.0             0.483913\n",
              "Micronesia, Federated States of        3      1.0             0.333333\n",
              "Moldova                                3      1.0             0.333333\n",
              "Monaco                                12      6.0             0.500000\n",
              "Montenegro                            16      5.0             0.312500\n",
              "Morocco                               44     25.0             0.568182\n",
              "Mozambique                             1      1.0             1.000000\n",
              "Myanmar (Burma)                        1      0.0             0.000000\n",
              "Namibia                                1      1.0             1.000000\n",
              "Nauru                                  1      1.0             1.000000\n",
              "Nepal                                  7      3.0             0.428571\n",
              "Netherlands                         1433    569.0             0.397069\n",
              "New Zealand                          119     72.0             0.605042\n",
              "Nicaragua                             10      6.0             0.600000\n",
              "Niger                                  1      0.0             0.000000\n",
              "Nigeria                               19      2.0             0.105263\n",
              "North Macedonia                       63     14.0             0.222222\n",
              "Norway                               172     82.0             0.476744\n",
              "Oman                                   4      3.0             0.750000\n",
              "Pakistan                             360    101.0             0.280556\n",
              "Panama                               759    242.0             0.318841\n",
              "Paraguay                               4      3.0             0.750000\n",
              "Peru                                  40     24.0             0.600000\n",
              "Philippines                          570    185.0             0.324561\n",
              "Poland                              3088   1234.0             0.399611\n",
              "Portugal                            1067    403.0             0.377694\n",
              "Qatar                                 20      9.0             0.450000\n",
              "Romania                              282    115.0             0.407801\n",
              "Russia                               132     64.0             0.484848\n",
              "Rwanda                                 2      0.0             0.000000\n",
              "Saint Lucia                            1      0.0             0.000000\n",
              "Saint Vincent and the Grenadines       1      0.0             0.000000\n",
              "San Marino                            14      4.0             0.285714\n",
              "Sao Tome and Principe                  1      0.0             0.000000\n",
              "Saudi Arabia                          13      5.0             0.384615\n",
              "Senegal                                3      1.0             0.333333\n",
              "Serbia                               266     92.0             0.345865\n",
              "Seychelles                             1      1.0             1.000000\n",
              "Singapore                             34     14.0             0.411765\n",
              "Slovakia                             942    365.0             0.387473\n",
              "Slovenia                              23      9.0             0.391304\n",
              "Solomon Islands                        1      1.0             1.000000\n",
              "Somalia                                1      1.0             1.000000\n",
              "South Africa                         135     63.0             0.466667\n",
              "Spain                                615    254.0             0.413008\n",
              "Sri Lanka                              3      0.0             0.000000\n",
              "Sudan                                  1      0.0             0.000000\n",
              "Sudan, South                           1      0.0             0.000000\n",
              "Suriname                               3      1.0             0.333333\n",
              "Sweden                              3055   1245.0             0.407529\n",
              "Switzerland                         1188    592.0             0.498316\n",
              "Taiwan                              2745    647.0             0.235701\n",
              "Tajikistan                             1      0.0             0.000000\n",
              "Tanzania                               2      1.0             0.500000\n",
              "Thailand                              19     11.0             0.578947\n",
              "The Bahamas                            2      0.0             0.000000\n",
              "The Gambia                             1      1.0             1.000000\n",
              "Trinidad and Tobago                    6      3.0             0.500000\n",
              "Tunisia                                8      3.0             0.375000\n",
              "Turkey                              1199    336.0             0.280234\n",
              "Turkmenistan                           1      0.0             0.000000\n",
              "Uganda                                17      4.0             0.235294\n",
              "Ukraine                               35     13.0             0.371429\n",
              "United Arab Emirates                  56     25.0             0.446429\n",
              "United Kingdom                      1500    680.0             0.453333\n",
              "United States                       2314   1235.0             0.533708\n",
              "Uruguay                                5      3.0             0.600000\n",
              "Uzbekistan                             2      0.0             0.000000\n",
              "Venezuela                             11      5.0             0.454545\n",
              "Vietnam                              173     95.0             0.549133\n",
              "Zambia                                 4      0.0             0.000000\n",
              "Zimbabwe                               4      0.0             0.000000\n",
              "other                                223     48.0             0.215247"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vSxcsRBfkmh4"
      },
      "source": [
        "### Group by region\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UtxkpQaoT4wW",
        "outputId": "7c6abc9d-1965-4e08-8b1c-bb6e73a83e2e"
      },
      "source": [
        "data.groupby(['ID']).count()['answered_all'].sort_values(ascending = False)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ID\n",
              "125306    1\n",
              "41759     1\n",
              "41761     1\n",
              "41762     1\n",
              "41763     1\n",
              "         ..\n",
              "83542     1\n",
              "83543     1\n",
              "83544     1\n",
              "83545     1\n",
              "1         1\n",
              "Name: answered_all, Length: 125306, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        },
        "id": "yV66765llFJw",
        "outputId": "01a7b333-ad95-49c8-dc03-dc962bf5f3c1"
      },
      "source": [
        "r_datapoint_summary = pd.DataFrame(data.groupby(['Region']).count()['ID'])\n",
        "r_datapoint_summary"
      ],
      "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>ID</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Region</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>East Asia and Pacific</th>\n",
              "      <td>11787</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Europe and Central Asia</th>\n",
              "      <td>91227</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Latin America and Caribbean</th>\n",
              "      <td>17120</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Middle East and North Africa</th>\n",
              "      <td>356</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>North America</th>\n",
              "      <td>2784</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Other</th>\n",
              "      <td>223</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>South Asia</th>\n",
              "      <td>982</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sub-Saharan Africa</th>\n",
              "      <td>282</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Unrecognizable</th>\n",
              "      <td>545</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                 ID\n",
              "Region                             \n",
              "East Asia and Pacific         11787\n",
              "Europe and Central Asia       91227\n",
              "Latin America and Caribbean   17120\n",
              "Middle East and North Africa    356\n",
              "North America                  2784\n",
              "Other                           223\n",
              "South Asia                      982\n",
              "Sub-Saharan Africa              282\n",
              "Unrecognizable                  545"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 328
        },
        "id": "TkhiLccelL22",
        "outputId": "67db382a-8de3-462f-b66d-d579ed2819ff"
      },
      "source": [
        "r_allanswer_summary = pd.DataFrame(data[data['answered_all'] == 'Yes'].groupby(['Region']).count()['ID'])\n",
        "r_allanswer_summary"
      ],
      "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>ID</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Region</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>East Asia and Pacific</th>\n",
              "      <td>4764</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Europe and Central Asia</th>\n",
              "      <td>39036</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Latin America and Caribbean</th>\n",
              "      <td>7353</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Middle East and North Africa</th>\n",
              "      <td>157</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>North America</th>\n",
              "      <td>1494</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Other</th>\n",
              "      <td>48</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>South Asia</th>\n",
              "      <td>276</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sub-Saharan Africa</th>\n",
              "      <td>103</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                 ID\n",
              "Region                             \n",
              "East Asia and Pacific          4764\n",
              "Europe and Central Asia       39036\n",
              "Latin America and Caribbean    7353\n",
              "Middle East and North Africa    157\n",
              "North America                  1494\n",
              "Other                            48\n",
              "South Asia                      276\n",
              "Sub-Saharan Africa              103"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 328
        },
        "id": "YogWhc73lt9f",
        "outputId": "ca8ff376-2cb9-464f-f86e-eb720a715b20"
      },
      "source": [
        "r_complete_summary = pd.merge(r_datapoint_summary, r_allanswer_summary, on = 'Region', how = 'left').reset_index()\n",
        "r_complete_summary = r_complete_summary.fillna(0)\n",
        "r_complete_summary['Percentage_complete'] = r_complete_summary['ID_y']/r_complete_summary['ID_x']\n",
        "r_complete_summary  "
      ],
      "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>Region</th>\n",
              "      <th>ID_x</th>\n",
              "      <th>ID_y</th>\n",
              "      <th>Percentage_complete</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>East Asia and Pacific</td>\n",
              "      <td>11787</td>\n",
              "      <td>4764.0</td>\n",
              "      <td>0.404174</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Europe and Central Asia</td>\n",
              "      <td>91227</td>\n",
              "      <td>39036.0</td>\n",
              "      <td>0.427900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Latin America and Caribbean</td>\n",
              "      <td>17120</td>\n",
              "      <td>7353.0</td>\n",
              "      <td>0.429498</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Middle East and North Africa</td>\n",
              "      <td>356</td>\n",
              "      <td>157.0</td>\n",
              "      <td>0.441011</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>North America</td>\n",
              "      <td>2784</td>\n",
              "      <td>1494.0</td>\n",
              "      <td>0.536638</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Other</td>\n",
              "      <td>223</td>\n",
              "      <td>48.0</td>\n",
              "      <td>0.215247</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>South Asia</td>\n",
              "      <td>982</td>\n",
              "      <td>276.0</td>\n",
              "      <td>0.281059</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Sub-Saharan Africa</td>\n",
              "      <td>282</td>\n",
              "      <td>103.0</td>\n",
              "      <td>0.365248</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Unrecognizable</td>\n",
              "      <td>545</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                         Region   ID_x     ID_y  Percentage_complete\n",
              "0         East Asia and Pacific  11787   4764.0             0.404174\n",
              "1       Europe and Central Asia  91227  39036.0             0.427900\n",
              "2   Latin America and Caribbean  17120   7353.0             0.429498\n",
              "3  Middle East and North Africa    356    157.0             0.441011\n",
              "4                 North America   2784   1494.0             0.536638\n",
              "5                         Other    223     48.0             0.215247\n",
              "6                    South Asia    982    276.0             0.281059\n",
              "7            Sub-Saharan Africa    282    103.0             0.365248\n",
              "8                Unrecognizable    545      0.0             0.000000"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 589
        },
        "id": "DNBlzAbKl1qv",
        "outputId": "bd51ea78-9127-4910-fac2-f1ce35764a01"
      },
      "source": [
        "import seaborn as sns\n",
        "sns.set_theme(style = 'whitegrid')\n",
        "ax = sns.barplot(x = 'Region', y = 'Percentage_complete', data = r_complete_summary)\n",
        "ax.set_xticklabels(ax.get_xticklabels(), rotation = 90, horizontalalignment = 'right')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Text(0, 0, 'East Asia and Pacific'),\n",
              " Text(0, 0, 'Europe and Central Asia'),\n",
              " Text(0, 0, 'Latin America and Caribbean'),\n",
              " Text(0, 0, 'Middle East and North Africa'),\n",
              " Text(0, 0, 'North America'),\n",
              " Text(0, 0, 'Other'),\n",
              " Text(0, 0, 'South Asia'),\n",
              " Text(0, 0, 'Sub-Saharan Africa'),\n",
              " Text(0, 0, 'Unrecognizable')]"
            ]
          },
          "metadata": {},
          "execution_count": 51
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RfulK2KLnGsE",
        "outputId": "7a6bd10c-43cb-44cf-d01b-4d8718ffcb5f"
      },
      "source": [
        "#Language count\n",
        "data.groupby(['UserLanguage']).count()['ID'].sort_values(ascending = False)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "UserLanguage\n",
              "FI       22420\n",
              "FR       14464\n",
              "DA       10670\n",
              "SME      10318\n",
              "LT        8774\n",
              "EN        7637\n",
              "SAR       5950\n",
              "BG        5055\n",
              "JA        5035\n",
              "SV        3277\n",
              "PL        3257\n",
              "HR        3200\n",
              "SQI       3119\n",
              "ZH-T      3089\n",
              "CS        2001\n",
              "DE        1725\n",
              "IT        1715\n",
              "ID        1546\n",
              "HU        1538\n",
              "NL        1444\n",
              "BS        1277\n",
              "TR        1158\n",
              "PT        1086\n",
              "SK         882\n",
              "PT-BR      700\n",
              "EL         656\n",
              "SSP        513\n",
              "KO         501\n",
              "FILI       409\n",
              "BAN        398\n",
              "SR         271\n",
              "RU         252\n",
              "RO         227\n",
              "S-CO       179\n",
              "VI         175\n",
              "FA         103\n",
              "AR          95\n",
              "UR          84\n",
              "NE          33\n",
              "ZH-S        31\n",
              "SCU         11\n",
              "DFB          8\n",
              "HE           8\n",
              "AFR          6\n",
              "HI           5\n",
              "ZUL          3\n",
              "XHO          1\n",
              "Name: ID, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A34vJSe0oOiY",
        "outputId": "272e4c9b-5d25-40b4-a1ac-edcef4c15d80"
      },
      "source": [
        "data.columns"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Unnamed: 0', 'ID', 'answered_all', 'Duration..in.seconds.',\n",
              "       'RecordedDate', 'UserLanguage', 'Dem_age', 'Dem_gender', 'Dem_edu',\n",
              "       'Dem_edu_mom',\n",
              "       ...\n",
              "       'Final_open', 'PSS10_avg', 'SLON3_avg', 'neu', 'ext', 'ope', 'agr',\n",
              "       'con', 'SPS_avg', 'Scale_UCLA_TRI_avg'],\n",
              "      dtype='object', length=160)"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vMBIVNNVOyhD",
        "outputId": "231d226e-4c68-4b6e-986d-a13891b61852"
      },
      "source": [
        "data['Dem_edu'].unique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['College degree, bachelor, master', 'PhD/Doctorate',\n",
              "       'Some College, short continuing education or equivalent', 'None',\n",
              "       'Up to 9 years of school', nan, 'Up to 6 years of school',\n",
              "       'Up to 12 years of school', 'Uninformative response'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pRz-7_HqO8a9"
      },
      "source": [
        "data['Education_year'] =  data['Dem_edu']\n",
        "data.loc[data['Dem_edu']=='Uninformative response', 'Education_year'] = -999\n",
        "data.loc[data['Dem_edu']=='Up to 12 years of school', 'Education_year'] = 12\n",
        "data.loc[data['Dem_edu']=='Up to 6 years of school', 'Education_year'] = 6\n",
        "data.loc[data['Dem_edu']=='Up to 9 years of school', 'Education_year'] = 9\n",
        "data.loc[data['Dem_edu']=='None', 'Education_year'] = 0\n",
        "data.loc[data['Dem_edu']=='Some College, short continuing education or equivalent', 'Education_year'] = 14\n",
        "data.loc[data['Dem_edu']=='PhD/Doctorate', 'Education_year'] = 20\n",
        "data.loc[data['Dem_edu']=='College degree, bachelor, master', 'Education_year'] = 19\n",
        "data['Education_year'] = data['Education_year'].fillna(0)\n",
        "\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 328
        },
        "id": "sNARfdGnO6Mz",
        "outputId": "d31b815b-1008-4638-e34f-0f170a8f41a1"
      },
      "source": [
        "region_edu = pd.DataFrame(data[data['Education_year'] != -999].groupby(['Region'])['Education_year'].mean().round(2)).reset_index()\n",
        "region_edu"
      ],
      "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>Region</th>\n",
              "      <th>Education_year</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>East Asia and Pacific</td>\n",
              "      <td>16.65</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Europe and Central Asia</td>\n",
              "      <td>16.31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Latin America and Caribbean</td>\n",
              "      <td>17.09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Middle East and North Africa</td>\n",
              "      <td>17.65</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>North America</td>\n",
              "      <td>17.85</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Other</td>\n",
              "      <td>14.60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>South Asia</td>\n",
              "      <td>17.63</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Sub-Saharan Africa</td>\n",
              "      <td>17.18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Unrecognizable</td>\n",
              "      <td>12.05</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                         Region  Education_year\n",
              "0         East Asia and Pacific           16.65\n",
              "1       Europe and Central Asia           16.31\n",
              "2   Latin America and Caribbean           17.09\n",
              "3  Middle East and North Africa           17.65\n",
              "4                 North America           17.85\n",
              "5                         Other           14.60\n",
              "6                    South Asia           17.63\n",
              "7            Sub-Saharan Africa           17.18\n",
              "8                Unrecognizable           12.05"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 589
        },
        "id": "IaQJ1WrfQiRr",
        "outputId": "64460f3a-ee27-4c77-ce94-30729e268095"
      },
      "source": [
        "import seaborn as sns\n",
        "sns.set_theme(style = 'whitegrid')\n",
        "ax = sns.barplot(x = 'Region', y = 'Education_year', data = region_edu)\n",
        "ax.set_xticklabels(ax.get_xticklabels(), rotation = 90, horizontalalignment = 'right')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Text(0, 0, 'East Asia and Pacific'),\n",
              " Text(0, 0, 'Europe and Central Asia'),\n",
              " Text(0, 0, 'Latin America and Caribbean'),\n",
              " Text(0, 0, 'Middle East and North Africa'),\n",
              " Text(0, 0, 'North America'),\n",
              " Text(0, 0, 'Other'),\n",
              " Text(0, 0, 'South Asia'),\n",
              " Text(0, 0, 'Sub-Saharan Africa'),\n",
              " Text(0, 0, 'Unrecognizable')]"
            ]
          },
          "metadata": {},
          "execution_count": 25
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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fJCAgAMeOHcM///yDUaNGwcjICIcPH8aiRYswd+5chISEoFq1aggMDOS/Z9y4cfDy8kKLFi3g6uqKv/76Cz179gQATJkyBfXr11c7DyGEkM/3SRtKvX79GqdOnUJqaipMTU3RvXt3GBoafvT7FixYgAULFpQ4bmFhUeqcjg0bNvBfi8ViLF68+FOiEkIIKWcqTyT8448/4OjoiN27d+P27dvYvXs3HB0d8ccff5RnPkIIIQKl8h3ITz/9BD8/P/Tp04c/duTIEQQEBCA8PLxcwhFCCBEule9AHj16hN69eysc69WrF5KTk8s8FCGEEOFTuYA0bNgQhw8fVjh29OhR6swmhJAvlMpNWL6+vpg4cSJ27NiBOnXq4NmzZ3j8+DFCQ0PLMx8hhBCBUrmAtG3bFsePH8fp06fx8uVLdO/eHV27doWRkVF55iOEECJQnzSM19DQEK6uruWVhRBCiBb5YAEZM2YMNm3aBAAYOnRoqXue06xwQgj58nywgLi5ufFfDxo0qNzDEEII0R4fLCBFN4hq3LgxWrVqVeI1N27cKPtUhBBCBE/lYbyjRo1Sepw2dSKEkC/TRzvRZTIZOI5T+E8uOTmZ3xCKEELIl+WjBaRZs2Z853mzZs0UntPR0cHEiRPLJxkhhBBB+2gBOXnyJDiOw/Dhw/G///2PPy4SiWBsbIxKlSqVa0BCCCHC9NECUrduXQDAqVOnyj0MIYQQ7fFJEwlPnjyJq1evIiMjQ6EvJCgoqMyDEUIIETaVR2GtXbsWfn5+kMlkOHr0KIyMjHD+/HlUq1atPPMRQggRKJULSHh4ODZv3gxfX1/o6urC19cXoaGhePr0aXnmI4QQIlAqF5A3b97A0tISAKCrq4v8/Hy0bNkSV69eLbdwhBBChEvlPpAGDRrg7t27aNKkCZo0aYJdu3ahWrVqKu2JTggh5L9H5QIyY8YMZGZmAgBmz54Nb29v5OTkYOHChZ8V4OnTp5gyZQr/OCsrC9nZ2bhy5YrC69asWYOdO3eiVq1aAAqXl/fz8/uscxNCCFGfygWka9eu/NctW7bE8ePHyyRAvXr1cPDgQf7x0qVLIZVKlb7Wzc0NPj4+ZXJeQgghn0flPpDIyEgkJSUpHEtKSkJkZGSZhZFIJIiKisKAAQPK7N8khBBSPlS+A/nll19KFIvatWtj0qRJCsu+f464uDiYmpqiefPmSp8/fPgwzp8/DxMTE0ybNg1t2rT5pH8/MTGR/9rW1vazsn6Oa9euKT1OmRRpUyaAXS4hZgK06/dHmRR96JoqSuUCkp2dDX19fYVjBgYGePPmzacl+4Dw8PBS7z4GDx6MiRMnQldXFxcuXMDkyZNx5MgRVK9eXeV/38bGBhUrViyruGpjeWGUhjKphjKpToi5KJNq5Jny8vIUPngXp3ITloWFBWJjYxWOHT9+HBYWFmpGVJSamoqrV68q7EFSlImJCXR1dQEAHTt2hJmZGe7evVsm5yaEEPLpVL4DmT17NsaPH4+YmBjUr18fycnJuHTpEtavX18mQQ4cOICuXbuWekeRmpoKU1NTAMCtW7fw7NkzmJubl8m5CSGEfDqVC4idnR2ioqJw+PBhvHjxAi1btsT8+fNhZmZWJkEOHDiA+fPnKxwbN24cvLy80KJFC6xcuRJ///03dHR0oKuri6CgIJiYmJTJuQkhhHy6T1pMsW7duhg/fny5BCnePAYAGzZs4L8ODAwsl/MSQghRj8oFZM6cOfzGUsXRaryEEPLlUbmANGzYUOHxq1evEBsbW2qnNyGEkP82lQvI1KlTSxwbOHAgfv311zINRAghRDuoPIxXGWtr6xJrVhFCCPkyqHwHcunSJYXH7969w+HDh/HVV1+VeShCCCHCp3IBKT7EtkqVKrCyssKKFSvKPBQhhBDhU7mAxMXFlWcOQgghWuaDBUQmk6n0j+jofFZXCiGEEC30wQLSrFmzUud+FHXr1q0yC0QIIUQ7fLCAnDx5kv/69OnTiI2NxYQJE1CnTh08f/4cGzZsQM+ePcs9JCGEEOH5YAGpW7cu//XWrVsRHh6OatWqAQDMzc1hY2ODAQMGYOjQoeWbkhBCiOCo3HmRlZWF3NxchWPv3r1DVlZWmYcihBAifCqPwnJ3d8eoUaMwcuRI1K5dGykpKdixY0eZ7UZICCFEu3zSYooNGjTAkSNH8PLlS5iYmMDT0xMeHh7lmY8QQohAfbSABAQEYMGCBdDR0cGQIUNQoUIFDBo0iH9+2rRpWLNmTbmGJIQQIjwf7QOJiIhQeBwcHKzw+MKFC2WbiBBCiFb4aAHhOO6DjwkhhHyZPlpAik8kVGViISGEkP++j/aBSKVS/P777/ydR0FBgcJjVZc7IYQQ8t/y0QJSo0YN+Pr68o+NjIwUHhsbG5dPMkIIIYL20QKiiVV47e3toaenh4oVKwIAZs+ejc6dOyu8Jjc3F/PmzcPff/8NsVgMHx8fdO/evdyzEUIIUU7leSDlbfXq1bC0tCz1+U2bNkFfXx/Hjx/Ho0eP4OnpiWPHjqFq1aoaTEkIIUROa9Zhj4mJ4SctNmrUCDY2Njh79izjVIQQ8uUSzB3I7NmzwXEcbG1tMWvWLH7RRrnnz58rLO5oZmaGlJSUTzpHYmIi/7Wtre3nBf4M165dU3qcMinSpkwAu1xCzARo1++PMin60DVVlCAKSFhYGMzMzCCRSLB06VL4+/tj+fLlZX4eGxsbvp+FJZYXRmkok2ook+qEmIsyqUaeKS8vT+GDd3GCaMIyMzMDAOjp6WHo0KG4fv16idfUqVMHz5494x+/ePECtWvX1lhGQgghipgXkJycHH5JeI7jcOTIEVhbW5d4nZOTE/bs2QMAePToERISEkqM1CKEEKI5zJuw0tLSMG3aNEilUshkMlhYWMDPzw8A4OrqivXr18PU1BRjxozB3Llz4ejoCB0dHfj7+0NfX59xekII+XIxLyD169dHZGSk0ucOHjzIf12lShWsXr1aU7EIIYR8BPMmLEIIIdqJCgghhBC1UAEhhBCiFioghBBC1EIFhBBCiFqogBBCCFELFRBCCCFqoQJCCCFELVRACCGEqIUKCCGEELVQASGEEKIWKiCEEELUQgWEEEKIWqiAEEIIUQsVEEIIIWqhAkIIIUQtVEAIIYSohQoIIYQQtVABIYQQohYqIIQQQtRSgXWAjIwM/PDDD0hOToaenh4aNmwIf39/GBsbK7xu7ty5uHjxIqpXrw4AcHJywqRJk1hEJoQQAgEUEJFIhLFjx6Jdu3YAgMDAQCxfvhw//fRTideOHz8ew4YN03REQgghSjBvwjIyMuKLBwC0bt0az58/Z5iIEEKIKpjfgRQlk8mwa9cu2NvbK31+y5Yt2LNnD+rXrw9vb29YWFh80r+fmJjIf21ra/tZWT/HtWvXlB6nTIq0KRPALpcQMwHa9fujTIo+dE0VJagCsmTJElSpUkVpM9XMmTNhYmICHR0dREZGYuzYsThx4gTEYrHK/76NjQ0qVqxYlpHVwvLCKA1lUg1lUp0Qc1Em1cgz5eXlKXzwLo55E5ZcYGAgHj9+jFWrVkFHp2QsU1NT/ribmxtycnKQkpKi6ZiEEELeE0QBWblyJRITE/Hrr79CT09P6WtSU1P5r8+dOwcdHR2YmppqKiIhhJBimDdh3b17F+vWrUOjRo0wePBgAEC9evXw66+/wtXVFevXr4epqSl8fHyQlpYGkUgEfX19/Pbbb6hQgXl8Qgj5YjF/B27SpAlu376t9LmDBw/yX2/dulVDiQghhKhCEE1YhBBCtA8VEEIIIWqhAkIIIUQtVEAIIYSohQoIIYQQtVABIYQQohYqIIQQQtRCBYQQQohaqIAQQghRCxUQQgghaqECQgghRC1UQAghhKiFCgghhBC1UAEhhBCiFioghBBC1EIFhBBCiFqogBBCCFELFRBCCCFqoQJCCCFELVRACCGEqEUQBeThw4fw8PBAr1694OHhgUePHpV4jVQqxeLFi+Hg4ABHR0fs27dP80EJIYTwBFFA/Pz8MHToUMTGxmLo0KFYuHBhiddERUUhOTkZx44dw549e7BmzRo8ffqUQVpCCCEAUIF1gLS0NNy8eRNbtmwBAPTt2xdLlixBeno6jI2N+dcdOXIEgwYNgo6ODoyNjeHg4ICjR49i7NixHz0Hx3EAAIlEonC8WhXdMvx/opq8vLwPv6CSgWaCFPGxTAa6VTWU5F8fy6RjILyfEwBUqqLZPylVMlWsoq+BJIo+lovTE2koyb8+lqlSpUoaSvKvj2USiStrKMm/imaSv2fK30OLE3GlPaMhiYmJ8PHxweHDh/ljzs7OCA4ORvPmzfljLi4uWLp0KVq2bAkA2LBhA1JTU7FgwYKPniMrKwt37twp+/CEEPIFsLS0hIGSD23M70A0oWrVqrC0tISuri5EIs1/8iGEEG3EcRzy8/NRtaryVgjmBcTMzAypqamQSqUQi8WQSqV4+fIlzMzMSrzu+fPn/B3IixcvUKdOHZXOoaOjo7R6EkII+bAPNe0x70SvUaMGrK2tER0dDQCIjo6GtbW1Qv8HADg5OWHfvn2QyWRIT0/HiRMn0KtXLxaRCSGEQAB9IABw//59zJ07F2/evEG1atUQGBiIxo0bY9y4cfDy8kKLFi0glUrh7++PCxcuAADGjRsHDw8PxskJIeTLJYgCQgghRPswb8IihBCinaiAEEIIUQsVEEIIIWqhAkIIIUQtVEAIIYSohflEQqK9cnNzkZKSAqlUyh/76quvGCYSpqysLDx8+FBhjaGvv/6aYSJCygYVkGKmTZuGJUuWwMjICACQkZGBRYsW4ZdffmGWqaCgAOHh4bh165bCm9CyZcuYZQoLC8Py5cthZGTELw8jEolw8uRJZpmSkpLg5+eHpKQkhYUzb926xSzTkSNHEBgYiDdv3qBWrVpITk6GlZUVDhw4wCyTVCrF/v37BTePSojXuRCvKbns7Gw8fvxYYc1ATaMmrGKePHnCFw8AqF69OpKTkxkmAhYuXIjr16/j9OnTaNSoERITE5msHFrU5s2bER0djVOnTiEuLg5xcXFMiwcALFq0CDNmzEDDhg1x5swZjB8/HjNnzmSaKTQ0FBEREWjYsCFiY2OxceNGtGjRgmkmsViMPXv2MM2gjBCvcyFeUwBw5swZ9OnTB9OmTQMAJCQkYOLEiRrPQQWkGKlUqtAkk5+fX2IZeE1LSEhAYGAgDAwMMGHCBOzcuRP37t1jmsnExAR169ZlmqE4iUSC9u3bg+M41KpVCzNnzkRsbCzTTBUqVECNGjX4a6pjx45ISEhgmgkA2rVrh6NHj7KOoUCI17kQrykAWL16Nfbv349q1aoBAFq0aMHkgy41YRXTqVMnzJw5EyNGjAAAbN++HZ07d2aaqWLFigAKPznm5ubCwMAAaWlpTDN16NABQUFB6NOnD58PYNsHIhaLAQCGhoZISkqCqakpMjIymOUBAD09PXAch4YNG2LHjh2oW7cucnJymGYCgAMHDmDLli2oVKkSKleuDI7jIBKJcOnSJWaZhHidC/GakjMxMVF4rKenp/EMVECKmTVrFtatW4eff/4ZANCtWzeMHz+eaSZDQ0O8fv0anTt3xrhx41C9enWYmpoyzRQZGQkACp9iWfeBODs7IyMjA+PHj8eQIUMgk8ng5eXFLA8ATJ8+HdnZ2Zg9ezYWLVqErKws+Pn5Mc0EAOHh4awjlCDE61yI1xRQuEXFP//8w/c/Xr58mcmK47QWlhaQL3Uvk8lw6NAhZGdnw83NDfr6mt9pTlvk5+cjLy+PfkYfIIRO2KKEfp0L6Zq6ceMG/Pz88PTpU1hZWeHRo0f47bffYGNjo9EcVEDei4mJQe/evREWFqb0eU9PTw0n0g5paWkKI2ZU3aOlPERGRqJ79+4wNDQEAGRmZuLs2bPo168fs0wBAQGYOnWqwqi+kJAQzJ8/n1kmoLATduHChRCLxYiLi0NCQgJ+/fVXhIaGMs0lNEK8puSysrJw/fp1AECbNm34/hBNoias9+SddYmJiYyT/GvOnDkIDg7GgAEDlO6kuH//fgapCl26dAlz585FWloadHR0kJ+fDyMjI6Zt6Js3b4abmxv/2MjICJs3b9BpmLoAACAASURBVGb6xx4fH19iVN/Vq1eZ5ZGTd8KOGzcOALtOWEDY17kQryk5AwMDdO3alWkGKiDvybdsHDhwIGxtbRmnKTRy5EgAgI+PD+MkJQUHB2Pr1q2YOXMmDhw4gP379+Pp06esY5VQdESdUM5fUFDAIElJQuiEBYR9nSvD8pr69ttvlRZZVoMgqIC8FxUVhdGjRyMgIIDpJK+i5O2Z33zzDX9MIpHg9evXJf74WTA3N0dBQQFEIhEGDRqE/v37Mx0jb2JigmPHjqFnz54AgNjYWNSoUYNZHqDwk31AQADGjRsHjuMEMQ8EEE4nLCDs61xo15TQBj9QH8h7gwcPhpGREa5fv4727duXeJ7lTPSZM2fC398furq6cHV1RUZGBiZMmIAxY8Ywy/Tdd99h7969GDNmDDw9PVG3bl1MnjyZ6Sis+/fvY/LkyZDJZAAKh2CGhISgcePGzDJlZ2dj6dKlOH36NEQiEbp164Z58+Yxe7OWE0onbFFCvM6FeE3J5efn4+HDhwCAxo0bo0IFzd8PUAF5LzMzExcvXkRwcLDSYXru7u4MUhVyc3NDZGQkjh49iosXL2LevHn47rvvEBUVxSxTdHQ0OnfujMePH8Pb2xtZWVmYN28eXF1dmWUCCpsX5H9U5ubm/Dh+UpIQOmGLEuJ1DgjzmoqPj4e3tzc/Uz8vLw8rV65E27ZtNZqDmrDeMzIygrOzM2rUqIF27dqxjqNA3mZ+9epVdO3aFZUrV4aODttFBPr27QsAaNmyJY4fP840i0QigZ6eHnJzcwGAnyEvX0GgcuXKGs907do12Nra4syZM0qfZ935CRR2wrZv355v08/NzWXys5IT0nUuxGuqKH9/fwQHB/PNfvHx8Vi0aBEOHTqk0RxUQN6T/8G/e/dO6R89yz94CwsLjB07Fg8ePIC3tzfevXvHLItcbm4uQkND8fTpU6xYsQL379/Hw4cP4eDgoPEsHh4eOHDgANq0aaPQwSjvWGSx8N2BAwdga2uLjRs3lnhOJBIxLyDHjh1DQEAAXr16BYDtz0pOSNe5EK+p4or2GdnZ2THJQE1Y7y1YsAABAQEYPnx4iedEIhG2b9/OIFWhd+/e4fz582jatCnq16+P1NRU3Llzh+kSKz4+PjAxMcGpU6dw+PBhvH37Fp6envwMdQLIZDLcuXMHVlZWrKOU4OjoiMDAQLRu3Zr53aycEK9zoQoMDIS1tTU/nDgqKgq3bt3CDz/8oNEcVEC0jEQiQUxMDCIiIrBt2zZmOeTt1fL/BYB+/fpp/BZaTiqVYuDAgYIZQSfn4uLCvA1fmYEDBzKdX/ExQrjOhXhNyYfxchyHzMxMfui1RCJB9erVaRgva0KdeXrjxg3s378fsbGxaNGiBdNOfaDknIG8vDyw/CwiFotRpUoV5OXlKSzuyFrDhg3x9OlT1KtXj3UUAODb9B0dHbFz5044Ozsr/LxYt+0L6ToX4jUltGG8VECKEdLM0/T0dBw6dAjh4eHIz8+Hm5sbKleurLRdXdPs7OwQGhoKiUSCy5cvY8uWLbC3t2eaydzcHJ6enujVqxeqVKnCH2e5DM3bt2/Rr18/2NraKmRiNSxc3qYvL/b+/v78Y1Zt+0K+zoV2TQltCwUqICpgNfO0S5cusLOzw+LFi/nhefv27WOSpbiZM2di48aNqFq1KoKDg2Fvb8981WKpVIomTZrgwYMHTHMU1a9fP+Z3r0UlJSWxjlCCkK9zIV5TAPDixQsEBwcjKSlJYS06jc/D4oiC0aNHc7Gxsfzjo0ePciNHjmSSJSgoiOvUqRPn6enJRUREcG/fvuXs7e2ZZBGyTZs2cRzHcfHx8YyTaI+AgACVjmmCEK9zoV9T33//Pbd3717OycmJu379OjdnzhxuzZo1Gs9BBaSYe/fucT179uQcHBw4BwcHrlevXtz9+/eZ5ZFKpVxcXBw3ZcoU7uuvv+batGnDXbp0iVkeuaysLC4wMJBzd3fn3N3duaCgIC4rK4tJFjc3N4X/FZKHDx9ygwcP5rp3785xHMclJiZyq1evZpxK+c/K1dWVQZJCQrvOhXxNcdy/v6u+fftyHFf48xs4cKDGc9AoLCWEOPMUKGwrjoyMxIEDB/D69WucPXuWWRYvLy/o6+tj4MCBAICIiAi8efMGq1ev1ngWIS9D8/3332P06NFYsWIFDh48CJlMBhcXFxw+fJhJnpiYGMTExOD3339X+FllZ2fj7du32L17N5NcRQnhOhfyNQUAgwYNwr59++Dh4YH/+7//Q82aNdG7d2+NN2FRH4gSYrEY1atXR15eHlJTUwGw3edCztjYGKNHj8bo0aNx48YNplnu3r2LmJgY/nHbtm3Ru3dvJllCQ0Nx8eJF3L59G926dWOSoTRZWVno0qULVq5cCQDQ0dGBrq4uszzm5ubo3r07EhIS0L17dwCFk+P09fWVvlGyIITrXMjXFFA4iCUzMxNDhgxB//79oaenh169emk8BxWQYoS4z4UyLVu2ZHr+WrVqIT09HcbGxgAKN0pitf1oacvQpKSkMB/2KBaLkZ+fz89mTk1NZTpxz8rKCtnZ2ahfvz5+/vlniEQiNG3aFFOnThXETnvFsbrOhXxNAf8ufe/m5oZvvvkG2dnZsLS01HgOKiDFaMs+F6wEBQUBKNwYydXVlf8Ue/r0aWbLKci1a9cOEokEJ06cQHh4OBITE5ksrVLU0KFDMXXqVGRkZGDNmjWIjIxkuuT9iRMnsGTJEkyaNAnz5s0DAPzxxx+YPXs2FixYwPznJTRCvKYAYNq0aRg3bhxatmzJt478/PPPmDt3rkZzUAFRQmj7XAiJfCz8V199ha+++oo//t1337GKBAC4efMm9u/fj5iYGFhbWyMpKQnnzp1jtkmSnJubG+rVq4dTp04hNzcXgYGBTAttSEgINm7ciCZNmvDHrK2tYWdnBx8fH0G8OQqFUK8p4N+i/8MPP/C/s8uXL2s8BxWQYuRr6puamiIuLg5169bF69evGacSjqlTp7KOUIK7uztycnLg7u6OAwcOoHbt2rC3txfEHzpQ2F7N+u5M7t27dwrFQ87S0lJhPgFLEolEYe4Vi9nxQr+mTExMEBISgokTJ+LZs2cYOXIkk5UgqIAUM2LECLx+/RrTp0/n97nw9fVlkkVo21cWlZ2djZCQEPz+++8ACrNOnjyZSTt6hQoVkJ+fD4lEwi8JruznxsKDBw8QGhqK5ORkha1sWa1DlZ+fj/z8/BId+RKJhF+qnJXjx49jyZIlglghWMjXlJyZmRn+97//wcvLC0+ePOE3vdIkGsZbxKlTp/Dw4UNYW1sLYkTKs2fPPvg8y2UNhDSMFwDu3buH8PBwREVFwdzcHPfu3UNcXBzztZ1cXFzg6uqK5s2bKwwHL7oUtyb9/PPPePnyJRYvXszvivjmzRssWrQIJiYmfL8IC0JbIVio1xQADBgwgO/MLygowI8//ojIyEiNF1sqIO+tWLECR44cQYsWLXD9+nWMHz8ew4YNYx1LsHr37q0wjLe0Y5pWUFCA06dPY//+/YiPj1cYQsuCvAlEKCQSCRYtWoSjR4+iYcOGAIDHjx/DyckJixYtYtpEI9QVgoV2TZXm+fPnGp9uQE1Y7504cQIHDx6Evr4+UlNTMWXKFMEUEMGse1OEkIbxFlWhQgU4ODjAwcEBr169Yr4/SefOnXHmzBnmG0jJ6enp4aeffsLUqVNx584dcBwHS0tLpnezQl8hWEjX1Md2uqQCwkilSpX49ntTU1NmCygq4+vrC2dnZ9y6dQvLly/Hrl270KBBAyZZhDyMtzgTExOMGzeOaYb27dtj8uTJ0NHRgZ6eniD6r4DCNxohTI4FhLlCcGlYX1NC2+mSmrDe69q1q8JqsuvXr1d4zHJJcPmmTfLNiWQyGTw8PJisWLp27doPPi/EUVosOTo6wtvbG82bN1do1xfastyEqIPuQN7r0KEDEhMTS33MknzETJUqVfD8+XPUrFkT6enpTLJQgfg0hoaGcHJyYh1DKyxduhTz58//6DFSKDk5GcnJyQqtJZq+A6EC8t6yZctYRyiVsnVvWL0pxcTEoHfv3ggLC1P6PMs7NSFycHDArl270Lt3b0G16wtRfHx8iWNXr15lkET4VqxYgX379sHCwoK/s6UmLKJUVlYWP+Ty+fPnzNa9AYDVq1fDy8ur1OGeLAvxgwcP8Ntvv+HJkyeCmHMBFK49JSfUdn3WhLxCsBCvKaCwafTAgQPM1y+jOxCB4zgOHh4eOHLkCAD2qwJ7eXlBJpPByclJMCOL5GbNmgUnJyf0799fMEvwC3EHQKExNzdHt27dkJCQoLDyrRBWCBbiNQUUduazLh4AFRDBE4lEMDMzw+vXr2FoaMg6DoDCJclXrVoluAIik8kwceJE1jFKJZFIcPToUYSHh2Pbtm2s4wiGlZUVrKysYG9vDyMjI9ZxFAj1mmrdujVf3Io2jVIfCClBX18f7u7u6NKlC7+YIQD88MMPzDJZWVnhxo0bzJeVL6p169ZISkpSaDYSghs3bmD//v2IjY1FixYt4O7uzjqSIC1cuFDpciEsN28S6jWVkJAAANixYwd/jEUfCBWQ9wYMGPDBtW5Ytnk2adJE6QJ4LP39998YMmQIGjZsqFDUWPyc5L+7goICREREwNzcXOFTGYtM6enpOHToEMLDw5Gfnw83NzdUrlxZ6fh9Ukg+pwgA8vLyEBsbCwsLC4aJCou/UK6poooWDpaoE/29K1euACicEPfgwQOFNZ7Mzc0xZ84cZtnu379f4g9J2TFNkv+8imOxxlNpWeRYZLKxsYGdnR28vLzQtm1bAECPHj2Yrh6gbSQSCcaMGcP0zVJI13lRymai6+vrw9LSkh9woxGa3IBdGwwcOJCTyWT844KCAiab1Rfl5uam0rEvXWRkpErHNCEoKIjr1KkT5+npyUVERHBv377l7O3tmWTRVhKJhHN0dGQdQ5C+++47ztramnNzc+Pc3Ny4Zs2ace7u7lyHDh24uLg4jeWgJqxiXr9+jby8PFSqVAlA4acgVvuBpKenIz09HXl5ebh//z6/1ENWVhZycnKYZJLLysrChg0bcOvWLYX1ubZv384s09atW+Hq6vrRY5owZ84ceHt748yZMwgPD8eyZctQUFCA33//Hd9++63G82gDLy8vvhmZ4zjcvn0bHTp0YJpJiNc5ADRo0AA//vgjbGxsABQ2KW/ZsgXBwcGYNWuWQnNgeaICUkzv3r3h4eEBZ2dnAIVj1OVfa1pUVBS2bduGly9fKqy/Y2BggLFjxzLJJOfr6wsLCws8evQI06dPR3h4OJo3b84kS0JCAm7cuIGMjAyFCY7Z2dnIz89nkgkoHK3WvXt3dO/eHenp6YiMjMTSpUvx+vVrnD17llkuoSr6picWizFmzBi0atWKYSJhXedFJSUl8cUDAJo3b447d+7AwsJCoxtLUQEpZubMmWjVqhXf9jljxgyFsemaNHLkSIwcORKhoaGCG0r4+PFjrFmzBidPnkTfvn3Rs2dPjBgxgkmW1NRUJCYmIjc3V2H5mapVqwpmhQFjY2OMHj0ao0ePxo0bN1jHEST56DT53XXRwRmsCOk6L6py5cqIjo5G3759AQDR0dF8q4kmN76iAqKEvb097O3tWcfgTZw4Ebm5uUhJSVFY96bonuSaJt83QldXF5mZmTA0NGS2PpeDgwPs7e1x6tQp9OjRg0mGTyGkoc9C8uTJE3h7e/OTL5s1a4bg4GDUr1+fWSYhXedFLVu2DHPmzMG8efMgEonw1VdfISgoCDk5ORod3k8FpBghLl0QFhaG5cuXw9DQUGHdG5Yjeho1aoTMzEy4uLjAw8MDBgYGTG/tdXR0sHbtWq0oIES5hQsX4rvvvsOAAQMAFI6AXLhwIbZs2cIsk9CuczkLCwtEREQgOzsbABRmpXfs2FFjOWgYbzFubm5wcnJCq1atBLEFKVA4/HP79u2CXQI8Pj4eWVlZ6Ny5MypUYPeZZN68eRgyZAh9wtdSrq6uOHjw4EePsSKU61zu3LlzuHjxIgCgU6dOGi0ccux/CgIjxKULTExMBFM87t69i1evXimMjrGzs8OFCxfw6NEjps1qQprcWJxEIlFofqTVeEvS0dHBgwcP0LhxYwDAw4cPma4/JZVKMXDgQH5LYiFtmLZx40ZERkaiT58+AAr3undzc8OYMWM0moMKSDFCXLqgQ4cOCAoKQp8+fRRmw7J4s16xYgVmzJhR4njNmjWxfPlyhIaGajyT3IIFC5iduzTHjx/HkiVL8OrVKwCg1Xg/YObMmfD09IS1tTU/jFe+AyYLYrEYVapUQV5ensLfnRAcPHgQu3fv5puuhg8fjiFDhlABYU2ISxfI92A+evQof4xVH8g///yjtLg2bdoUz54903ieouTNjEIaxRMUFIRVq1ahdevWCjsSkpK6dOmCw4cP46+//gIAtGrVCsbGxkwzmZubw9PTE7169VK4noSw703Rfg9WK/NSASnG19eXdYQS4uLiWEfgZWVllfocyzkXwL+jeG7dugWRSCSIUTyGhob8Uibk44yNjWFra4srV67g5cuXzAuIVCpFkyZN8ODBA6Y5irOxscG8efMwaNAgAIUfcIvOC9EU6kTXEpcuXcL9+/cxbNgwpKWl4c2bNzA3N9d4jiFDhuDHH39Es2bNFI7fvHkT/v7+TDf/GTVqFPr06aMwiic6OprJKJ7c3FwAhTOWDQwM4OzsTDsSlmL27NkYO3YsrKyskJmZCVdXV+jr6yMjIwMzZ87k3yTJv3JychASEsJ3onfo0AGTJ0/W+F03FZBihLh0wfr163HmzBm8evUKx44dQ0pKCmbOnIldu3ZpPMu5c+fw448/YsqUKWjRogWAwpngISEhWLx4Mbp06aLxTHJCGsVjZWXF70AoRzsSKufs7MxvmLZt2zacOXMGmzdvRkpKCiZMmMB8FNaDBw+QlJQEiUTCH3Nzc2OYSDioCasYIS5dEB0djfDwcP6TWO3atfnx35rWuXNnBAQEICQkBEuXLgVQuIyCv78/OnfuzCSTnJBG8dBOhKoremd27do1ODg4ACi8zjU5q1qZ7du3Y8+ePXj16hVatGiB+Ph4fP3118wLSEBAAKZOncpvwJWRkYGQkBDMnz9fozmoV6+Yx48fY8aMGahUqRL69u2LdevWIT4+nmmmSpUqQVdXV+EYyz+sTp06YefOnfjzzz/x559/IiwsjHnxAP4dxSNfMsTT0xOzZs1imkleZD927EuXmpqKd+/e4cqVKwpzroq2ArCwd+9e7Nu3D2ZmZti0aRP27duHqlWrMs0EFM5JKbp7Y/Xq1XH16lWN56A7kGKEuHRB7dq1ER8fD5FIBJlMhtDQUMFtMCUEXbp0QXR0NL/WlBBG8Sj78MHiD13Ixo8fDzc3N+jq6sLW1pYfnv7nn3+iTp06TLPp6emhSpUqkMlk4DgOlpaWePToEdNMABTmFMkVXTlDU6iAFCPEpQt+/PFH+Pj44O7du2jVqhXs7OwQHBzMNJNQ1ahRQ2NLWX9ITEwMYmJi8OzZM0yfPp0/np2dzS96Rwr17t0bdnZ2JYaIm5mZYcmSJQyTFQ52yM/Ph5WVFYKDg2FmZgaZTMY0EwC0aNECAQEBGDduHDiOw8aNG/k+SU2iTvQPENrSBbm5uZDJZIK4hRaSb7/9ttQmPZFIxI9U0aSkpCTcvHkTa9asgZeXF39cX18f7du3ZzZun3yaO3fuoF69esjNzcXKlSuRlZWFSZMmwdrammmu7OxsLF26FKdPn4ZIJEK3bt0wb948ze5GCCoggnby5ElkZ2eX2BApMjIS1apVE9SKwSwpm8AYHx+P1atXo3r16swmgUqlUixbtkyQM+QJKQvsP1aTUm3atAlr1qwpcbxLly6YPHkykwLyoU/7QOF8FU0ruk5YUlISVq5ciadPn8LHxwc9e/bUeB45sViMP//8k9n5yedLS0vDjh07SqzO/csvvzBMVdgasW7dOjx58gQrVqzA/fv38fDhQ34Em6ZQAREwiUSCGjVqlDhubGzMbEvb8PBwAIUzXzMzM+Hh4QGO47B//34YGhoyyQQUzkJftWoV/vjjD0yaNAkDBgwQxNIh3bp1w6ZNm+Dm5qYwyYsmEmqHadOmwcLCAu3bt2e6sGNxixYtgomJCT9cvHbt2vD29qYCQv71ob3Y5TOdNU3+af/MmTOIiIjgj//4448YMGCAQnu/pixevBgnTpzAmDFjsGzZMn4knRCsXbsWABQGPdBEQu3x5s0b5h35yty+fRuBgYE4f/48gMLdN1l07lMBUeL8+fMlZqJPnTpV4zmaNm2KqKgouLi4KBw/fPgw82G82dnZSE9P54fJpqenM5vcuGvXLlSpUgWhoaFYt24df1w+65tFs5ocTSjUbk2aNEFqaipMTU1ZR1FQ/ENSXl6eRvdCl6MCUszy5cuRkJCAe/fuoUePHjh58iTat2/PJIu3tzeGDx+O06dPo1WrVgCAv/76C5cvX8aOHTuYZJIbOXIk3Nzc+P3iz5w5gwkTJjDJwnJnRlVkZGTwK8y2bt1aYQIYESYvLy+IRCJkZ2ejX79+aNOmjcKMedZ9IHZ2dggNDYVEIsHly5exZcsWJn2iNAqrGBcXFxw4cAD9+/fHoUOHkJqaigULFmDDhg1M8rx69QphYWG4efMmgMJ9oocOHYpatWoxyVNUUlISPynum2++QdOmTRknEp5z585hzpw5/LDP27dvIzg4mMnucUR18k2kSuPu7q6hJMrl5+dj48aNiIuLA8dxsLe3x/jx4zU/3YAjCvr3789xHMf169ePk0gkHMdxXN++fVlGIlrM3d2du3fvHv/43r17nLu7O8NERNsVFBRw8+fPZx2D4ziOoyasYqpWrYrc3Fy0adMGc+fOhYmJCc0cVuL69esIDg7GkydPIJVKBdHfIEQFBQWwsLDgH1tYWDBZcoKop6CgAOHh4SX6RJctW8Ysk1gsxu3bt5mdvygqIMWsXLkSYrEYPj4+2LJlC7Kyspi3dwrR/PnzMXnyZNpp7yOMjY0RERGB/v37AyhsGmG9PhdR3cKFCyGVSnH58mUMGTIE0dHRgtgb/dtvv4W/v3+J4eGa3uaa+kCIWtzd3T/aTqwpZ86c+eDzXbt21VCSkpKTkzF79mx+l0Rra2sEBwejQYMGzDIR1bm4uPAjIaOiopCVlYXJkyczH8SirMOcxTbXdAfyXnBwMObMmcOPviiOxV3IvXv3Pvi8pj9tFNWlSxecOXOG6Zuz3MaNGwEUTrxMSEiApaUlgMJ1jFq2bMk0Y4MGDbB37168ffsWAGgdMy0jH3klFouRm5sLAwMDpKWlMU4lnG2uqYC8Z2trCwCCWMlVbvz48fwudi9evIC+vj5EIhGysrJgZmbG9CLas2cP1q1bh6pVq0JPT49pH4j80+CsWbPg6+vLD3m+ceMGtm3bpvE8gLCLP1GdoaEhXr9+jc6dO2PcuHGoXr26YOaE5ObmIiUlRWFpd2rCIiUsWbIEdnZ26N27NwDg6NGjiI+PZ7pIn7IFDAHFdak0Td7M8LFjmlBaE8Pbt2/x+vVrmomuJaRSKcRiMWQyGd+E5ebmxnw15bCwMCxfvhyGhoZ8HySLJiwaxlvMsmXLuDdv3nD5+fnckCFDuFatWnGRkZFMM7m4uJQ41q9fPwZJhG3QoEEKv6uDBw9ygwYNYpjoX2/fvuXWrl3LdejQgfv5559ZxyGfKD8/n7t58yaXlpbGOgrHcRxnb2/PPX36lHUMjobPFHPx4kUYGBjg/PnzMDU1RWxsLDZv3sw0E8dxCjvbXbt2jfmmNi9evMCsWbPg7OyMHj168P+xtGzZMmzbtg0tWrRAy5YtsX37dqbDLYHCYaA7duyAk5MTUlJSEBERAR8fH6aZyMcFBQXhzp07AIB3795hwIABGDFiBHr06IETJ04wTgeYmJgwvduXoz6QUly9ehWOjo4wNTVluv84APj5+WHWrFn8Cq55eXlYsWIF00y+vr5wdnbGrVu3sHz5cuzatYv5yCILCwtERETwa3KxbmaIjIzE2rVrYWNjg23btsHc3JxpHqK606dPY86cOQCAQ4cOQU9PDxcvXsSDBw/g6+ur8VVvi+vQoQOCgoLQp08fhSVWNN0HQgWkmBo1asDPzw/nzp3D+PHjUVBQoHT/YU2ys7PDiRMn8PDhQwCAubk58xVnMzIyMGjQIGzfvh1t2rRBq1at4OHhwWTRyaKSk5ORnJys8DtjMQrLxcUFOTk5mDZtGmxsbCCVShU61qkTXdj09PT4D46XL19Gnz59oKuri6ZNmzJ/PwAKP5wAhf2hcjSMVwBWrFiBQ4cOwd3dHYaGhnj69ClGjRrFOhakUin09PQglUqRnJwMgO2bkK6uLgCgSpUqeP78OWrWrIn09HRmeYDC392+fftgYWGh0LHIooDIh+2uXr2aH0knx6Szk3wSqVSK7OxsVK5cGfHx8QrvARKJhGGyQjSMV6CMjY3x/fff849TU1MRHx/PzyRmQT7iwsjIiP9UxPpNyM7ODpmZmRgyZAj69+8PPT09ODk5McsDFH4aO3HiBPOmK0A4f+BEPYMHD8aAAQNgYGCA2rVrw8bGBgBw9+5dpisJPH/+XOGxSCSCsbGxQjOWJtEwXiVevXqFAwcOICIiAiKRCC4uLpg8eTKzPD169MD27dsF0WmmzPPnz5Gdnc1P4GNl6NCh2LlzJ9MM5L8jISEBqamp6NixI9//+ODBA7x79w7NmjVjkkm+pXTRt+3s7Gy0bt0aQUFBqFOnjkbzUAF5TyqVIi4uDvv378eff/6Jnj174vTp0zh37hzraBg8eDB2797NOobgBQUFISUlBU5OTgqfyIQwW55ovytXruCbb75hHaMEqVSK3bt34/z58/jtt980em4qIO+1b98eDRs2hKenJxwdgNaJ6wAAFHBJREFUHVGpUiV+QynWVq9ejXfv3jEfcSF0w4cPL3FMJBJh+/btDNKQ/xohrf+mDIt81AfyXrt27XDp0iV+/oeQPmkIZcSF0LFe4I78twn9szaL0WFUQN5btWoVXr9+jaioKCxbtgyvX79GdnY2njx5gvr16zPNRh2yqsvKysLDhw8V9m74+uuvGSYi/xWsB4kAhetfFZeZmYndu3ejSZMmGs9DTViluHnzJsLDwxEdHY1GjRphz549rCMhLS1N4Y1R0x1mRQlxo50jR44gMDAQb968Qa1atZCcnAwrKytBNzsQ4cvPz+fnYDVu3Fjz28YWYWVlpdCJLh+F1aFDB8ybN0/jI8ToDqQUzZo1Q7NmzeDj48N86YJLly5h7ty5SEtLg46ODvLz82FkZMR09z8hbrQTGhqKiIgIjBkzBpGRkbhw4QJiY2OZZiLaLT4+Ht7e3vyupHl5eVi5ciXatm3LJE9SUhKT85aKyQpc5JO4u7tzDx484FxdXTmZTMbt3buXW7lyJdNM8n3i5f/75s0bbtiwYSwj8XuNF93D3s3NjVUc8h/g4uLCXb58mX989epVpYubfqloMUUtYW5ujoKCAohEIgwaNIj58GIhbrQj35ekYcOG2LFjB+Li4pCTk8M0E9F+RQfUsL7LFhpqwtIC8jZXU1NTxMXFoW7dunj9+jXTTELcaGf69OnIzs7G7NmzsWjRImRlZcHPz49pJqLdOnbsiEOHDqFfv34AgKioKHTq1IlxKuGgTvRiNmzYgHHjxn30mCZFR0ejc+fOePz4Mby9vZGVlQVfX1/+omZBqBvtEFIWis74zszM5BcvlUgkqF69OtP+RyGhAlKMssk4Qp9AxEJ6ejr09fUV/rCys7OZrhNESFkpbcdNOaEuK6Rp1IT13oULF3D+/Hm8fPkSQUFB/PHs7GzBTyBiYcKECQozvAsKCjBx4kTs3buXYSpCygYVCNVQAXlPV1cXVatWhUgkQpUqVfjjtWrVwvjx4xkmEyaJRMIvMAcULutedD4IIf8F8qas4qgJqxAVkPe++eYbfPPNN+jZsyfzVWW1RXp6Ot9klZaWxnyb3aVLl2L+/PkfPUaIqsLDw/mv8/LyEBUVxXQiodDQMN5iLly4gKysLADAnDlz4OTkhPPnzzNOVdiU9vfff7OOwRs+fDiGDBmCkJAQhISEYOjQoQr7qLBQdN94uatXrzJIQv4r6taty//XuHFjTJ8+HWfOnGEdSzColBYTERGBUaNG4ffff0d6ejp++uknBAQEMB26d+bMGSxcuBBisRhxcXFISEjAr7/+itDQUGaZBg4ciPr16/N/TEuWLGG2AGVMTAxiYmLw7NkzTJ8+nT+enZ3NzyAmpCw8efKE+XwnIaECUoxYLAZQuA+yi4sL2rZty7wTffXq1di/fz8/lLhFixb8trYstWvXDu3atWMdA+bm5ujWrRsSEhLQrVs3/ri+vj7at2/PLhjRekX7QGQyGQoKCqhJtAgqIMVUqlQJ69evx+HDhxEWFgaO45Cfn886FkxMTBQey4fPalpwcDDmzJkDLy8vpZ2Lv/zyi8YzWVlZwcrKCvb29jAyMtL4+cl/V9E+kAoVKqBmzZr8h0xCBaSEZcuWYefOnZg9ezZMTEyQnJwMFxcXppmqVq2Kf/75h3/Dvnz5MgwMDJhksbW1BQB0796dyfk/JDQ0FFOmTEHlypUxYsQI3Lx5E4sXL4arqyvraERLyYfzvnnzBleuXEG9evVgZWXFOJWAMFuFi6jsr7/+4tzc3Dg7Oztu2LBhXKdOnbiEhARmeQoKCrhffvmF2flLI1/k7tSpU9yMGTO4lJQUrl+/foxTEW3k7e3N3bp1i+M4jsvIyOC6dOnCOTs7c+3bt+f27t3LOJ1w0B1IMVlZWdiwYUOJfS5YbovasmVLbN++HdevXwcAtGnTBtWqVWOWRywW4+zZs/Dy8mKW4UOuXr0KR0dHmJqaKm1mI+Rjbt68yd9pHDx4EBYWFti8eTNSUlIwYcIEDBo0iHFCYaBhvMX4+vpCR0cHjx49wnfffQexWIyWLVuyjoX8/HzIZDK+I4+1bt26YdOmTUhLS0Nubi7/H0s1atSAn58fYmJi0LFjRxQUFDDZ5pNoP/lq0wBw7do1ODg4AABq165NH0qKoAJSzOPHjzFjxgxUqlQJffv2xbp165TOL9CkY8eOoXfv3vjf//6HHTt2oE+fPsw3uVq7di2Cg4PRsWNHtG3bFm3atGG2yY7cihUrYG5ujpUrV8LQ0BApKSkYNWoU00xEe6WmpuLdu3e4cuWKwhB1WnHhX9SEVYx8dJOuri4yMzNhaGiI9PR0ppn+7//+D7t374a5uTkA4NGjR5g0aRL/qYgFwe2MBsDY2FhhMmO9evVQr149doGI1ho/fjzc3Nygq6sLW1tbfPXVVwCAP//8k+lW0kJDBaSYRo0aITMzEy4uLvDw8ICBgQGaN2/ONFPFihX54gEUZhTCBLmHDx/i/v37cHBwwNu3b/mtdll58eIFgoODkZSUpPAp8eTJk8wyEe3Uu3dv2NnZ4Z9//lEYdWVmZoYlS5YwTCYstJz7B8THxyMrKwudO3dmuv7NmjVrUKFCBQwcOBAcxyEiIgIFBQUYO3YsOI5TWNRQUyIiIrB+/Xrk5+fj5MmTePDgAfz9/bF161aNZ5EbNWoUnJ2dsXnzZvz000/YtWsXGjRogKlTpzLLRMh/GRWQ954/f17qrenff//N9C7kQ+PORSIRbt26pcE0hdzc3BAWFgZPT09ERkYCAPr27Yvo6GiNZymaKTIyEi4uLoiKioJMJoOHhwf27dvHLBMh/2XUhPXelClT+E2jBg4ciP379/PPLViwgOmGUkLsb5Avf18U6xm6urq6AAqXln/+/Dlq1qzJvP+KkP9v7/5joq7/OIA/DxDRdDoiPWLQ0FZhzrzBQMzz3GmTJLi4TYUYgy374eRHJWpZGkKbYzEoqFaNtog5mcYBwRr90Z11LYJZEFKyksE0DmkqsK55wB33/QP5fDkRv18+6r2Pu+djczvuc7t7nkNfe//4vF/ejAXkhukDsZu3yXrCIG1oaAi//vorAGD9+vXCj+xYvnw5ent7pS2NDQ0NUCqVQjPFxMRgeHgYaWlp0Ov1CAwMxPbt24VmIvJmLCA3TN/bffM+b9H7vs1mMw4cOICoqCgAk/eqTG2hFeXw4cPYv38/ent7odVqERQUJPR0YAA4dOgQgMmprNjYWFitVvZ2IbqHWEBuGB0dRU9PD5xOp8vjqWsilZWV4cSJE1i9ejUAoKenBwcOHBBaQCIjI3H69Gn09fXB6XQiMjJS2BRWR0cH1q9fD+C/Ta6m1rNMJpNHnttF5A1YQG6w2WzScekAXB6LHoHY7XapeADA6tWrPeJu9LGxMfj5+cHhcKC3txcApP3y7nTs2DFpjer55593Wa8qLy9nASG6R1hAbjAajaIjzCo4OBgGgwF6vR4AUFdXJ7WSFeWLL75AWVkZli9fLhVYhUIh5J6L6WtUN69XecL6FZG3YgGZBwoLC5Gfn4+CggIAQFRUFEpKSoRmqqqqQnNzM1auXCk0B+DZ61dE3owFxMM5HA60tLTg1KlT+PfffwFgxvZZEZRKpUcUD2DyBOWp1rpWq9WlZ7XVahUVi8jr8UbCeUCv18NgMIiO4eKnn35CY2MjNBqNy8mlGo3G7VkyMjJue726utpNSYh8C0cg80BcXByam5uRkJAgOorEZDLBZDKhr68Pfn6ThzorFAohBYQFgkgMjkDmgQ0bNmB4eBhBQUFYtGgRnE4nFAoFWlpahGXSarX4+uuvPeJQRyISgyOQeaC2tlZ0hBnCw8OFHjBJROLxf4B5ICwsTHSEGR566CFkZmZi27ZtUg8VAEhPTxeYiojciQVkHtiwYcMtt6OKnMIaHx9HREQE/vjjD2EZiEgsroHMA/39/dLj0dFRNDY2IiAgAPv27ROYaqbbHYnvDn19fXjjjTcwODgIo9GI3377DUajETk5OcIyEXkz9kSfB8LCwqQ/q1atQl5ensu9DiKNjY2hqakJWVlZ0Ol0QrMUFBRg7969WLp0KYDJGy6bm5uFZiLyZiwg89ClS5dw9epVoRm6urpQUFAAtVqNt956Czt37oTZbBaa6Z9//sHmzZul6T4/Pz+pRwgR3X1cA5kHpq+BTExMwG6348033xSSpaqqCgaDATabDXq9HvX19UhPT0diYqKQPNP5+/tjfHxc+rsaHByU7lEhoruPBWQemL6NNyAgACEhIcKOTj9+/Dji4+NRWFiI8PBwAJ5z3tRzzz2H7OxsDA0NoaKiAvX19Xj11VdFxyLyWlxE92Ce2Ke9p6cHtbW1+OqrrxAZGYmUlBR88MEHHnOa8dmzZ2EymeB0OqHVahETEyM6EpHXYgHxYCkpKbP2aZ9+TQSHw4EzZ86gtrYWP/zwAxISEpCUlAS1Wi0sExG5F6ewPJgn92n39/fH1q1bsXXrVly9ehV1dXUoLi4WUkByc3NvO432/vvvuzENke9gAfFg86XPxf333489e/Zgz549Qj6fHQeJxGAB8WCe3Kfdk6SkpIiOQOSTuAbiwbRa7azXRLWP9UScwiISgyMQD+YpO5s83dQUVmdnJzo7O5GcnAwAaGpqwrp160RGI/JqHIGQbC0tLbh48aLLAr/I03hTU1Px+eefSz1KbDYbsrKyUFNTIywTkTfjCIRkef3119HV1YU1a9YIu6nxZkNDQy5Hyy9YsABDQ0MCExF5NxYQkqW9vR1NTU0eddZUXFwcXnjhBWlRvaGhAXFxcYJTEXkvFhCSRalUio4ww5EjR1BTU4NvvvkGALBlyxbs2rVLcCoi78U1EJLl7bffxoULF9iRkMiHcQRCsoyNjXlMR0Ju4yUSgwWEZDl+/LjoCBJu4yUSg1NYNCc///wzoqOjZ+2IqNFo3Jzov7iNl8i9OAKhOamrq0N0dDQqKytnXFMoFEILCLfxErkXRyDkNY4ePYr+/n6XbbyhoaEoLCwUnIzIO7GAkCxpaWk4efLk/3zOncbHx1FTU4O2tjYAk62Ad+3a5VH3qhB5E05hkSw2m83lZ4fDgZGREUFpJi1YsAAZGRnIyMgQmoPIV7CA0JxUVlaisrISVqsV8fHx0vM2mw1JSUlCMlVVVSEzMxPFxcW33M578OBBAamIvB8LCM3J7t27kZCQgKKiIhw9elR6fsmSJVi2bJmQTAsXLgQA3HfffUI+n8hXcQ2EiIhk4QiEZBkYGMC7776L7u5ul+6IIppcnThx4rbXebwK0b3BAkKyHD58GDt27MD58+dRUlKCkydPIiIiQkiWoqIiPP7443jkkUeEfD6Rr+IUFsny7LPPor6+HklJSWhsbMTExAR2796N06dPuz2LwWBAXV0drl+/jpSUFDzzzDPC1mOIfImf6AA0P03dW7F48WJYLBbY7XZcu3ZNSBa9Xo/q6mqUlZXh2rVrSE1NRV5eHrq7u4XkIfIVnMIiWWJiYjA8PIy0tDTo9XoEBgZi+/btQjOFh4cjKysLISEhKC8vx6ZNm/DYY48JzUTkzTiFRXfMYrHAarUiNDQUS5cudfvnO51OmM1mGAwG/Pnnn3j66aeh0+kQHh7u9ixEvoQFhO6aLVu24MyZM27/XLVajRUrVkCv1yM2NnbGzYQPP/yw2zMR+QIWELprNBrNrMe830tarVZ6rFAoMP1XWqFQCNlaTOQLuAZCd83tugLeS0ajUcjnEvk6FhCakwsXLsx6zW63uzEJEYnGAkJz8uKLL856bepMKiLyDVwDISIiWXgjIRERycICQkREsrCAEBGRLCwgRB7CYrFApVLB4XCIjkL0f+EiOpEMWq0WV65cgb+/PxYvXgy1Wo0jR46wKyL5FI5AiGT6+OOP0d7ejvr6evz+++/49NNPRUcicisWEKI79MADD2DTpk04f/48AKCjowOpqamIiYlBcnIyWltbpddeunQJ6enpUKlUyMrKwrFjx5Cfnw8A+Ouvv/Doo49KN2QODg7i5ZdfRmxsLJ566imcOnVKep+Kigrk5eXh4MGDUKlUSExMxLlz59z4rYlYQIju2OXLl2E2mxEREYHBwUG89NJL2Lt3L9ra2nDo0CHk5uZKvVLy8/Oxbt06tLa2Ijs7Gw0NDbO+72uvvQalUgmz2Yzy8nKUlpaipaVFum40GpGYmIizZ89Cq9WiqKjonn9XoulYQIhk2rdvH1QqFTQaDYKDg5Gbm4uGhgZs3rwZGo0Gfn5+ePLJJ7F27Vp89913sFgsOHfuHHJzcxEYGIiYmBiXgyCnGxgYwC+//IL8/HwsXLgQUVFR2Llzp0vBiY6Ohkajgb+/P3Q6HRtokdvxKBMimT788ENs3LgRbW1t2L9/P4aGhmCxWNDc3AyTySS9zm63Iy4uDn///TeWLVuGRYsWSddCQ0MxMDAw472nXrtkyRLpuQcffBBdXV3SzyEhIdLjoKAgjI6Owm63IyCA/6zJPfibRnSHYmNjodfrUVxcjCeeeAI6nQ7vvPPOjNf19/djZGQE169fl4rIrYoHAKxYsQIjIyOwWq1SERkYGMDKlSvv3RchmiNOYRHdBZmZmfjxxx+hUqlgMplgNpvhcDgwOjqK1tZWXL58GWFhYVi7di0qKiowNjaG9vZ2l5HKdKGhoVCpVCgtLcXo6Ci6u7vx5ZdfIjk52c3fjGh2LCBEd0FwcDB0Oh2qq6vx0Ucf4ZNPPkF8fDw0Gg0+++wzTExMAABKSkrQ0dGBuLg4vPfee9ixYwcCAwNv+Z6lpaXo7++HWq1GdnY2cnJysHHjRnd+LaLb4o2ERAK98sorWLVqFXJzc0VHIZozjkCI3KizsxMXL17ExMQEvv/+e3z77bfYtm2b6FhEsnARnciNrly5gpycHAwPD0OpVKKgoABr1qwRHYtIFk5hERGRLJzCIiIiWVhAiIhIFhYQIiKShQWEiIhkYQEhIiJZWECIiEiW/wACMxgowoDssQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SzyCI7GQQ2MS"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}