05 - Geographic Visualization using plotly and geoplotlib.ipynb

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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Geographic Visualization using plotly/geoplotib - Neerja Doshi"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dataset - The dataset used here is the World Happiness Report data for 2015. It can be found here: https://www.kaggle.com/unsdsn/world-happiness/data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window.Plotly) {{require(['plotly'],function(plotly) {window.Plotly=plotly;});}}</script>"
      ],
      "text/vnd.plotly.v1+html": [
       "<script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window.Plotly) {{require(['plotly'],function(plotly) {window.Plotly=plotly;});}}</script>"
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     "metadata": {},
     "output_type": "display_data"
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   "source": [
    "# import the necessary packages\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import geoplotlib\n",
    "from geoplotlib.colors import ColorMap\n",
    "from geoplotlib.colors import create_set_cmap\n",
    "import pyglet\n",
    "from sklearn.cluster import KMeans\n",
    "from geoplotlib.layers import BaseLayer\n",
    "from geoplotlib.core import BatchPainter\n",
    "from geoplotlib.utils import BoundingBox\n",
    "\n",
    "import plotly.graph_objs as go\n",
    "from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot\n",
    "init_notebook_mode(connected=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       Country          Region  Happiness Rank  Happiness Score  \\\n",
      "0  Switzerland  Western Europe               1            7.587   \n",
      "1      Iceland  Western Europe               2            7.561   \n",
      "2      Denmark  Western Europe               3            7.527   \n",
      "3       Norway  Western Europe               4            7.522   \n",
      "4       Canada   North America               5            7.427   \n",
      "\n",
      "   Standard Error  Economy (GDP per Capita)   Family  \\\n",
      "0         0.03411                   1.39651  1.34951   \n",
      "1         0.04884                   1.30232  1.40223   \n",
      "2         0.03328                   1.32548  1.36058   \n",
      "3         0.03880                   1.45900  1.33095   \n",
      "4         0.03553                   1.32629  1.32261   \n",
      "\n",
      "   Health (Life Expectancy)  Freedom  Trust (Government Corruption)  \\\n",
      "0                   0.94143  0.66557                        0.41978   \n",
      "1                   0.94784  0.62877                        0.14145   \n",
      "2                   0.87464  0.64938                        0.48357   \n",
      "3                   0.88521  0.66973                        0.36503   \n",
      "4                   0.90563  0.63297                        0.32957   \n",
      "\n",
      "   Generosity  Dystopia Residual  \n",
      "0     0.29678            2.51738  \n",
      "1     0.43630            2.70201  \n",
      "2     0.34139            2.49204  \n",
      "3     0.34699            2.46531  \n",
      "4     0.45811            2.45176  \n"
     ]
    },
    {
     "data": {
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       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ISO 3166 Country Code</th>\n",
       "      <th>Country</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AD</td>\n",
       "      <td>Andorra</td>\n",
       "      <td>42.50</td>\n",
       "      <td>1.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AE</td>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>24.00</td>\n",
       "      <td>54.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AF</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>33.00</td>\n",
       "      <td>65.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AG</td>\n",
       "      <td>Antigua and Barbuda</td>\n",
       "      <td>17.05</td>\n",
       "      <td>-61.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AI</td>\n",
       "      <td>Anguilla</td>\n",
       "      <td>18.25</td>\n",
       "      <td>-63.17</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  ISO 3166 Country Code               Country  Latitude  Longitude\n",
       "0                    AD               Andorra     42.50       1.50\n",
       "1                    AE  United Arab Emirates     24.00      54.00\n",
       "2                    AF           Afghanistan     33.00      65.00\n",
       "3                    AG   Antigua and Barbuda     17.05     -61.80\n",
       "4                    AI              Anguilla     18.25     -63.17"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('2015.csv')\n",
    "print(df.head())\n",
    "map_data = pd.read_csv('countries.csv')\n",
    "map_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Choropleth\n",
    "This map shows the happiness rank of all the countries in the world in 2015. The darker the colour, the higher the rank, i.e. the happier the people in that country. It can be seen that the countries of North America (the US, Mexico and Canada), Australia, New Zealand and the western countries of Europe have the happiest citizens. Countries that have lower happiness scores are the ones that are either war struck (eg., Iraq) or are highly underdeveloped as can be said of the countries in Africa like Congo and Chad. The top 5 countries are:\n",
    "1. Switzerland\n",
    "2. Iceland\n",
    "3. Denmark\n",
    "4. Norway\n",
    "5. Canada\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
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         "autocolorscale": true,
         "colorbar": {
          "title": "Happiness"
         },
         "colorscale": [
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           0,
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          ],
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           0.35,
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         "locationmode": "country names",
         "locations": [
          "Switzerland",
          "Iceland",
          "Denmark",
          "Norway",
          "Canada",
          "Finland",
          "Netherlands",
          "Sweden",
          "New Zealand",
          "Australia",
          "Israel",
          "Costa Rica",
          "Austria",
          "Mexico",
          "United States",
          "Brazil",
          "Luxembourg",
          "Ireland",
          "Belgium",
          "United Arab Emirates",
          "United Kingdom",
          "Oman",
          "Venezuela",
          "Singapore",
          "Panama",
          "Germany",
          "Chile",
          "Qatar",
          "France",
          "Argentina",
          "Czech Republic",
          "Uruguay",
          "Colombia",
          "Thailand",
          "Saudi Arabia",
          "Spain",
          "Malta",
          "Taiwan",
          "Kuwait",
          "Suriname",
          "Trinidad and Tobago",
          "El Salvador",
          "Guatemala",
          "Uzbekistan",
          "Slovakia",
          "Japan",
          "South Korea",
          "Ecuador",
          "Bahrain",
          "Italy",
          "Bolivia",
          "Moldova",
          "Paraguay",
          "Kazakhstan",
          "Slovenia",
          "Lithuania",
          "Nicaragua",
          "Peru",
          "Belarus",
          "Poland",
          "Malaysia",
          "Croatia",
          "Libya",
          "Russia",
          "Jamaica",
          "North Cyprus",
          "Cyprus",
          "Algeria",
          "Kosovo",
          "Turkmenistan",
          "Mauritius",
          "Hong Kong",
          "Estonia",
          "Indonesia",
          "Vietnam",
          "Turkey",
          "Kyrgyzstan",
          "Nigeria",
          "Bhutan",
          "Azerbaijan",
          "Pakistan",
          "Jordan",
          "Montenegro",
          "China",
          "Zambia",
          "Romania",
          "Serbia",
          "Portugal",
          "Latvia",
          "Philippines",
          "Somaliland region",
          "Morocco",
          "Macedonia",
          "Mozambique",
          "Albania",
          "Bosnia and Herzegovina",
          "Lesotho",
          "Dominican Republic",
          "Laos",
          "Mongolia",
          "Swaziland",
          "Greece",
          "Lebanon",
          "Hungary",
          "Honduras",
          "Tajikistan",
          "Tunisia",
          "Palestinian Territories",
          "Bangladesh",
          "Iran",
          "Ukraine",
          "Iraq",
          "South Africa",
          "Ghana",
          "Zimbabwe",
          "Liberia",
          "India",
          "Sudan",
          "Haiti",
          "Congo (Kinshasa)",
          "Nepal",
          "Ethiopia",
          "Sierra Leone",
          "Mauritania",
          "Kenya",
          "Djibouti",
          "Armenia",
          "Botswana",
          "Myanmar",
          "Georgia",
          "Malawi",
          "Sri Lanka",
          "Cameroon",
          "Bulgaria",
          "Egypt",
          "Yemen",
          "Angola",
          "Mali",
          "Congo (Brazzaville)",
          "Comoros",
          "Uganda",
          "Senegal",
          "Gabon",
          "Niger",
          "Cambodia",
          "Tanzania",
          "Madagascar",
          "Central African Republic",
          "Chad",
          "Guinea",
          "Ivory Coast",
          "Burkina Faso",
          "Afghanistan",
          "Rwanda",
          "Benin",
          "Syria",
          "Burundi",
          "Togo"
         ],
         "reversescale": true,
         "text": [
          "Switzerland",
          "Iceland",
          "Denmark",
          "Norway",
          "Canada",
          "Finland",
          "Netherlands",
          "Sweden",
          "New Zealand",
          "Australia",
          "Israel",
          "Costa Rica",
          "Austria",
          "Mexico",
          "United States",
          "Brazil",
          "Luxembourg",
          "Ireland",
          "Belgium",
          "United Arab Emirates",
          "United Kingdom",
          "Oman",
          "Venezuela",
          "Singapore",
          "Panama",
          "Germany",
          "Chile",
          "Qatar",
          "France",
          "Argentina",
          "Czech Republic",
          "Uruguay",
          "Colombia",
          "Thailand",
          "Saudi Arabia",
          "Spain",
          "Malta",
          "Taiwan",
          "Kuwait",
          "Suriname",
          "Trinidad and Tobago",
          "El Salvador",
          "Guatemala",
          "Uzbekistan",
          "Slovakia",
          "Japan",
          "South Korea",
          "Ecuador",
          "Bahrain",
          "Italy",
          "Bolivia",
          "Moldova",
          "Paraguay",
          "Kazakhstan",
          "Slovenia",
          "Lithuania",
          "Nicaragua",
          "Peru",
          "Belarus",
          "Poland",
          "Malaysia",
          "Croatia",
          "Libya",
          "Russia",
          "Jamaica",
          "North Cyprus",
          "Cyprus",
          "Algeria",
          "Kosovo",
          "Turkmenistan",
          "Mauritius",
          "Hong Kong",
          "Estonia",
          "Indonesia",
          "Vietnam",
          "Turkey",
          "Kyrgyzstan",
          "Nigeria",
          "Bhutan",
          "Azerbaijan",
          "Pakistan",
          "Jordan",
          "Montenegro",
          "China",
          "Zambia",
          "Romania",
          "Serbia",
          "Portugal",
          "Latvia",
          "Philippines",
          "Somaliland region",
          "Morocco",
          "Macedonia",
          "Mozambique",
          "Albania",
          "Bosnia and Herzegovina",
          "Lesotho",
          "Dominican Republic",
          "Laos",
          "Mongolia",
          "Swaziland",
          "Greece",
          "Lebanon",
          "Hungary",
          "Honduras",
          "Tajikistan",
          "Tunisia",
          "Palestinian Territories",
          "Bangladesh",
          "Iran",
          "Ukraine",
          "Iraq",
          "South Africa",
          "Ghana",
          "Zimbabwe",
          "Liberia",
          "India",
          "Sudan",
          "Haiti",
          "Congo (Kinshasa)",
          "Nepal",
          "Ethiopia",
          "Sierra Leone",
          "Mauritania",
          "Kenya",
          "Djibouti",
          "Armenia",
          "Botswana",
          "Myanmar",
          "Georgia",
          "Malawi",
          "Sri Lanka",
          "Cameroon",
          "Bulgaria",
          "Egypt",
          "Yemen",
          "Angola",
          "Mali",
          "Congo (Brazzaville)",
          "Comoros",
          "Uganda",
          "Senegal",
          "Gabon",
          "Niger",
          "Cambodia",
          "Tanzania",
          "Madagascar",
          "Central African Republic",
          "Chad",
          "Guinea",
          "Ivory Coast",
          "Burkina Faso",
          "Afghanistan",
          "Rwanda",
          "Benin",
          "Syria",
          "Burundi",
          "Togo"
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         "z": [
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       "layout": {
        "geo": {
         "projection": {
          "type": "Orthographic"
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         "showframe": false
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        "title": "Global Happiness"
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       "<div id=\"43aa928b-725a-4e85-b021-ba87927d22c6\" style=\"height: 525px; width: 100%;\" class=\"plotly-graph-div\"></div><script type=\"text/javascript\">require([\"plotly\"], function(Plotly) { window.PLOTLYENV=window.PLOTLYENV || {};window.PLOTLYENV.BASE_URL=\"https://plot.ly\";Plotly.newPlot(\"43aa928b-725a-4e85-b021-ba87927d22c6\", [{\"reversescale\": true, \"autocolorscale\": true, \"colorscale\": [[0, \"rgb(5, 10, 172)\"], [0.35, \"rgb(40, 60, 190)\"], [0.5, \"rgb(70, 100, 245)\"], [0.6, \"rgb(90, 120, 245)\"], [0.7, \"rgb(106, 137, 247)\"], [1, \"rgb(240, 210, 250)\"]], \"text\": [\"Switzerland\", \"Iceland\", \"Denmark\", \"Norway\", \"Canada\", \"Finland\", \"Netherlands\", \"Sweden\", \"New Zealand\", \"Australia\", \"Israel\", \"Costa Rica\", \"Austria\", \"Mexico\", \"United States\", \"Brazil\", \"Luxembourg\", \"Ireland\", \"Belgium\", \"United Arab Emirates\", \"United Kingdom\", \"Oman\", \"Venezuela\", \"Singapore\", \"Panama\", \"Germany\", \"Chile\", \"Qatar\", \"France\", \"Argentina\", \"Czech Republic\", \"Uruguay\", \"Colombia\", \"Thailand\", \"Saudi Arabia\", \"Spain\", \"Malta\", \"Taiwan\", \"Kuwait\", \"Suriname\", \"Trinidad and Tobago\", \"El Salvador\", \"Guatemala\", \"Uzbekistan\", \"Slovakia\", \"Japan\", \"South Korea\", \"Ecuador\", \"Bahrain\", \"Italy\", \"Bolivia\", \"Moldova\", \"Paraguay\", \"Kazakhstan\", \"Slovenia\", \"Lithuania\", \"Nicaragua\", \"Peru\", \"Belarus\", \"Poland\", \"Malaysia\", \"Croatia\", \"Libya\", \"Russia\", \"Jamaica\", \"North Cyprus\", \"Cyprus\", \"Algeria\", \"Kosovo\", \"Turkmenistan\", \"Mauritius\", \"Hong Kong\", \"Estonia\", \"Indonesia\", \"Vietnam\", \"Turkey\", \"Kyrgyzstan\", \"Nigeria\", \"Bhutan\", \"Azerbaijan\", \"Pakistan\", \"Jordan\", \"Montenegro\", \"China\", \"Zambia\", \"Romania\", \"Serbia\", \"Portugal\", \"Latvia\", \"Philippines\", \"Somaliland region\", \"Morocco\", \"Macedonia\", \"Mozambique\", \"Albania\", \"Bosnia and Herzegovina\", \"Lesotho\", 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Zealand\", \"Australia\", \"Israel\", \"Costa Rica\", \"Austria\", \"Mexico\", \"United States\", \"Brazil\", \"Luxembourg\", \"Ireland\", \"Belgium\", \"United Arab Emirates\", \"United Kingdom\", \"Oman\", \"Venezuela\", \"Singapore\", \"Panama\", \"Germany\", \"Chile\", \"Qatar\", \"France\", \"Argentina\", \"Czech Republic\", \"Uruguay\", \"Colombia\", \"Thailand\", \"Saudi Arabia\", \"Spain\", \"Malta\", \"Taiwan\", \"Kuwait\", \"Suriname\", \"Trinidad and Tobago\", \"El Salvador\", \"Guatemala\", \"Uzbekistan\", \"Slovakia\", \"Japan\", \"South Korea\", \"Ecuador\", \"Bahrain\", \"Italy\", \"Bolivia\", \"Moldova\", \"Paraguay\", \"Kazakhstan\", \"Slovenia\", \"Lithuania\", \"Nicaragua\", \"Peru\", \"Belarus\", \"Poland\", \"Malaysia\", \"Croatia\", \"Libya\", \"Russia\", \"Jamaica\", \"North Cyprus\", \"Cyprus\", \"Algeria\", \"Kosovo\", \"Turkmenistan\", \"Mauritius\", \"Hong Kong\", \"Estonia\", \"Indonesia\", \"Vietnam\", \"Turkey\", \"Kyrgyzstan\", \"Nigeria\", \"Bhutan\", \"Azerbaijan\", \"Pakistan\", \"Jordan\", \"Montenegro\", \"China\", \"Zambia\", \"Romania\", \"Serbia\", \"Portugal\", \"Latvia\", \"Philippines\", \"Somaliland region\", \"Morocco\", \"Macedonia\", \"Mozambique\", \"Albania\", \"Bosnia and Herzegovina\", \"Lesotho\", \"Dominican Republic\", \"Laos\", \"Mongolia\", \"Swaziland\", \"Greece\", \"Lebanon\", \"Hungary\", \"Honduras\", \"Tajikistan\", \"Tunisia\", \"Palestinian Territories\", \"Bangladesh\", \"Iran\", \"Ukraine\", \"Iraq\", \"South Africa\", \"Ghana\", \"Zimbabwe\", \"Liberia\", \"India\", \"Sudan\", \"Haiti\", \"Congo (Kinshasa)\", \"Nepal\", \"Ethiopia\", \"Sierra Leone\", \"Mauritania\", \"Kenya\", \"Djibouti\", \"Armenia\", \"Botswana\", \"Myanmar\", \"Georgia\", \"Malawi\", \"Sri Lanka\", \"Cameroon\", \"Bulgaria\", \"Egypt\", \"Yemen\", \"Angola\", \"Mali\", \"Congo (Brazzaville)\", \"Comoros\", \"Uganda\", \"Senegal\", \"Gabon\", \"Niger\", \"Cambodia\", \"Tanzania\", \"Madagascar\", \"Central African Republic\", \"Chad\", \"Guinea\", \"Ivory Coast\", \"Burkina Faso\", \"Afghanistan\", \"Rwanda\", \"Benin\", \"Syria\", \"Burundi\", \"Togo\"], \"colorbar\": {\"title\": \"Happiness\"}, \"type\": \"choropleth\", \"z\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 82, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158], \"locationmode\": \"country names\"}], {\"geo\": {\"projection\": {\"type\": \"Orthographic\"}, \"showframe\": false}, \"title\": \"Global Happiness\"}, {\"linkText\": \"Export to plot.ly\", \"showLink\": true})});</script>"
      ],
      "text/vnd.plotly.v1+html": [
       "<div id=\"43aa928b-725a-4e85-b021-ba87927d22c6\" style=\"height: 525px; width: 100%;\" class=\"plotly-graph-div\"></div><script type=\"text/javascript\">require([\"plotly\"], function(Plotly) { window.PLOTLYENV=window.PLOTLYENV || {};window.PLOTLYENV.BASE_URL=\"https://plot.ly\";Plotly.newPlot(\"43aa928b-725a-4e85-b021-ba87927d22c6\", [{\"reversescale\": true, \"autocolorscale\": true, \"colorscale\": [[0, \"rgb(5, 10, 172)\"], [0.35, \"rgb(40, 60, 190)\"], [0.5, \"rgb(70, 100, 245)\"], [0.6, \"rgb(90, 120, 245)\"], [0.7, \"rgb(106, 137, 247)\"], [1, \"rgb(240, 210, 250)\"]], \"text\": [\"Switzerland\", \"Iceland\", \"Denmark\", \"Norway\", \"Canada\", \"Finland\", \"Netherlands\", \"Sweden\", \"New Zealand\", \"Australia\", \"Israel\", \"Costa Rica\", \"Austria\", \"Mexico\", \"United States\", \"Brazil\", \"Luxembourg\", \"Ireland\", \"Belgium\", \"United Arab Emirates\", \"United Kingdom\", \"Oman\", \"Venezuela\", \"Singapore\", \"Panama\", \"Germany\", \"Chile\", \"Qatar\", \"France\", \"Argentina\", \"Czech Republic\", \"Uruguay\", \"Colombia\", \"Thailand\", \"Saudi Arabia\", \"Spain\", \"Malta\", \"Taiwan\", \"Kuwait\", \"Suriname\", \"Trinidad and Tobago\", \"El Salvador\", \"Guatemala\", \"Uzbekistan\", \"Slovakia\", \"Japan\", \"South Korea\", \"Ecuador\", \"Bahrain\", \"Italy\", \"Bolivia\", \"Moldova\", \"Paraguay\", \"Kazakhstan\", \"Slovenia\", \"Lithuania\", \"Nicaragua\", \"Peru\", \"Belarus\", \"Poland\", \"Malaysia\", \"Croatia\", \"Libya\", \"Russia\", \"Jamaica\", \"North Cyprus\", \"Cyprus\", \"Algeria\", \"Kosovo\", \"Turkmenistan\", \"Mauritius\", \"Hong Kong\", \"Estonia\", \"Indonesia\", \"Vietnam\", \"Turkey\", \"Kyrgyzstan\", \"Nigeria\", \"Bhutan\", \"Azerbaijan\", \"Pakistan\", \"Jordan\", \"Montenegro\", \"China\", \"Zambia\", \"Romania\", \"Serbia\", \"Portugal\", \"Latvia\", \"Philippines\", \"Somaliland region\", \"Morocco\", \"Macedonia\", \"Mozambique\", \"Albania\", \"Bosnia and Herzegovina\", \"Lesotho\", \"Dominican Republic\", \"Laos\", \"Mongolia\", \"Swaziland\", \"Greece\", \"Lebanon\", \"Hungary\", \"Honduras\", \"Tajikistan\", \"Tunisia\", \"Palestinian Territories\", \"Bangladesh\", \"Iran\", \"Ukraine\", \"Iraq\", \"South Africa\", \"Ghana\", \"Zimbabwe\", \"Liberia\", \"India\", \"Sudan\", \"Haiti\", \"Congo (Kinshasa)\", \"Nepal\", \"Ethiopia\", \"Sierra Leone\", \"Mauritania\", \"Kenya\", \"Djibouti\", \"Armenia\", \"Botswana\", \"Myanmar\", \"Georgia\", \"Malawi\", \"Sri Lanka\", \"Cameroon\", \"Bulgaria\", \"Egypt\", \"Yemen\", \"Angola\", \"Mali\", \"Congo (Brazzaville)\", \"Comoros\", \"Uganda\", \"Senegal\", \"Gabon\", \"Niger\", \"Cambodia\", \"Tanzania\", \"Madagascar\", \"Central African Republic\", \"Chad\", \"Guinea\", \"Ivory Coast\", \"Burkina Faso\", \"Afghanistan\", \"Rwanda\", \"Benin\", \"Syria\", \"Burundi\", \"Togo\"], \"locations\": [\"Switzerland\", \"Iceland\", \"Denmark\", \"Norway\", \"Canada\", \"Finland\", \"Netherlands\", \"Sweden\", \"New Zealand\", \"Australia\", \"Israel\", \"Costa Rica\", \"Austria\", \"Mexico\", \"United States\", \"Brazil\", \"Luxembourg\", \"Ireland\", \"Belgium\", \"United Arab Emirates\", \"United Kingdom\", \"Oman\", \"Venezuela\", \"Singapore\", \"Panama\", \"Germany\", \"Chile\", \"Qatar\", \"France\", \"Argentina\", \"Czech Republic\", \"Uruguay\", \"Colombia\", \"Thailand\", \"Saudi Arabia\", \"Spain\", \"Malta\", \"Taiwan\", \"Kuwait\", \"Suriname\", \"Trinidad and Tobago\", \"El Salvador\", \"Guatemala\", \"Uzbekistan\", \"Slovakia\", \"Japan\", \"South Korea\", \"Ecuador\", \"Bahrain\", \"Italy\", \"Bolivia\", \"Moldova\", \"Paraguay\", \"Kazakhstan\", \"Slovenia\", \"Lithuania\", \"Nicaragua\", \"Peru\", \"Belarus\", \"Poland\", \"Malaysia\", \"Croatia\", \"Libya\", \"Russia\", \"Jamaica\", \"North Cyprus\", \"Cyprus\", \"Algeria\", \"Kosovo\", \"Turkmenistan\", \"Mauritius\", \"Hong Kong\", \"Estonia\", \"Indonesia\", \"Vietnam\", \"Turkey\", \"Kyrgyzstan\", \"Nigeria\", \"Bhutan\", \"Azerbaijan\", \"Pakistan\", \"Jordan\", \"Montenegro\", \"China\", \"Zambia\", \"Romania\", \"Serbia\", \"Portugal\", \"Latvia\", \"Philippines\", \"Somaliland region\", \"Morocco\", \"Macedonia\", \"Mozambique\", \"Albania\", \"Bosnia and Herzegovina\", \"Lesotho\", \"Dominican Republic\", \"Laos\", \"Mongolia\", \"Swaziland\", \"Greece\", \"Lebanon\", \"Hungary\", \"Honduras\", \"Tajikistan\", \"Tunisia\", \"Palestinian Territories\", \"Bangladesh\", \"Iran\", \"Ukraine\", \"Iraq\", \"South Africa\", \"Ghana\", \"Zimbabwe\", \"Liberia\", \"India\", \"Sudan\", \"Haiti\", \"Congo (Kinshasa)\", \"Nepal\", \"Ethiopia\", \"Sierra Leone\", \"Mauritania\", \"Kenya\", \"Djibouti\", \"Armenia\", \"Botswana\", \"Myanmar\", \"Georgia\", \"Malawi\", \"Sri Lanka\", \"Cameroon\", \"Bulgaria\", \"Egypt\", \"Yemen\", \"Angola\", \"Mali\", \"Congo (Brazzaville)\", \"Comoros\", \"Uganda\", \"Senegal\", \"Gabon\", \"Niger\", \"Cambodia\", \"Tanzania\", \"Madagascar\", \"Central African Republic\", \"Chad\", \"Guinea\", \"Ivory Coast\", \"Burkina Faso\", \"Afghanistan\", \"Rwanda\", \"Benin\", \"Syria\", \"Burundi\", \"Togo\"], \"colorbar\": {\"title\": \"Happiness\"}, \"type\": \"choropleth\", \"z\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 82, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158], \"locationmode\": \"country names\"}], {\"geo\": {\"projection\": {\"type\": \"Orthographic\"}, \"showframe\": false}, \"title\": \"Global Happiness\"}, {\"linkText\": \"Export to plot.ly\", \"showLink\": true})});</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "scl = [[0,\"rgb(5, 10, 172)\"],[0.35,\"rgb(40, 60, 190)\"],[0.5,\"rgb(70, 100, 245)\"],\\\n",
    "            [0.6,\"rgb(90, 120, 245)\"],[0.7,\"rgb(106, 137, 247)\"],[1,\"rgb(240, 210, 250)\"]]\n",
    "\n",
    "\n",
    "# scl = [[0.0, 'rgb(50,10,143)'],[0.2, 'rgb(117,107,177)'],[0.4, 'rgb(158,154,200)'],\\\n",
    "#             [0.6, 'rgb(188,189,220)'],[0.8, 'rgb(218,208,235)'],[1.0, 'rgb(250,240,255)']]\n",
    "data = dict(type = 'choropleth', \n",
    "            colorscale = scl,\n",
    "            autocolorscale = True,\n",
    "            reversescale = True,\n",
    "           locations = df['Country'],\n",
    "           locationmode = 'country names',\n",
    "           z = df['Happiness Rank'], \n",
    "           text = df['Country'],\n",
    "           colorbar = {'title':'Happiness'})\n",
    "layout = dict(title = 'Global Happiness', \n",
    "             geo = dict(showframe = False, \n",
    "                       projection = {'type': 'Orthographic'}))\n",
    "\n",
    "choromap3 = go.Figure(data = [data], layout=layout)\n",
    "iplot(choromap3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Symbol Map\n",
    "To take our analysis further, we plot a symbol map where the size of the circles represents the Happiness Score while the colour represents the GDP of the countries. Larger the circle, happier the citizens while darker the circle, higher the GDP.\n",
    "From this plot, we can see that the top 5 countries we have seen above definitely have a much higher GDP. ** This seems to imply that more well off a country is economically, the happier its citizens are. ** Also, the underdeveloped countries like Chad, Congo, Burundi and Togo have a very low GDP and also a very low happiness score. While we cannot directly say that low GDP implies lower happiness, it seems like an important factor. This trend remain consistent throughout all the countries. ** There are almost no countries that have a high GDP but low happiness index or vice versa. **\n",
    "\n",
    "Also geographic location and neighbours may be playing an important role. We can clusters/regions with countries having similar GDPs and similar Happiness Scores. ** So countries that have a good economy and good relations with their neighbours benefit from mutual growth and this is also reflected in their happiness scores. Countries that have disturbed neighbourhoods, like in middle east Asia (Iraq, Afghanistan, etc.), show much lower growth/economic prosperity as well as lower happiness scores. **\n",
    "\n",
    "One thing that can also be noted is that in general, countries which are known for their lower population densities (https://www.worldatlas.com/articles/the-10-least-densely-populated-places-in-the-world-2015.html) like Denmark and Iceland are much happier than the more densly populated countries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>Happiness Rank</th>\n",
       "      <th>Happiness Score</th>\n",
       "      <th>Standard Error</th>\n",
       "      <th>Economy (GDP per Capita)</th>\n",
       "      <th>Family</th>\n",
       "      <th>Health (Life Expectancy)</th>\n",
       "      <th>Freedom</th>\n",
       "      <th>Trust (Government Corruption)</th>\n",
       "      <th>Generosity</th>\n",
       "      <th>Dystopia Residual</th>\n",
       "      <th>ISO 3166 Country Code</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Switzerland</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>1</td>\n",
       "      <td>7.587</td>\n",
       "      <td>0.03411</td>\n",
       "      <td>1.39651</td>\n",
       "      <td>1.34951</td>\n",
       "      <td>0.94143</td>\n",
       "      <td>0.66557</td>\n",
       "      <td>0.41978</td>\n",
       "      <td>0.29678</td>\n",
       "      <td>2.51738</td>\n",
       "      <td>CH</td>\n",
       "      <td>47.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Iceland</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>2</td>\n",
       "      <td>7.561</td>\n",
       "      <td>0.04884</td>\n",
       "      <td>1.30232</td>\n",
       "      <td>1.40223</td>\n",
       "      <td>0.94784</td>\n",
       "      <td>0.62877</td>\n",
       "      <td>0.14145</td>\n",
       "      <td>0.43630</td>\n",
       "      <td>2.70201</td>\n",
       "      <td>IS</td>\n",
       "      <td>65.0</td>\n",
       "      <td>-18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Denmark</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>3</td>\n",
       "      <td>7.527</td>\n",
       "      <td>0.03328</td>\n",
       "      <td>1.32548</td>\n",
       "      <td>1.36058</td>\n",
       "      <td>0.87464</td>\n",
       "      <td>0.64938</td>\n",
       "      <td>0.48357</td>\n",
       "      <td>0.34139</td>\n",
       "      <td>2.49204</td>\n",
       "      <td>DK</td>\n",
       "      <td>56.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Norway</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>4</td>\n",
       "      <td>7.522</td>\n",
       "      <td>0.03880</td>\n",
       "      <td>1.45900</td>\n",
       "      <td>1.33095</td>\n",
       "      <td>0.88521</td>\n",
       "      <td>0.66973</td>\n",
       "      <td>0.36503</td>\n",
       "      <td>0.34699</td>\n",
       "      <td>2.46531</td>\n",
       "      <td>NO</td>\n",
       "      <td>62.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Canada</td>\n",
       "      <td>North America</td>\n",
       "      <td>5</td>\n",
       "      <td>7.427</td>\n",
       "      <td>0.03553</td>\n",
       "      <td>1.32629</td>\n",
       "      <td>1.32261</td>\n",
       "      <td>0.90563</td>\n",
       "      <td>0.63297</td>\n",
       "      <td>0.32957</td>\n",
       "      <td>0.45811</td>\n",
       "      <td>2.45176</td>\n",
       "      <td>CA</td>\n",
       "      <td>60.0</td>\n",
       "      <td>-95.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Country          Region  Happiness Rank  Happiness Score  \\\n",
       "0  Switzerland  Western Europe               1            7.587   \n",
       "1      Iceland  Western Europe               2            7.561   \n",
       "2      Denmark  Western Europe               3            7.527   \n",
       "3       Norway  Western Europe               4            7.522   \n",
       "4       Canada   North America               5            7.427   \n",
       "\n",
       "   Standard Error  Economy (GDP per Capita)   Family  \\\n",
       "0         0.03411                   1.39651  1.34951   \n",
       "1         0.04884                   1.30232  1.40223   \n",
       "2         0.03328                   1.32548  1.36058   \n",
       "3         0.03880                   1.45900  1.33095   \n",
       "4         0.03553                   1.32629  1.32261   \n",
       "\n",
       "   Health (Life Expectancy)  Freedom  Trust (Government Corruption)  \\\n",
       "0                   0.94143  0.66557                        0.41978   \n",
       "1                   0.94784  0.62877                        0.14145   \n",
       "2                   0.87464  0.64938                        0.48357   \n",
       "3                   0.88521  0.66973                        0.36503   \n",
       "4                   0.90563  0.63297                        0.32957   \n",
       "\n",
       "   Generosity  Dystopia Residual ISO 3166 Country Code  Latitude  Longitude  \n",
       "0     0.29678            2.51738                    CH      47.0        8.0  \n",
       "1     0.43630            2.70201                    IS      65.0      -18.0  \n",
       "2     0.34139            2.49204                    DK      56.0       10.0  \n",
       "3     0.34699            2.46531                    NO      62.0       10.0  \n",
       "4     0.45811            2.45176                    CA      60.0      -95.0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.merge(map_data, how='left', on = ['Country'])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2.839, 7.5870000000000006)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Happiness Score'].min(), df['Happiness Score'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
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          "Switzerland<br>Happiness Score 7.587",
          "Iceland<br>Happiness Score 7.561",
          "Denmark<br>Happiness Score 7.527",
          "Norway<br>Happiness Score 7.522",
          "Canada<br>Happiness Score 7.427",
          "Finland<br>Happiness Score 7.406",
          "Netherlands<br>Happiness Score 7.378",
          "Sweden<br>Happiness Score 7.364",
          "New Zealand<br>Happiness Score 7.286",
          "Australia<br>Happiness Score 7.284",
          "Israel<br>Happiness Score 7.278",
          "Costa Rica<br>Happiness Score 7.226",
          "Austria<br>Happiness Score 7.2",
          "Mexico<br>Happiness Score 7.187",
          "United States<br>Happiness Score 7.119",
          "Brazil<br>Happiness Score 6.983",
          "Luxembourg<br>Happiness Score 6.946",
          "Ireland<br>Happiness Score 6.94",
          "Belgium<br>Happiness Score 6.937",
          "United Arab Emirates<br>Happiness Score 6.901",
          "United Kingdom<br>Happiness Score 6.867",
          "Oman<br>Happiness Score 6.853",
          "Venezuela<br>Happiness Score 6.81",
          "Singapore<br>Happiness Score 6.798",
          "Panama<br>Happiness Score 6.786",
          "Germany<br>Happiness Score 6.75",
          "Chile<br>Happiness Score 6.67",
          "Qatar<br>Happiness Score 6.611",
          "France<br>Happiness Score 6.575",
          "Argentina<br>Happiness Score 6.574",
          "Czech Republic<br>Happiness Score 6.505",
          "Uruguay<br>Happiness Score 6.485",
          "Colombia<br>Happiness Score 6.477",
          "Thailand<br>Happiness Score 6.455",
          "Saudi Arabia<br>Happiness Score 6.411",
          "Spain<br>Happiness Score 6.329",
          "Malta<br>Happiness Score 6.302",
          "Taiwan<br>Happiness Score 6.298",
          "Kuwait<br>Happiness Score 6.295",
          "Suriname<br>Happiness Score 6.269",
          "Trinidad and Tobago<br>Happiness Score 6.168",
          "El Salvador<br>Happiness Score 6.13",
          "Guatemala<br>Happiness Score 6.123",
          "Uzbekistan<br>Happiness Score 6.003",
          "Slovakia<br>Happiness Score 5.995",
          "Japan<br>Happiness Score 5.987",
          "South Korea<br>Happiness Score 5.984",
          "Ecuador<br>Happiness Score 5.975",
          "Bahrain<br>Happiness Score 5.96",
          "Italy<br>Happiness Score 5.948",
          "Bolivia<br>Happiness Score 5.89",
          "Moldova<br>Happiness Score 5.889",
          "Paraguay<br>Happiness Score 5.878",
          "Kazakhstan<br>Happiness Score 5.855",
          "Slovenia<br>Happiness Score 5.848",
          "Lithuania<br>Happiness Score 5.833",
          "Nicaragua<br>Happiness Score 5.828",
          "Peru<br>Happiness Score 5.824",
          "Belarus<br>Happiness Score 5.813",
          "Poland<br>Happiness Score 5.791",
          "Malaysia<br>Happiness Score 5.77",
          "Croatia<br>Happiness Score 5.759",
          "Libya<br>Happiness Score 5.754",
          "Russia<br>Happiness Score 5.716",
          "Jamaica<br>Happiness Score 5.709",
          "North Cyprus<br>Happiness Score 5.695",
          "Cyprus<br>Happiness Score 5.689",
          "Algeria<br>Happiness Score 5.605",
          "Kosovo<br>Happiness Score 5.589",
          "Turkmenistan<br>Happiness Score 5.548",
          "Mauritius<br>Happiness Score 5.477",
          "Hong Kong<br>Happiness Score 5.474",
          "Estonia<br>Happiness Score 5.429",
          "Indonesia<br>Happiness Score 5.399",
          "Vietnam<br>Happiness Score 5.36",
          "Turkey<br>Happiness Score 5.332",
          "Kyrgyzstan<br>Happiness Score 5.286",
          "Nigeria<br>Happiness Score 5.268",
          "Bhutan<br>Happiness Score 5.253",
          "Azerbaijan<br>Happiness Score 5.212",
          "Pakistan<br>Happiness Score 5.194",
          "Jordan<br>Happiness Score 5.192",
          "Montenegro<br>Happiness Score 5.192",
          "China<br>Happiness Score 5.14",
          "Zambia<br>Happiness Score 5.129",
          "Romania<br>Happiness Score 5.124",
          "Serbia<br>Happiness Score 5.123",
          "Portugal<br>Happiness Score 5.102",
          "Latvia<br>Happiness Score 5.098",
          "Philippines<br>Happiness Score 5.073",
          "Somaliland region<br>Happiness Score 5.057",
          "Morocco<br>Happiness Score 5.013",
          "Macedonia<br>Happiness Score 5.007",
          "Mozambique<br>Happiness Score 4.971",
          "Albania<br>Happiness Score 4.959",
          "Bosnia and Herzegovina<br>Happiness Score 4.949",
          "Lesotho<br>Happiness Score 4.898",
          "Dominican Republic<br>Happiness Score 4.885",
          "Laos<br>Happiness Score 4.876",
          "Mongolia<br>Happiness Score 4.874",
          "Swaziland<br>Happiness Score 4.867",
          "Greece<br>Happiness Score 4.857",
          "Lebanon<br>Happiness Score 4.839",
          "Hungary<br>Happiness Score 4.8",
          "Honduras<br>Happiness Score 4.788",
          "Tajikistan<br>Happiness Score 4.786",
          "Tunisia<br>Happiness Score 4.739",
          "Palestinian Territories<br>Happiness Score 4.715",
          "Bangladesh<br>Happiness Score 4.694",
          "Iran<br>Happiness Score 4.686",
          "Ukraine<br>Happiness Score 4.681",
          "Iraq<br>Happiness Score 4.677",
          "South Africa<br>Happiness Score 4.642",
          "Ghana<br>Happiness Score 4.633",
          "Zimbabwe<br>Happiness Score 4.61",
          "Liberia<br>Happiness Score 4.571",
          "India<br>Happiness Score 4.565",
          "Sudan<br>Happiness Score 4.55",
          "Haiti<br>Happiness Score 4.518",
          "Congo (Kinshasa)<br>Happiness Score 4.517",
          "Nepal<br>Happiness Score 4.514",
          "Ethiopia<br>Happiness Score 4.512",
          "Sierra Leone<br>Happiness Score 4.507",
          "Mauritania<br>Happiness Score 4.436",
          "Kenya<br>Happiness Score 4.419",
          "Djibouti<br>Happiness Score 4.369",
          "Armenia<br>Happiness Score 4.35",
          "Botswana<br>Happiness Score 4.332",
          "Myanmar<br>Happiness Score 4.307",
          "Georgia<br>Happiness Score 4.297",
          "Malawi<br>Happiness Score 4.292",
          "Sri Lanka<br>Happiness Score 4.271",
          "Cameroon<br>Happiness Score 4.252",
          "Bulgaria<br>Happiness Score 4.218",
          "Egypt<br>Happiness Score 4.194",
          "Yemen<br>Happiness Score 4.077",
          "Angola<br>Happiness Score 4.033",
          "Mali<br>Happiness Score 3.995",
          "Congo (Brazzaville)<br>Happiness Score 3.989",
          "Comoros<br>Happiness Score 3.956",
          "Uganda<br>Happiness Score 3.931",
          "Senegal<br>Happiness Score 3.904",
          "Gabon<br>Happiness Score 3.896",
          "Niger<br>Happiness Score 3.845",
          "Cambodia<br>Happiness Score 3.819",
          "Tanzania<br>Happiness Score 3.781",
          "Madagascar<br>Happiness Score 3.681",
          "Central African Republic<br>Happiness Score 3.678",
          "Chad<br>Happiness Score 3.667",
          "Guinea<br>Happiness Score 3.656",
          "Ivory Coast<br>Happiness Score 3.655",
          "Burkina Faso<br>Happiness Score 3.587",
          "Afghanistan<br>Happiness Score 3.575",
          "Rwanda<br>Happiness Score 3.465",
          "Benin<br>Happiness Score 3.34",
          "Syria<br>Happiness Score 3.006",
          "Burundi<br>Happiness Score 2.905",
          "Togo<br>Happiness Score 2.839"
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        "title": "Happiness Scores by GDP"
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Score 5.007\", \"Mozambique<br>Happiness Score 4.971\", \"Albania<br>Happiness Score 4.959\", \"Bosnia and Herzegovina<br>Happiness Score 4.949\", \"Lesotho<br>Happiness Score 4.898\", \"Dominican Republic<br>Happiness Score 4.885\", \"Laos<br>Happiness Score 4.876\", \"Mongolia<br>Happiness Score 4.874\", \"Swaziland<br>Happiness Score 4.867\", \"Greece<br>Happiness Score 4.857\", \"Lebanon<br>Happiness Score 4.839\", \"Hungary<br>Happiness Score 4.8\", \"Honduras<br>Happiness Score 4.788\", \"Tajikistan<br>Happiness Score 4.786\", \"Tunisia<br>Happiness Score 4.739\", \"Palestinian Territories<br>Happiness Score 4.715\", \"Bangladesh<br>Happiness Score 4.694\", \"Iran<br>Happiness Score 4.686\", \"Ukraine<br>Happiness Score 4.681\", \"Iraq<br>Happiness Score 4.677\", \"South Africa<br>Happiness Score 4.642\", \"Ghana<br>Happiness Score 4.633\", \"Zimbabwe<br>Happiness Score 4.61\", \"Liberia<br>Happiness Score 4.571\", \"India<br>Happiness Score 4.565\", \"Sudan<br>Happiness Score 4.55\", \"Haiti<br>Happiness Score 4.518\", \"Congo (Kinshasa)<br>Happiness Score 4.517\", \"Nepal<br>Happiness Score 4.514\", \"Ethiopia<br>Happiness Score 4.512\", \"Sierra Leone<br>Happiness Score 4.507\", \"Mauritania<br>Happiness Score 4.436\", \"Kenya<br>Happiness Score 4.419\", \"Djibouti<br>Happiness Score 4.369\", \"Armenia<br>Happiness Score 4.35\", \"Botswana<br>Happiness Score 4.332\", \"Myanmar<br>Happiness Score 4.307\", \"Georgia<br>Happiness Score 4.297\", \"Malawi<br>Happiness Score 4.292\", \"Sri Lanka<br>Happiness Score 4.271\", \"Cameroon<br>Happiness Score 4.252\", \"Bulgaria<br>Happiness Score 4.218\", \"Egypt<br>Happiness Score 4.194\", \"Yemen<br>Happiness Score 4.077\", \"Angola<br>Happiness Score 4.033\", \"Mali<br>Happiness Score 3.995\", \"Congo (Brazzaville)<br>Happiness Score 3.989\", \"Comoros<br>Happiness Score 3.956\", \"Uganda<br>Happiness Score 3.931\", \"Senegal<br>Happiness Score 3.904\", \"Gabon<br>Happiness Score 3.896\", \"Niger<br>Happiness Score 3.845\", \"Cambodia<br>Happiness Score 3.819\", \"Tanzania<br>Happiness Score 3.781\", \"Madagascar<br>Happiness Score 3.681\", \"Central African Republic<br>Happiness Score 3.678\", \"Chad<br>Happiness Score 3.667\", \"Guinea<br>Happiness Score 3.656\", \"Ivory Coast<br>Happiness Score 3.655\", \"Burkina Faso<br>Happiness Score 3.587\", \"Afghanistan<br>Happiness Score 3.575\", \"Rwanda<br>Happiness Score 3.465\", \"Benin<br>Happiness Score 3.34\", \"Syria<br>Happiness Score 3.006\", \"Burundi<br>Happiness Score 2.905\", \"Togo<br>Happiness Score 2.839\"], \"lon\": [8.0, -18.0, 10.0, 10.0, -95.0, 26.0, 5.75, 15.0, 174.0, 133.0, 34.75, -84.0, 13.33, -102.0, -97.0, -55.0, 6.17, -8.0, 4.0, 54.0, -2.0, 57.0, -66.0, 103.8, -80.0, 9.0, -71.0, 51.25, 2.0, -64.0, 15.5, -56.0, -72.0, 100.0, 45.0, -4.0, 14.58, 121.0, 47.66, -56.0, -61.0, -88.92, -90.25, 64.0, 19.5, 138.0, null, -77.5, 50.55, 12.83, -65.0, null, -58.0, 68.0, 15.0, 24.0, -85.0, -76.0, 28.0, 20.0, 112.5, 15.5, null, null, -77.5, null, 33.0, 3.0, null, 60.0, 57.55, 114.17, 26.0, 120.0, 106.0, 35.0, 75.0, 8.0, 90.5, 47.5, 70.0, 36.0, 19.0, 105.0, 30.0, 25.0, 21.0, -8.0, 25.0, 122.0, null, -5.0, 22.0, 35.0, 20.0, 18.0, 28.5, -70.67, null, 105.0, 31.5, 22.0, 35.83, 20.0, -86.5, 71.0, 9.0, null, 90.0, null, 32.0, 44.0, 24.0, -2.0, 30.0, -9.5, 77.0, 30.0, -72.42, null, 84.0, 38.0, -11.5, -12.0, 38.0, 43.0, 45.0, 24.0, 98.0, 43.5, 34.0, 81.0, 12.0, 25.0, 30.0, 48.0, 18.5, -4.0, null, 44.25, 32.0, -14.0, 11.75, 8.0, 105.0, null, 47.0, 21.0, 19.0, -10.0, null, -2.0, 65.0, 30.0, 2.25, null, 30.0, 1.17], \"lat\": [47.0, 65.0, 56.0, 62.0, 60.0, 64.0, 52.5, 62.0, -41.0, -27.0, 31.5, 10.0, 47.33, 23.0, 38.0, -10.0, 49.75, 53.0, 50.83, 24.0, 54.0, 21.0, 8.0, 1.37, 9.0, 51.0, -30.0, 25.5, 46.0, -34.0, 49.75, -33.0, 4.0, 15.0, 25.0, 40.0, 35.83, 23.5, 29.34, 4.0, 11.0, 13.83, 15.5, 41.0, 48.67, 36.0, null, -2.0, 26.0, 42.83, -17.0, null, -23.0, 48.0, 46.0, 56.0, 13.0, -10.0, 53.0, 52.0, 2.5, 45.17, null, null, 18.25, null, 35.0, 28.0, null, 40.0, -20.28, 22.25, 59.0, -5.0, 16.0, 39.0, 41.0, 10.0, 27.5, 40.5, 30.0, 31.0, 42.0, 35.0, -15.0, 46.0, 44.0, 39.5, 57.0, 13.0, null, 32.0, 41.83, -18.25, 41.0, 44.0, -29.5, 19.0, null, 46.0, -26.5, 39.0, 33.83, 47.0, 15.0, 39.0, 34.0, null, 24.0, null, 49.0, 33.0, -29.0, 8.0, -20.0, 6.5, 20.0, 15.0, 19.0, null, 28.0, 8.0, 8.5, 20.0, 1.0, 11.5, 40.0, -22.0, 22.0, 42.0, -13.5, 7.0, 6.0, 43.0, 27.0, 15.0, -12.5, 17.0, null, -12.17, 1.0, 14.0, -1.0, 16.0, 13.0, null, -20.0, 7.0, 15.0, 11.0, null, 13.0, 33.0, -2.0, 9.5, null, -3.5, 8.0], \"mode\": \"markers\", \"type\": \"scattergeo\", \"marker\": {\"opacity\": 0.8, \"reversescale\": true, \"autocolorscale\": false, \"cmin\": 0, \"colorscale\": [[0, \"rgb(5, 10, 172)\"], [0.35, \"rgb(40, 60, 190)\"], [0.5, \"rgb(70, 100, 245)\"], [0.6, \"rgb(90, 120, 245)\"], [0.7, \"rgb(106, 137, 247)\"], [1, \"rgb(220, 220, 220)\"]], \"color\": [1.3965100000000001, 1.3023200000000001, 1.32548, 1.459, 1.32629, 1.29025, 1.32944, 1.33171, 1.2501799999999998, 1.33358, 1.2285700000000002, 0.95578, 1.33723, 1.02054, 1.3945100000000001, 0.98124, 1.5639100000000001, 1.33596, 1.30782, 1.42727, 1.26637, 1.36011, 1.04424, 1.52186, 1.0635299999999999, 1.32792, 1.10715, 1.69042, 1.27778, 1.05351, 1.17898, 1.06166, 0.91861, 0.9669, 1.39541, 1.23011, 1.2074, 1.29098, 1.5542200000000002, 0.99534, 1.21183, 0.76454, 0.74553, 0.63244, 1.1689100000000001, 1.27074, 1.24461, 0.8640200000000001, 1.32376, 1.25114, 0.68133, 0.59448, 0.75985, 1.1225399999999999, 1.18498, 1.14723, 0.59325, 0.9001899999999999, 1.0319200000000002, 1.12555, 1.12486, 1.08254, 1.13145, 1.13764, 0.81038, 1.2080600000000001, 1.20813, 0.93929, 0.80148, 0.95847, 1.0076100000000001, 1.38604, 1.15174, 0.8282700000000001, 0.63216, 1.06098, 0.47428000000000003, 0.65435, 0.77042, 1.02389, 0.59543, 0.9019799999999999, 0.9743799999999999, 0.89012, 0.47038, 1.04345, 0.92053, 1.15991, 1.11312, 0.70532, 0.18847, 0.7347899999999999, 0.91851, 0.08308, 0.8786700000000001, 0.8322299999999999, 0.37545, 0.8953700000000001, 0.5906600000000001, 0.82819, 0.71206, 1.15406, 1.0256399999999999, 1.12094, 0.59532, 0.39047, 0.88113, 0.59867, 0.39753, 1.0088, 0.79907, 0.98549, 0.9204899999999999, 0.5455800000000001, 0.271, 0.0712, 0.64499, 0.52107, 0.26673, 0.0, 0.35996999999999996, 0.19072999999999998, 0.33024000000000003, 0.45407, 0.36471, 0.44025, 0.7682100000000001, 0.99355, 0.27108000000000004, 0.7419, 0.016040000000000002, 0.83524, 0.4225, 1.01216, 0.8818, 0.5464899999999999, 0.75778, 0.26074, 0.67866, 0.23906, 0.21101999999999999, 0.36498, 1.06024, 0.0694, 0.46038, 0.2852, 0.20824, 0.0785, 0.34193, 0.17417, 0.46534, 0.25811999999999996, 0.31982, 0.22208000000000003, 0.28665, 0.6632, 0.0153, 0.20868], \"symbol\": \"circle\", \"colorbar\": {\"title\": \"GDP per Capita\"}, \"line\": {\"color\": \"rgba(102, 102, 102)\", \"width\": 1}, \"cmax\": 1.69042, \"size\": [22.761000000000003, 22.683, 22.581, 22.566000000000003, 22.281000000000002, 22.218000000000004, 22.133999999999997, 22.092, 21.858, 21.852, 21.834, 21.678, 21.6, 21.561, 21.357, 20.948999999999998, 20.838, 20.82, 20.811, 20.703, 20.601000000000003, 20.558999999999997, 20.43, 20.394, 20.358, 20.25, 20.009999999999998, 19.833000000000002, 19.725, 19.722, 19.515, 19.455000000000002, 19.431, 19.365000000000002, 19.233, 18.987, 18.906000000000002, 18.894, 18.884999999999998, 18.807000000000002, 18.503999999999998, 18.39, 18.369, 18.008999999999997, 17.985, 17.961, 17.951999999999998, 17.924999999999997, 17.88, 17.843999999999998, 17.669999999999998, 17.666999999999998, 17.633999999999997, 17.565, 17.544, 17.499, 17.483999999999998, 17.472, 17.439, 17.373, 17.31, 17.276999999999997, 17.262, 17.148, 17.127, 17.085, 17.067, 16.815, 16.767, 16.644, 16.431, 16.422, 16.287, 16.197, 16.080000000000002, 15.996000000000002, 15.858, 15.803999999999998, 15.758999999999997, 15.636000000000003, 15.582, 15.576, 15.576, 15.419999999999998, 15.386999999999999, 15.372, 15.368999999999998, 15.306000000000001, 15.294, 15.219000000000001, 15.171, 15.039, 15.020999999999999, 14.913, 14.876999999999999, 14.847, 14.693999999999999, 14.655, 14.628, 14.622, 14.600999999999999, 14.571000000000002, 14.517, 14.399999999999999, 14.364, 14.358, 14.216999999999999, 14.145, 14.082, 14.058, 14.043, 14.030999999999999, 13.925999999999998, 13.899000000000001, 13.830000000000002, 13.713000000000001, 13.695, 13.649999999999999, 13.553999999999998, 13.550999999999998, 13.541999999999998, 13.535999999999998, 13.520999999999999, 13.308, 13.256999999999998, 13.107, 13.049999999999999, 12.995999999999999, 12.921, 12.890999999999998, 12.876, 12.812999999999999, 12.756, 12.654, 12.582, 12.231, 12.099, 11.985, 11.966999999999999, 11.868000000000002, 11.793, 11.712, 11.688, 11.535, 11.457, 11.343, 11.043, 11.033999999999999, 11.001, 10.968, 10.965, 10.761, 10.725000000000001, 10.395, 10.02, 9.018, 8.715, 8.517]}, \"locationmode\": \"country names\"}], {\"geo\": {\"showland\": true, \"landcolor\": \"rgb(250, 250, 250)\", \"subunitwidth\": 0.5, \"projection\": {\"type\": \"Mercator\"}, \"countrywidth\": 0.5}, \"title\": \"Happiness Scores by GDP\"}, {\"linkText\": \"Export to plot.ly\", \"showLink\": true})});</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['text']=df['Country'] + '<br>Happiness Score ' + (df['Happiness Score']).astype(str)\n",
    "scl = [ [0,\"rgb(5, 10, 172)\"],[0.35,\"rgb(40, 60, 190)\"],[0.5,\"rgb(70, 100, 245)\"],\\\n",
    "    [0.6,\"rgb(90, 120, 245)\"],[0.7,\"rgb(106, 137, 247)\"],[1,\"rgb(220, 220, 220)\"] ]\n",
    "\n",
    "data = [ dict(\n",
    "        type = 'scattergeo',\n",
    "        locationmode = 'country names',\n",
    "        lon = df['Longitude'],\n",
    "        lat = df['Latitude'],\n",
    "        text = df['text'],\n",
    "        mode = 'markers',\n",
    "        marker = dict(\n",
    "            size = df['Happiness Score']*3,\n",
    "            opacity = 0.8,\n",
    "            reversescale = True,\n",
    "            autocolorscale = False,\n",
    "            symbol = 'circle',\n",
    "            line = dict(\n",
    "                width=1,\n",
    "                color='rgba(102, 102, 102)'\n",
    "            ),\n",
    "            colorscale = scl,\n",
    "            cmin = 0,\n",
    "            color = df['Economy (GDP per Capita)'],\n",
    "            cmax = df['Economy (GDP per Capita)'].max(),\n",
    "            colorbar=dict(\n",
    "                title=\"GDP per Capita\"\n",
    "            )\n",
    "        ))]\n",
    "\n",
    "layout = dict(\n",
    "        title = 'Happiness Scores by GDP',\n",
    "        geo = dict(\n",
    "#             scope='usa',\n",
    "            projection=dict( type='Mercator' ),\n",
    "            showland = True,\n",
    "            landcolor = \"rgb(250, 250, 250)\",\n",
    "#             subunitcolor = \"rgb(217, 217, 217)\",\n",
    "#             countrycolor = \"rgb(217, 217, 217)\",\n",
    "            countrywidth = 0.5,\n",
    "            subunitwidth = 0.5\n",
    "        ),\n",
    "    )\n",
    "\n",
    "symbolmap = go.Figure(data = data, layout=layout)\n",
    "\n",
    "iplot(symbolmap)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Extra plot\n",
    "A cluster plot may also be used to see if the clustered regions coincide with any of the regions above. (This plot opens in a new window)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "Example of keyboard interaction\n",
    "\"\"\"\n",
    "\n",
    "class KMeansLayer(BaseLayer):\n",
    "\n",
    "    def __init__(self, data):\n",
    "        self.data = data\n",
    "        self.k = 2\n",
    "\n",
    "\n",
    "    def invalidate(self, proj):\n",
    "        self.painter = BatchPainter()\n",
    "        x, y = proj.lonlat_to_screen(self.data['Longitude'], self.data['Latitude'])\n",
    "\n",
    "        k_means = KMeans(n_clusters=self.k)\n",
    "        k_means.fit(np.vstack([x,y]).T)\n",
    "        labels = k_means.labels_\n",
    "\n",
    "        self.cmap = create_set_cmap(set(labels), 'hsv')\n",
    "        for l in set(labels):\n",
    "            self.painter.set_color(self.cmap[l])\n",
    "            self.painter.convexhull(x[labels == l], y[labels == l])\n",
    "            self.painter.points(x[labels == l], y[labels == l], 2)\n",
    "    \n",
    "            \n",
    "    def draw(self, proj, mouse_x, mouse_y, ui_manager):\n",
    "        ui_manager.info('Use left and right to increase/decrease the number of clusters. k = %d' % self.k)\n",
    "        self.painter.batch_draw()\n",
    "\n",
    "\n",
    "    def on_key_release(self, key, modifiers):\n",
    "        if key == pyglet.window.key.LEFT:\n",
    "            self.k = max(2,self.k - 1)\n",
    "            return True\n",
    "        elif key == pyglet.window.key.RIGHT:\n",
    "            self.k = self.k + 1\n",
    "            return True\n",
    "        return False\n",
    "  \n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = geoplotlib.utils.DataAccessObject(df)\n",
    "geoplotlib.add_layer(KMeansLayer(data))\n",
    "geoplotlib.set_smoothing(True)\n",
    "geoplotlib.set_bbox(geoplotlib.utils.BoundingBox.DK)\n",
    "geoplotlib.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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   "codemirror_mode": {
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