codePredictSimilarCountry.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "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>Density (people/km²)</th>\n",
       "      <th>Hospital Beds</th>\n",
       "      <th>Hospital Beds (Source Year)</th>\n",
       "      <th>Senior Population</th>\n",
       "      <th>Arrivals - Tourism</th>\n",
       "      <th>Population Below Poverty Line (%)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>59.6850</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>2005.0</td>\n",
       "      <td>2.584927</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>100.1043</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>13.744736</td>\n",
       "      <td>4643000.0</td>\n",
       "      <td>0.143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>18.4113</td>\n",
       "      <td>3.647400</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>6.362497</td>\n",
       "      <td>2451000.0</td>\n",
       "      <td>0.230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>American Samoa</td>\n",
       "      <td>277.3417</td>\n",
       "      <td>3.800000</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Andorra</td>\n",
       "      <td>165.0962</td>\n",
       "      <td>1.624524</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2831000.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>227</th>\n",
       "      <td>Wallis and Futuna</td>\n",
       "      <td>79.1479</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>228</th>\n",
       "      <td>Western Sahara</td>\n",
       "      <td>2.2456</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>229</th>\n",
       "      <td>Yemen</td>\n",
       "      <td>56.4920</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>366700.0</td>\n",
       "      <td>0.540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>230</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>24.4269</td>\n",
       "      <td>1.700000</td>\n",
       "      <td>2011.0</td>\n",
       "      <td>2.099678</td>\n",
       "      <td>1083000.0</td>\n",
       "      <td>0.544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>231</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>38.0362</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.939524</td>\n",
       "      <td>2423000.0</td>\n",
       "      <td>0.723</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>232 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               Country  Density (people/km²)  Hospital Beds  \\\n",
       "0          Afghanistan               59.6850       0.800000   \n",
       "1              Albania              100.1043       2.500000   \n",
       "2              Algeria               18.4113       3.647400   \n",
       "3       American Samoa              277.3417       3.800000   \n",
       "4              Andorra              165.0962       1.624524   \n",
       "..                 ...                   ...            ...   \n",
       "227  Wallis and Futuna               79.1479            NaN   \n",
       "228     Western Sahara                2.2456            NaN   \n",
       "229              Yemen               56.4920            NaN   \n",
       "230             Zambia               24.4269       1.700000   \n",
       "231           Zimbabwe               38.0362            NaN   \n",
       "\n",
       "     Hospital Beds (Source Year)  Senior Population  Arrivals - Tourism  \\\n",
       "0                         2005.0           2.584927                 NaN   \n",
       "1                         2009.0          13.744736           4643000.0   \n",
       "2                         2012.0           6.362497           2451000.0   \n",
       "3                         2014.0                NaN                 NaN   \n",
       "4                         2014.0                NaN           2831000.0   \n",
       "..                           ...                ...                 ...   \n",
       "227                          NaN                NaN                 NaN   \n",
       "228                          NaN                NaN                 NaN   \n",
       "229                          NaN                NaN            366700.0   \n",
       "230                       2011.0           2.099678           1083000.0   \n",
       "231                          NaN           2.939524           2423000.0   \n",
       "\n",
       "     Population Below Poverty Line (%)  \n",
       "0                                0.545  \n",
       "1                                0.143  \n",
       "2                                0.230  \n",
       "3                                  NaN  \n",
       "4                                  NaN  \n",
       "..                                 ...  \n",
       "227                                NaN  \n",
       "228                                NaN  \n",
       "229                              0.540  \n",
       "230                              0.544  \n",
       "231                              0.723  \n",
       "\n",
       "[232 rows x 7 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import math\n",
    "\n",
    "file_path = \"SDGData.xlsx\"\n",
    "df = pd.read_excel(file_path, \"Countries_Constant\", encoding='utf-16')\n",
    "df = df.dropna(how='all')\n",
    "countriesConstant = {} #{country:{density:100,Hospital Beds:100, Senior Population:.5,\tArrivals - Tourism:100,\tPopulation Below Poverty Line:50}}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Afghanistan': {'density': 59.685, 'beds': 0.8, 'seniors': 2.58492693988024, 'tourism': nan, 'poverty': 0.545}, 'Albania': {'density': 100.1043, 'beds': 2.5, 'seniors': 13.7447359109736, 'tourism': 4643000.0, 'poverty': 0.143}, 'Algeria': {'density': 18.4113, 'beds': 3.64740031, 'seniors': 6.36249654497986, 'tourism': 2451000.0, 'poverty': 0.23}, 'American Samoa': {'density': 277.3417, 'beds': 3.8, 'seniors': nan, 'tourism': nan, 'poverty': nan}, 'Andorra': {'density': 165.0962, 'beds': 1.624523582, 'seniors': nan, 'tourism': 2831000.0, 'poverty': nan}, 'Angola': {'density': 26.3626, 'beds': 2.9, 'seniors': 2.21637364776329, 'tourism': 261000.0, 'poverty': 0.366}, 'Anguilla': {'density': 164.8681, 'beds': nan, 'seniors': nan, 'tourism': nan, 'poverty': 0.23}, 'Antigua and Barbuda': {'density': 221.5588, 'beds': 3.8, 'seniors': 8.79982551980558, 'tourism': 247000.0, 'poverty': nan}, 'Argentina': {'density': 16.2551, 'beds': 4.2, 'seniors': 11.1177888760774, 'tourism': 6720000.0, 'poverty': 0.257}, 'Armenia': {'density': 99.6282, 'beds': nan, 'seniors': 11.2538176570131, 'tourism': 1495000.0, 'poverty': 0.32}, 'Aruba': {'density': 593.1444, 'beds': 0.5, 'seniors': 13.5509471396854, 'tourism': nan, 'poverty': nan}, 'Australia': {'density': 3.3151, 'beds': 7.6, 'seniors': 15.6564752275591, 'tourism': 8815000.0, 'poverty': nan}, 'Austria': {'density': 107.3839, 'beds': 4.7, 'seniors': 19.0015664595899, 'tourism': 29460000.0, 'poverty': 0.03}, 'Azerbaijan': {'density': 117.0806, 'beds': 0.8, 'seniors': 6.19518275071493, 'tourism': 2454000.0, 'poverty': 0.049}, 'Bahamas': {'density': 28.2037, 'beds': nan, 'seniors': nan, 'tourism': nan, 'poverty': nan}, 'Bahrain': {'density': 2224.281, 'beds': 2.9, 'seniors': 2.42633387832394, 'tourism': 11370000.0, 'poverty': nan}, 'Bangladesh': {'density': 1116.009, 'beds': 6.8, 'seniors': 5.15839063962068, 'tourism': 125000.0, 'poverty': 0.243}, 'Barbados': {'density': 668.314, 'beds': 2.7, 'seniors': 15.8026939621338, 'tourism': 664000.0, 'poverty': nan}, 'Belarus': {'density': 45.517, 'beds': 1.3, 'seniors': 14.8451481743098, 'tourism': 11060200.0, 'poverty': 0.057}, 'Belgium': {'density': 379.6391, 'beds': 0.5, 'seniors': 18.7887437383395, 'tourism': 8385000.0, 'poverty': 0.151}, 'Belize': {'density': 17.3138, 'beds': 6.300000191, 'seniors': 4.73645877657144, 'tourism': 427000.0, 'poverty': 0.41}, 'Benin': {'density': 107.645, 'beds': 0.4, 'seniors': 3.25360529746338, 'tourism': 281000.0, 'poverty': 0.362}, 'Bermuda': {'density': 1153.296, 'beds': 1.1, 'seniors': nan, 'tourism': nan, 'poverty': 0.11}, 'Bhutan': {'density': 20.0971, 'beds': 1.8, 'seniors': 6.00301171280561, 'tourism': 255000.0, 'poverty': 0.12}, 'Bolivia': {'density': 10.6255, 'beds': 2.2, 'seniors': 7.19194739218447, 'tourism': 1134000.0, 'poverty': 0.386}, 'Bosnia and Herzegovina': {'density': 64.0672, 'beds': 11.0, 'seniors': 16.4703174710621, 'tourism': 923000.0, 'poverty': 0.169}, 'Botswana': {'density': 4.0406, 'beds': 1.0, 'seniors': 4.22387434629301, 'tourism': 1574000.0, 'poverty': 0.193}, 'Brazil': {'density': 24.9607, 'beds': 5.8, 'seniors': 8.92283783244003, 'tourism': 6589000.0, 'poverty': 0.042}, 'British Virgin Islands': {'density': 200.2053, 'beds': 18.68000031, 'seniors': nan, 'tourism': nan, 'poverty': nan}, 'Brunei': {'density': 75.8853, 'beds': nan, 'seniors': nan, 'tourism': 259000.0, 'poverty': nan}, 'Bulgaria': {'density': 62.6669, 'beds': 2.0, 'seniors': 21.021914434268, 'tourism': 8883000.0, 'poverty': 0.234}, 'Burkina Faso': {'density': 76.578, 'beds': 0.8, 'seniors': 2.40698082866355, 'tourism': 143000.0, 'poverty': 0.401}, 'Burundi': {'density': 427.2036, 'beds': 6.2, 'seniors': 2.24694046033717, 'tourism': 299000.0, 'poverty': 0.646}, 'Cambodia': {'density': 92.3521, 'beds': 1.9, 'seniors': 4.56868001756576, 'tourism': 5602000.0, 'poverty': 0.165}, 'Cameroon': {'density': 55.8341, 'beds': 0.8, 'seniors': 2.72887735524057, 'tourism': 994000.0, 'poverty': 0.3}, 'Canada': {'density': 3.78, 'beds': 6.421446661, 'seniors': 17.2320066788651, 'tourism': 20798000.0, 'poverty': 0.094}, 'Cape Verde': {'density': 137.8594, 'beds': nan, 'seniors': nan, 'tourism': nan, 'poverty': 0.3}, 'Cayman Islands': {'density': 248.947, 'beds': 3.4, 'seniors': nan, 'tourism': 418000.0, 'poverty': nan}, 'Central African Republic': {'density': 7.7526, 'beds': 2.7, 'seniors': 2.82577370666008, 'tourism': 120500.0, 'poverty': nan}, 'Chad': {'density': 12.7927, 'beds': 3.51424905, 'seniors': 2.48051894434901, 'tourism': 87000.0, 'poverty': 0.467}, 'Chile': {'density': 25.2826, 'beds': 4.2, 'seniors': 11.5298016568816, 'tourism': 6450000.0, 'poverty': 0.144}, 'China': {'density': 148.2775, 'beds': 0.4, 'seniors': 10.9208835350654, 'tourism': 60740000.0, 'poverty': 0.033}, 'Colombia': {'density': 44.5658, 'beds': 2.2, 'seniors': 8.47804701986958, 'tourism': 4113000.0, 'poverty': 0.28}, 'Comoros': {'density': 467.0252, 'beds': 2.1, 'seniors': 3.00700930649436, 'tourism': 28000.0, 'poverty': 0.448}, 'Cook Islands': {'density': 74.4237, 'beds': nan, 'seniors': nan, 'tourism': nan, 'poverty': nan}, 'Costa Rica': {'density': 99.6892, 'beds': 2.267233925, 'seniors': 9.54984767296984, 'tourism': 2960000.0, 'poverty': 0.217}, 'Croatia': {'density': 72.5389, 'beds': 0.7, 'seniors': 20.445433012423, 'tourism': 15593000.0, 'poverty': 0.195}, 'Cuba': {'density': 103.0779, 'beds': nan, 'seniors': 15.1862114171521, 'tourism': 4594000.0, 'poverty': nan}, 'Curacao': {'density': 369.5788, 'beds': 3.0, 'seniors': 16.6848948092804, 'tourism': nan, 'poverty': nan}, 'Cyprus': {'density': 130.5112, 'beds': 6.5, 'seniors': 13.7190617734483, 'tourism': 3652000.0, 'poverty': nan}, 'Czech Republic': {'density': 135.7888, 'beds': 8.3, 'seniors': 19.4208768932343, 'tourism': 10160000.0, 'poverty': 0.097}, 'Denmark': {'density': 134.4085, 'beds': 1.6, 'seniors': 19.8129526369902, 'tourism': 11743000.0, 'poverty': 0.134}, 'Djibouti': {'density': 42.5862, 'beds': 3.8, 'seniors': 4.52757937811482, 'tourism': 63000.0, 'poverty': 0.23}, 'Dominica': {'density': 95.8535, 'beds': 2.5, 'seniors': nan, 'tourism': 72000.0, 'poverty': 0.29}, 'Dominican Republic': {'density': 222.8824, 'beds': 1.9, 'seniors': 7.08281747649721, 'tourism': 6188000.0, 'poverty': 0.305}, 'DR Congo': {'density': 38.1948, 'beds': nan, 'seniors': nan, 'tourism': nan, 'poverty': nan}, 'Ecuador': {'density': 63.7299, 'beds': 1.6, 'seniors': 7.15728972666108, 'tourism': 1608000.0, 'poverty': 0.215}, 'Egypt': {'density': 102.0843, 'beds': nan, 'seniors': nan, 'tourism': 8157000.0, 'poverty': 0.278}, 'El Salvador': {'density': 308.2651, 'beds': 3.8, 'seniors': 8.28709000480629, 'tourism': 1556000.0, 'poverty': 0.327}, 'Equatorial Guinea': {'density': 50.0155, 'beds': 4.3, 'seniors': 2.45787734677897, 'tourism': nan, 'poverty': 0.44}, 'Eritrea': {'density': 30.1566, 'beds': 3.0, 'seniors': nan, 'tourism': 142000.0, 'poverty': 0.5}, 'Estonia': {'density': 29.3306, 'beds': 0.3, 'seniors': 19.6263568469749, 'tourism': 3245000.0, 'poverty': 0.211}, 'Ethiopia': {'density': 104.1054, 'beds': 6.108282827, 'seniors': 3.50113299760986, 'tourism': 933000.0, 'poverty': 0.296}, 'Falkland Islands': {'density': 0.2859, 'beds': nan, 'seniors': nan, 'tourism': nan, 'poverty': nan}, 'Faroe Islands': {'density': 35.0775, 'beds': 3.2, 'seniors': nan, 'tourism': nan, 'poverty': 0.1}, 'Fiji': {'density': 49.0611, 'beds': 6.5, 'seniors': 5.44968041263952, 'tourism': 843000.0, 'poverty': 0.31}, 'Finland': {'density': 16.3721, 'beds': 2.3, 'seniors': 21.7207875455222, 'tourism': 3180000.0, 'poverty': nan}, 'France': {'density': 118.3145, 'beds': nan, 'seniors': 20.0346247469881, 'tourism': 86861000.0, 'poverty': 0.142}, 'French Guiana': {'density': 3.5756, 'beds': nan, 'seniors': nan, 'tourism': nan, 'poverty': nan}, 'French Polynesia': {'density': 67.4125, 'beds': 1.2, 'seniors': 8.29050810468202, 'tourism': nan, 'poverty': 0.197}, 'Gabon': {'density': 8.3153, 'beds': 2.8, 'seniors': 3.56390746835592, 'tourism': 269000.0, 'poverty': 0.343}, 'Gambia': {'density': 226.0893, 'beds': nan, 'seniors': nan, 'tourism': nan, 'poverty': nan}, 'Georgia': {'density': 57.2334, 'beds': 0.9, 'seniors': 14.8654914310525, 'tourism': 6483000.0, 'poverty': 0.092}, 'Germany': {'density': 234.614, 'beds': 1.4, 'seniors': 21.4619619953309, 'tourism': 37452000.0, 'poverty': 0.167}, 'Ghana': {'density': 130.2668, 'beds': nan, 'seniors': 3.06889800693396, 'tourism': 897000.0, 'poverty': 0.242}, 'Gibraltar': {'density': 5615.167, 'beds': 0.3, 'seniors': nan, 'tourism': nan, 'poverty': nan}, 'Greece': {'density': 78.9685, 'beds': 3.7, 'seniors': 21.6552720778435, 'tourism': 27194000.0, 'poverty': 0.36}, 'Greenland': {'density': 0.0262, 'beds': 0.6, 'seniors': nan, 'tourism': nan, 'poverty': 0.162}, 'Grenada': {'density': 327.1017, 'beds': 14.35340023, 'seniors': 9.62190679562869, 'tourism': 168000.0, 'poverty': 0.38}, 'Guadeloupe': {'density': 245.7764, 'beds': nan, 'seniors': nan, 'tourism': nan, 'poverty': nan}, 'Guam': {'density': 307.4226, 'beds': 1.6, 'seniors': 9.84991071859466, 'tourism': nan, 'poverty': 0.23}, 'Guatemala': {'density': 164.5306, 'beds': nan, 'seniors': 4.81207250828785, 'tourism': 2113000.0, 'poverty': 0.593}, 'Guinea': {'density': 53.4164, 'beds': 1.1, 'seniors': 2.92602244847935, 'tourism': 60000.0, 'poverty': 0.47}, 'Guinea-Bissau': {'density': 54.4775, 'beds': 2.1, 'seniors': 2.82371633615269, 'tourism': 43800.0, 'poverty': 0.67}, 'Guyana': {'density': 3.6589, 'beds': 4.110693963, 'seniors': 6.45027124309697, 'tourism': 247000.0, 'poverty': 0.35}, 'Haiti': {'density': 410.9019, 'beds': 7.0, 'seniors': 4.94940384843253, 'tourism': 467000.0, 'poverty': 0.585}, 'Honduras': {'density': 88.0472, 'beds': 0.743511694, 'seniors': 4.69061765907812, 'tourism': 851000.0, 'poverty': 0.296}, 'Hong Kong': {'density': 6790.744, 'beds': nan, 'seniors': nan, 'tourism': nan, 'poverty': 0.199}, 'Hungary': {'density': 103.8435, 'beds': 2.602965999, 'seniors': 19.1577253832321, 'tourism': 5650000.0, 'poverty': 0.149}, 'Iceland': {'density': 3.313, 'beds': 3.1, 'seniors': 14.7950925565689, 'tourism': 2225000.0, 'poverty': nan}, 'India': {'density': 419.7617, 'beds': nan, 'seniors': 6.17995564947149, 'tourism': 15543000.0, 'poverty': 0.219}, 'Indonesia': {'density': 143.6144, 'beds': 0.974968637, 'seniors': 5.85716561272863, 'tourism': 14040000.0, 'poverty': 0.109}, 'Iran': {'density': 50.9606, 'beds': nan, 'seniors': nan, 'tourism': 4867000.0, 'poverty': 0.187}, 'Iraq': {'density': 91.7658, 'beds': 3.2, 'seniors': 3.32359966279505, 'tourism': 892000.0, 'poverty': 0.23}, 'Ireland': {'density': 70.2658, 'beds': 1.5, 'seniors': 13.8658017012923, 'tourism': 10338000.0, 'poverty': 0.082}, 'Isle of Man': {'density': 148.6591, 'beds': 0.7, 'seniors': nan, 'tourism': nan, 'poverty': nan}, 'Israel': {'density': 416.7325, 'beds': 3.4, 'seniors': 11.9769860249625, 'tourism': 3613000.0, 'poverty': 0.22}, 'Italy': {'density': 200.6459, 'beds': 1.7, 'seniors': 22.7516796025077, 'tourism': 58253000.0, 'poverty': 0.299}, 'Ivory Coast': {'density': 81.8025, 'beds': nan, 'seniors': nan, 'tourism': nan, 'poverty': 0.463}, 'Jamaica': {'density': 269.4174, 'beds': 1.4, 'seniors': 8.79664255070196, 'tourism': 2353000.0, 'poverty': 0.171}, 'Japan': {'density': 334.6558, 'beds': 6.7, 'seniors': 27.576369910355, 'tourism': 28691000.0, 'poverty': 0.161}, 'Jordan': {'density': 114.2031, 'beds': 13.4, 'seniors': 3.84649039799834, 'tourism': 3843500.0, 'poverty': 0.142}, 'Kazakhstan': {'density': 6.8908, 'beds': 1.4, 'seniors': 7.39184627103032, 'tourism': 7701000.0, 'poverty': 0.026}, 'Kenya': {'density': 92.6505, 'beds': 4.5, 'seniors': 2.33918661191556, 'tourism': 1364000.0, 'poverty': 0.361}, 'Kiribati': {'density': 147.2861, 'beds': 2.3, 'seniors': 3.95262717204589, 'tourism': 5800.0, 'poverty': nan}, 'Kuwait': {'density': 239.6773, 'beds': 2.162905132, 'seniors': 2.55047238400198, 'tourism': 307000.0, 'poverty': nan}, 'Kyrgyzstan': {'density': 32.629, 'beds': nan, 'seniors': nan, 'tourism': nan, 'poverty': 0.321}, 'Laos': {'density': 30.7245, 'beds': nan, 'seniors': nan, 'tourism': nan, 'poverty': 0.22}, 'Latvia': {'density': 29.2167, 'beds': 5.295300007, 'seniors': 20.0436203206809, 'tourism': 1949000.0, 'poverty': 0.255}, 'Lebanon': {'density': 653.0277, 'beds': 0.8, 'seniors': 7.00236813439294, 'tourism': 1857000.0, 'poverty': 0.286}, 'Lesotho': {'density': 70.5732, 'beds': 4.218731436, 'seniors': 4.90108749682213, 'tourism': 1137000.0, 'poverty': 0.57}, 'Liberia': {'density': 45.4137, 'beds': 3.7, 'seniors': 3.25343179968014, 'tourism': nan, 'poverty': 0.541}, 'Libya': {'density': 3.9052, 'beds': 1.3, 'seniors': 4.39204025898401, 'tourism': 34000.0, 'poverty': nan}, 'Liechtenstein': {'density': 238.3, 'beds': 3.6, 'seniors': nan, 'tourism': 69000.0, 'poverty': nan}, 'Lithuania': {'density': 41.689, 'beds': 4.8, 'seniors': 19.7050331564608, 'tourism': 2523000.0, 'poverty': 0.222}, 'Luxembourg': {'density': 242.0642, 'beds': 5.8, 'seniors': 14.1831541841472, 'tourism': 1046000.0, 'poverty': nan}, 'Macau': {'density': 21644.5, 'beds': nan, 'seniors': nan, 'tourism': nan, 'poverty': nan}, 'Macedonia': {'density': 81.0242, 'beds': nan, 'seniors': nan, 'tourism': nan, 'poverty': nan}, 'Madagascar': {'density': 47.1705, 'beds': 4.3, 'seniors': 2.98671712579162, 'tourism': 255000.0, 'poverty': 0.707}, 'Malawi': {'density': 161.456, 'beds': 1.9, 'seniors': 2.64543493031032, 'tourism': 837000.0, 'poverty': 0.507}, 'Malaysia': {'density': 97.8407, 'beds': 2.9, 'seniors': 6.67175462547886, 'tourism': 25948000.0, 'poverty': 0.038}, 'Maldives': {'density': 1801.813, 'beds': 1.628485071, 'seniors': 3.70334460612454, 'tourism': nan, 'poverty': 0.15}, 'Mali': {'density': 16.3288, 'beds': 4.7, 'seniors': 2.50722975755683, 'tourism': 193300.0, 'poverty': 0.361}, 'Malta': {'density': 1397.288, 'beds': 0.9, 'seniors': 20.3493242997122, 'tourism': 2274000.0, 'poverty': 0.163}, 'Marshall Islands': {'density': 327.0166, 'beds': 2.400060569, 'seniors': nan, 'tourism': nan, 'poverty': nan}, 'Martinique': {'density': 332.6817, 'beds': nan, 'seniors': nan, 'tourism': nan, 'poverty': nan}, 'Mauritania': {'density': 4.5112, 'beds': 3.4, 'seniors': 3.14111215805009, 'tourism': 30000.0, 'poverty': 0.31}, 'Mauritius': {'density': 623.4157, 'beds': 1.3, 'seniors': 11.4741730686522, 'tourism': 1342000.0, 'poverty': 0.08}, 'Mayotte': {'density': 729.4519, 'beds': nan, 'seniors': nan, 'tourism': nan, 'poverty': nan}, 'Mexico': {'density': 65.6355, 'beds': 2.7, 'seniors': 7.22368498087198, 'tourism': 39291000.0, 'poverty': 0.462}, 'Micronesia': {'density': 163.8504, 'beds': nan, 'seniors': nan, 'tourism': nan, 'poverty': 0.267}, 'Moldova': {'density': 119.1858, 'beds': 0.2, 'seniors': 11.4695563413512, 'tourism': 145000.0, 'poverty': 0.096}, 'Monaco': {'density': 19426.73, 'beds': 5.8, 'seniors': nan, 'tourism': 355000.0, 'poverty': nan}, 'Mongolia': {'density': 2.0959, 'beds': nan, 'seniors': 4.08353878726245, 'tourism': 469000.0, 'poverty': 0.296}, 'Montenegro': {'density': 45.4725, 'beds': 7.0, 'seniors': 14.9749366447439, 'tourism': 1877000.0, 'poverty': 0.086}, 'Montserrat': {'density': 48.9412, 'beds': nan, 'seniors': nan, 'tourism': nan, 'poverty': nan}, 'Morocco': {'density': 82.6572, 'beds': 13.8, 'seniors': 7.0129048211122, 'tourism': 11349000.0, 'poverty': 0.15}, 'Mozambique': {'density': 38.9918, 'beds': 0.4, 'seniors': 2.8907644574499, 'tourism': 1447000.0, 'poverty': 0.461}, 'Myanmar': {'density': 80.4191, 'beds': 1.516696717, 'seniors': 5.78464193257209, 'tourism': 3443000.0, 'poverty': 0.256}, 'Namibia': {'density': 3.0776, 'beds': nan, 'seniors': 3.63603168074514, 'tourism': 1499000.0, 'poverty': 0.287}, 'Nauru': {'density': 515.4286, 'beds': 2.8, 'seniors': nan, 'tourism': nan, 'poverty': nan}, 'Nepal': {'density': 197.9658, 'beds': 5.0, 'seniors': 5.72767077569198, 'tourism': 940000.0, 'poverty': 0.252}, 'Netherlands': {'density': 409.4354, 'beds': 3.9, 'seniors': 19.1961926333387, 'tourism': 17924000.0, 'poverty': 0.088}, 'New Caledonia': {'density': 15.37, 'beds': 0.3, 'seniors': 9.17415792537671, 'tourism': 121000.0, 'poverty': 0.17}, 'New Zealand': {'density': 17.8293, 'beds': 3.814342645, 'seniors': 15.6524245271741, 'tourism': 3555000.0, 'poverty': nan}, 'Nicaragua': {'density': 50.8123, 'beds': 4.7, 'seniors': 5.24749744838026, 'tourism': 1787000.0, 'poverty': 0.296}, 'Niger': {'density': 19.1055, 'beds': 0.5, 'seniors': 2.59500788269853, 'tourism': 164000.0, 'poverty': 0.454}, 'Nigeria': {'density': 223.1508, 'beds': 0.9, 'seniors': 2.74737700532743, 'tourism': 1889000.0, 'poverty': 0.7}, 'Niue': {'density': 6.2538, 'beds': nan, 'seniors': nan, 'tourism': nan, 'poverty': nan}, 'North Korea': {'density': 213.8646, 'beds': nan, 'seniors': nan, 'tourism': nan, 'poverty': nan}, 'Northern Mariana Islands': {'density': 124.0496, 'beds': 0.7, 'seniors': nan, 'tourism': nan, 'poverty': nan}, 'Norway': {'density': 16.7425, 'beds': 0.3, 'seniors': 17.0492221563211, 'tourism': 6252000.0, 'poverty': nan}, 'Oman': {'density': 16.4996, 'beds': nan, 'seniors': 2.39278695625796, 'tourism': 2372000.0, 'poverty': nan}, 'Pakistan': {'density': 250.4698, 'beds': 2.3, 'seniors': 4.31277431133756, 'tourism': 966000.0, 'poverty': 0.295}, 'Palau': {'density': 39.4205, 'beds': 4.024199963, 'seniors': nan, 'tourism': 123000.0, 'poverty': nan}, 'Palestine': {'density': 820.163, 'beds': nan, 'seniors': nan, 'tourism': nan, 'poverty': nan}, 'Panama': {'density': 57.2121, 'beds': 1.6, 'seniors': 8.10473107966757, 'tourism': 1843000.0, 'poverty': 0.23}, 'Papua New Guinea': {'density': 19.3307, 'beds': 6.5, 'seniors': 3.44526925145617, 'tourism': 179000.0, 'poverty': 0.37}, 'Paraguay': {'density': 17.5353, 'beds': 1.200000048, 'seniors': 6.43021500946081, 'tourism': 1584000.0, 'poverty': 0.222}, 'Peru': {'density': 25.6547, 'beds': 1.0, 'seniors': 8.08839279183076, 'tourism': 4032000.0, 'poverty': 0.227}, 'Philippines': {'density': 320.0821, 'beds': 4.8, 'seniors': 5.12256876829946, 'tourism': 6621000.0, 'poverty': 0.216}, 'Poland': {'density': 121.0398, 'beds': 1.32934185, 'seniors': 17.5178167625452, 'tourism': 18258000.0, 'poverty': 0.176}, 'Portugal': {'density': 110.7255, 'beds': 1.3, 'seniors': 21.9538575375873, 'tourism': 15432000.0, 'poverty': 0.19}, 'Puerto Rico': {'density': 322.5313, 'beds': 13.2, 'seniors': 18.6748502103569, 'tourism': 3797000.0, 'poverty': nan}, 'Qatar': {'density': 248.6668, 'beds': 6.3, 'seniors': 1.3700703387375, 'tourism': 2256500.0, 'poverty': nan}, 'Republic of the Congo': {'density': 16.1348, 'beds': nan, 'seniors': nan, 'tourism': nan, 'poverty': nan}, 'Reunion': {'density': 356.556, 'beds': nan, 'seniors': nan, 'tourism': nan, 'poverty': nan}, 'Romania': {'density': 80.6981, 'beds': 8.2, 'seniors': 18.3387013938296, 'tourism': 10926000.0, 'poverty': 0.224}, 'Russia': {'density': 8.5351, 'beds': nan, 'seniors': nan, 'tourism': 24390000.0, 'poverty': 0.133}, 'Rwanda': {'density': 491.7692, 'beds': 0.674261835, 'seniors': 2.9381960775388, 'tourism': 932000.0, 'poverty': 0.391}, 'Saint Barthélemy': {'density': 470.3333, 'beds': nan, 'seniors': nan, 'tourism': nan, 'poverty': nan}, 'Saint Kitts and Nevis': {'density': 203.8276, 'beds': nan, 'seniors': nan, 'tourism': nan, 'poverty': nan}, 'Saint Lucia': {'density': 298.0958, 'beds': nan, 'seniors': nan, 'tourism': nan, 'poverty': nan}, 'Saint Martin': {'density': 729.5472, 'beds': nan, 'seniors': nan, 'tourism': nan, 'poverty': nan}, 'Saint Pierre and Miquelon': {'density': 23.9421, 'beds': nan, 'seniors': nan, 'tourism': nan, 'poverty': nan}, 'Saint Vincent and the Grenadines': {'density': 285.1928, 'beds': nan, 'seniors': nan, 'tourism': nan, 'poverty': nan}, 'Samoa': {'density': 69.8149, 'beds': nan, 'seniors': 4.80041197375197, 'tourism': 146000.0, 'poverty': nan}, 'San Marino': {'density': 556.2459, 'beds': 0.9, 'seniors': nan, 'tourism': 78000.0, 'poverty': nan}, 'Sao Tome and Principe': {'density': 227.3434, 'beds': 3.1, 'seniors': 2.9256781090661, 'tourism': nan, 'poverty': nan}, 'Saudi Arabia': {'density': 16.1948, 'beds': 0.8, 'seniors': 3.31408802294981, 'tourism': 16109000.0, 'poverty': nan}, 'Senegal': {'density': 85.1147, 'beds': 2.4, 'seniors': 3.08682370101833, 'tourism': 1365000.0, 'poverty': 0.467}, 'Serbia': {'density': 98.8827, 'beds': 1.212575422, 'seniors': 18.3457926916963, 'tourism': 1497000.0, 'poverty': 0.089}, 'Seychelles': {'density': 217.5819, 'beds': 1.5, 'seniors': 7.59454560435034, 'tourism': 350000.0, 'poverty': 0.393}, 'Sierra Leone': {'density': 111.193, 'beds': 1.3, 'seniors': 2.96655621131612, 'tourism': 55000.0, 'poverty': 0.702}, 'Singapore': {'density': 8239.918, 'beds': 1.4, 'seniors': 11.4633801933791, 'tourism': 13903000.0, 'poverty': nan}, 'Sint Maarten': {'density': 1261.059, 'beds': nan, 'seniors': nan, 'tourism': nan, 'poverty': nan}, 'Slovakia': {'density': 111.3372, 'beds': nan, 'seniors': nan, 'tourism': nan, 'poverty': 0.123}, 'Slovenia': {'density': 102.5471, 'beds': 2.6, 'seniors': 19.6068796541788, 'tourism': 3586000.0, 'poverty': 0.139}, 'Solomon Islands': {'density': 23.7709, 'beds': 0.4, 'seniors': 3.59895045928894, 'tourism': 25700.0, 'poverty': nan}, 'Somalia': {'density': 24.9244, 'beds': 5.7, 'seniors': 2.87331094294556, 'tourism': nan, 'poverty': nan}, 'South Africa': {'density': 48.5724, 'beds': 2.0, 'seniors': 5.31800500542302, 'tourism': 10285000.0, 'poverty': 0.166}, 'South Korea': {'density': 511.6175, 'beds': nan, 'seniors': nan, 'tourism': nan, 'poverty': nan}, 'South Sudan': {'density': 18.0618, 'beds': 1.212575422, 'seniors': 3.39848287985152, 'tourism': nan, 'poverty': 0.66}, 'Spain': {'density': 92.4022, 'beds': 5.0, 'seniors': 19.3785075224995, 'tourism': 81786000.0, 'poverty': 0.211}, 'Sri Lanka': {'density': 326.3717, 'beds': 0.984311608, 'seniors': 10.4732197537841, 'tourism': 2116400.0, 'poverty': 0.067}, 'Sudan': {'density': 23.249, 'beds': 0.3, 'seniors': 3.58116052825144, 'tourism': 813000.0, 'poverty': 0.465}, 'Suriname': {'density': 3.581, 'beds': 5.8, 'seniors': 6.90636990225525, 'tourism': 278000.0, 'poverty': 0.7}, 'Swaziland': {'density': 66.8143, 'beds': nan, 'seniors': nan, 'tourism': nan, 'poverty': nan}, 'Sweden': {'density': 22.4281, 'beds': 2.1, 'seniors': 20.0955249278002, 'tourism': 7054000.0, 'poverty': 0.15}, 'Switzerland': {'density': 209.6362, 'beds': 10.5951004, 'seniors': 18.6232165647717, 'tourism': 9889000.0, 'poverty': 0.066}, 'Syria': {'density': 94.5062, 'beds': nan, 'seniors': nan, 'tourism': nan, 'poverty': 0.825}, 'Taiwan': {'density': 658.0492, 'beds': nan, 'seniors': nan, 'tourism': nan, 'poverty': 0.015}, 'Tajikistan': {'density': 66.6502, 'beds': 7.4, 'seniors': 3.02188755207627, 'tourism': 431000.0, 'poverty': 0.315}, 'Tanzania': {'density': 63.205, 'beds': 0.5, 'seniors': 2.60129917835952, 'tourism': 1275000.0, 'poverty': 0.228}, 'Thailand': {'density': 136.0305, 'beds': 4.8, 'seniors': 11.9008931395893, 'tourism': 35592000.0, 'poverty': 0.072}, 'Timor-Leste': {'density': 88.6409, 'beds': 1.516696717, 'seniors': 4.31649229400603, 'tourism': 74000.0, 'poverty': nan}, 'Togo': {'density': 145.7907, 'beds': 2.1, 'seniors': 2.86946801108822, 'tourism': 496000.0, 'poverty': 0.551}, 'Tokelau': {'density': 113.0833, 'beds': nan, 'seniors': nan, 'tourism': nan, 'poverty': nan}, 'Tonga': {'density': 141.4926, 'beds': 0.674261835, 'seniors': 5.95366144364662, 'tourism': 62500.0, 'poverty': 0.225}, 'Trinidad and Tobago': {'density': 272.8047, 'beds': 2.3, 'seniors': 10.7349535163324, 'tourism': 395000.0, 'poverty': 0.2}, 'Tunisia': {'density': 72.2365, 'beds': 2.7, 'seniors': 8.31567907898877, 'tourism': 7052000.0, 'poverty': 0.155}, 'Turkey': {'density': 107.6355, 'beds': 5.6, 'seniors': 8.4832129399716, 'tourism': 37601000.0, 'poverty': 0.219}, 'Turkmenistan': {'density': 12.3565, 'beds': 2.089675477, 'seniors': 4.42663446194014, 'tourism': 8200.0, 'poverty': 0.002}, 'Turks and Caicos Islands': {'density': 40.8407, 'beds': 0.4, 'seniors': nan, 'tourism': nan, 'poverty': nan}, 'Tuvalu': {'density': 453.5385, 'beds': 0.7, 'seniors': nan, 'tourism': 2500.0, 'poverty': 0.263}, 'Uganda': {'density': 189.3645, 'beds': 8.8, 'seniors': 1.94098692046317, 'tourism': 1402000.0, 'poverty': 0.214}, 'Ukraine': {'density': 72.4669, 'beds': 3.849647335, 'seniors': 16.4346864392016, 'tourism': 14230000.0, 'poverty': 0.038}, 'United Arab Emirates': {'density': 118.3062, 'beds': 5.0, 'seniors': 1.08500098484481, 'tourism': 7126000.0, 'poverty': 0.195}, 'United Kingdom': {'density': 279.4813, 'beds': 2.6, 'seniors': 18.3958656741466, 'tourism': 37651000.0, 'poverty': 0.15}, 'United States': {'density': 35.316, 'beds': 4.0, 'seniors': 15.8076540556035, 'tourism': 76941000.0, 'poverty': 0.118}, 'United States Virgin Islands': {'density': 300.9366, 'beds': nan, 'seniors': nan, 'tourism': nan, 'poverty': nan}, 'Uruguay': {'density': 19.1883, 'beds': 2.9, 'seniors': 14.8145195308593, 'tourism': 3674000.0, 'poverty': 0.097}, 'Uzbekistan': {'density': 74.8082, 'beds': 2.6, 'seniors': 4.4191378781364, 'tourism': 2690000.0, 'poverty': 0.14}, 'Vanuatu': {'density': 25.1985, 'beds': 2.704325206, 'seniors': 3.64015306819735, 'tourism': 109000.0, 'poverty': nan}, 'Vatican City': {'density': 1820.455, 'beds': nan, 'seniors': nan, 'tourism': nan, 'poverty': nan}, 'Venezuela': {'density': 31.0285, 'beds': nan, 'seniors': nan, 'tourism': 427000.0, 'poverty': 0.197}, 'Vietnam': {'density': 293.886, 'beds': 1.7, 'seniors': 7.2749782978793, 'tourism': 12922000.0, 'poverty': 0.08}, 'Wallis and Futuna': {'density': 79.1479, 'beds': nan, 'seniors': nan, 'tourism': nan, 'poverty': nan}, 'Western Sahara': {'density': 2.2456, 'beds': nan, 'seniors': nan, 'tourism': nan, 'poverty': nan}, 'Yemen': {'density': 56.492, 'beds': nan, 'seniors': nan, 'tourism': 366700.0, 'poverty': 0.54}, 'Zambia': {'density': 24.4269, 'beds': 1.7, 'seniors': 2.09967802593266, 'tourism': 1083000.0, 'poverty': 0.544}, 'Zimbabwe': {'density': 38.0362, 'beds': nan, 'seniors': 2.93952365300113, 'tourism': 2423000.0, 'poverty': 0.723}}\n"
     ]
    }
   ],
   "source": [
    "for _,data in df.iterrows():\n",
    "    countryData = {}\n",
    "    countryData[\"density\"] = data[1]\n",
    "    countryData[\"beds\"] = data[2]\n",
    "    countryData[\"seniors\"] = data[4]\n",
    "    countryData[\"tourism\"] = data[5]\n",
    "    countryData[\"poverty\"] = data[6]\n",
    "    countriesConstant[data[0]] = countryData\n",
    "# print(countriesConstant)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['25\\n', '525\\n', '235\\n', '253\\n', '235']\n"
     ]
    }
   ],
   "source": [
    "inputFile = open('input.txt','r')    \n",
    "inputs = inputFile.readlines()\n",
    "print(inputs)\n",
    "inputL = []\n",
    "for line in inputs: \n",
    "    inputL.append(float(line.strip()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Input your population density 35.316\n",
      "Input number of hospital beds per 1000 people 4\n",
      "Input percentage of population above 65 years old 15.8\n",
      "Input number of airport arrivals 76941000\n",
      "Input fraction of people in poverty .118\n"
     ]
    }
   ],
   "source": [
    "#inputed values\n",
    "inDensity = float(input(\"Input your population density \"))\n",
    "inBeds = float(input(\"Input number of hospital beds per 1000 people \"))\n",
    "inSeniors = float(input(\"Input percentage of population above 65 years old \"))\n",
    "inTourism = float(input(\"Input number of airport arrivals \"))\n",
    "inPoverty = float(input(\"Input fraction of people in poverty \"))\n",
    "inputData = {\"density\":inDensity,\"beds\":inBeds,\"seniors\":inSeniors,\"tourism\":inTourism,\"poverty\":inPoverty}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "def euclidean_distance(inputData, countryData): #[Density,Beds,Seniors,Tourism,Poverty]\n",
    "    sqDiffSum=0\n",
    "    multipliers = {\"density\":2, \"beds\":.5, \"seniors\":1.5,\"tourism\":1.5,\"poverty\":.75}\n",
    "    for k in inputData.keys():            \n",
    "        if(math.isnan(inputData[k]) or math.isnan(countryData[k])):\n",
    "            sqDiffSum +=0\n",
    "        else:\n",
    "            sqDiffSum += (multipliers[k]*(inputData[k] - countryData[k]))**2\n",
    "    return sqDiffSum**(1/2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "United States\n"
     ]
    }
   ],
   "source": [
    "nearScores = {}\n",
    "for country in countriesConstant.keys():\n",
    "    nearness = euclidean_distance(inputData, countriesConstant[country])\n",
    "    nearScores[country] = nearness\n",
    "# print(nearScores)\n",
    "closest = min(nearScores.keys(), key=(lambda k: nearScores[k]))\n",
    "file = open(\"request.txt\",\"w\");\n",
    "file.write(closest)\n",
    "print(closest)\n",
    "file.close()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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