SQL-Python connectivity.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "d119c3b3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: mysql-connector-python in /Applications/anaconda3/lib/python3.9/site-packages (8.0.31)\n",
      "Requirement already satisfied: protobuf<=3.20.1,>=3.11.0 in /Applications/anaconda3/lib/python3.9/site-packages (from mysql-connector-python) (3.19.1)\n"
     ]
    }
   ],
   "source": [
    "# installingmysql connector\n",
    "!pip install mysql-connector-python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "bb805fa7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing the required libraries\n",
    "import mysql.connector as c\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt \n",
    "import seaborn as sns\n",
    "import plotly.express as px\n",
    "import plotly.graph_objects as go\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "89deb2a1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Successfully Connected\n"
     ]
    }
   ],
   "source": [
    "con = c.connect(host = \"localhost\",\n",
    "               user = \"root\",\n",
    "               passwd = \"gmail@8821\",\n",
    "               database = \"A&S Consulting Group Ltd\")\n",
    "if con.is_connected():\n",
    "    print(\"Successfully Connected\")\n",
    "else:\n",
    "    print(\"Connectivity Issue\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "1d0e0eb0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Afghanistan\n",
      "Netherlands\n",
      "Albania\n",
      "Algeria\n",
      "American Samoa\n",
      "And or ra\n",
      "Angola\n",
      "Antigua and Barbuda\n",
      "United Arab Emirates\n",
      "Argentina\n",
      "Armenia\n",
      "Aruba\n",
      "Australia\n",
      "Azerbaijan\n",
      "Bahamas\n",
      "Bahrain\n",
      "Bangladesh\n",
      "Barbados\n",
      "Belgium\n",
      "Belize\n",
      "Benin\n",
      "Bermuda\n",
      "Bhutan\n",
      "Bolivia\n",
      "Bosnia and Herzegovina\n",
      "Botswana\n",
      "Brazil\n",
      "United Kingdom\n",
      "Virgin Islands British\n",
      "Brunei\n",
      "Bulgaria\n",
      "Burkina Faso\n",
      "Burundi\n",
      "Cayman Islands\n",
      "Chile\n",
      "Costa Rica\n",
      "Djibouti\n",
      "Dominica\n",
      "Dominican Republic\n",
      "Ecuad or \n",
      "Egypt\n",
      "El Salvad or \n",
      "Eritrea\n",
      "Spain\n",
      "South Africa\n",
      "Ethiopia\n",
      "Fiji Islands\n",
      "Philippines\n",
      "Faroe Islands\n",
      "Gabon\n",
      "Gambia\n",
      "Ge or gia\n",
      "Ghana\n",
      "Gibraltar\n",
      "Grenada\n",
      "Greenland\n",
      "Guam\n",
      "Guatemala\n",
      "Guinea\n",
      "Guinea Bissau\n",
      "Guyana\n",
      "Haiti\n",
      "Honduras\n",
      "Indonesia\n",
      "India\n",
      "Iraq\n",
      "Iran\n",
      "Ireland\n",
      "Iceland\n",
      "Israel\n",
      "Italy\n",
      "Austria\n",
      "Jamaica\n",
      "Japan\n",
      "Yemen\n",
      "J or dan\n",
      "Cambodia\n",
      "Cameroon\n",
      "Canada\n",
      "Cape Verde\n",
      "Kazakhstan\n",
      "Kenya\n",
      "Central African Republic\n",
      "China\n",
      "Kyrgyzstan\n",
      "Kiribati\n",
      "Colombia\n",
      "Com or os\n",
      "Congo\n",
      "Congo The Democratic Republic of the\n",
      "N or th K or ea\n",
      "South K or ea\n",
      "Greece\n",
      "Croatia\n",
      "Cuba\n",
      "Kuwait\n",
      "Cyprus\n",
      "Laos\n",
      "Latvia\n",
      "Lesotho\n",
      "Lebanon\n",
      "Liberia\n",
      "Libya\n",
      "Liechtenstein\n",
      "Lithuania\n",
      "Luxembourg\n",
      "Macao\n",
      "Madagascar\n",
      "Macedonia\n",
      "Malawi\n",
      "Maldives\n",
      "Malaysia\n",
      "Mali\n",
      "Malta\n",
      "M or occo\n",
      "Marshall Islands\n",
      "Mauritania\n",
      "Mauritius\n",
      "Mexico\n",
      "Micronesia  Federated States of\n",
      "Moldova\n",
      "Monaco\n",
      "Mongolia\n",
      "Mozambique\n",
      "Myanmar\n",
      "Namibia\n",
      "Nauru\n",
      "Nepal\n",
      "Nicaragua\n",
      "Niger\n",
      "Nigeria\n",
      "N or way\n",
      "Cote d'Ivoire\n",
      "Oman\n",
      "Pakistan\n",
      "Palau\n",
      "Panama\n",
      "Papua New Guinea\n",
      "Paraguay\n",
      "Peru\n",
      "N or thern Mariana Islands\n",
      "P or tugal\n",
      "Puerto Rico\n",
      "Poland\n",
      "Equat or ial Guinea\n",
      "Qatar\n",
      "France\n",
      "French Polynesia\n",
      "Rwanda\n",
      "Sweden\n",
      "Saint Kitts and Nevis\n",
      "Saint Lucia\n",
      "Saint Vincent and the Grenadines\n",
      "Germany\n",
      "Solomon Islands\n",
      "Zambia\n",
      "Samoa\n",
      "San Marino\n",
      "Sao Tome and Principe\n",
      "Saudi Arabia\n",
      "Senegal\n",
      "Seychelles\n",
      "Sierra Leone\n",
      "Singap or e\n",
      "Slovakia\n",
      "Slovenia\n",
      "Somalia\n",
      "Sri Lanka\n",
      "Sudan\n",
      "Finland\n",
      "Suriname\n",
      "Swaziland\n",
      "Switzerland\n",
      "Syria\n",
      "Tajikistan\n",
      "Tanzania\n",
      "Denmark\n",
      "Thailand\n",
      "Togo\n",
      "Tonga\n",
      "Trinidad and Tobago\n",
      "Chad\n",
      "Czech Republic\n",
      "Tunisia\n",
      "Turkey\n",
      "Turkmenistan\n",
      "Turks and Caicos Islands\n",
      "Tuvalu\n",
      "Uganda\n",
      "Ukraine\n",
      "Hungary\n",
      "Uruguay\n",
      "New Caledonia\n",
      "New Zealand\n",
      "Uzbekistan\n",
      "Belarus\n",
      "Vanuatu\n",
      "Venezuela\n",
      "Russian Federation\n",
      "Vietnam\n",
      "Estonia\n",
      "United States\n",
      "Virgin Islands U.S.\n",
      "Zimbabwe\n",
      "Palestine\n",
      "The list of countries have been printed\n"
     ]
    }
   ],
   "source": [
    "# getting the list of all the countries from the 'nation' table\n",
    "cur = con.cursor()\n",
    "nation = \"SELECT * From nation\"\n",
    "cur.execute(nation)\n",
    "result = cur.fetchall()\n",
    "for rec in result:\n",
    "    print(rec[1])\n",
    "print(\"The list of countries have been printed\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "46e85a09",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('AFG', 'Afghanistan', 'Asia', 'Southern and Central Asia', 652090, 1919, 'Afganistan and Afqanestan', 'Islamic Emirate', 'Kabul', 69.1761, 34.5228)\n",
      "('NLD', 'Netherlands', 'Europe', 'Western Europe', 41526, 1581, 'Nederland', 'Constitutional Monarchy', 'Amsterdam', 4.89095, 52.3738)\n",
      "('ALB', 'Albania', 'Europe', 'Southern Europe', 28748, 1912, 'Shqiperia', 'Republic', 'Tirane', 19.8172, 41.3317)\n",
      "('DZA', 'Algeria', 'Africa', 'N or thern Africa', 2381740, 1962, 'Al Jaza or   or  Algerie', 'Republic', 'Algiers', 3.05097, 36.7397)\n",
      "('ASM', 'American Samoa', 'Oceania', 'Polynesia', 199, 0, 'Amerika Samoa', 'US Territ or y', 'Pago Pago', 170.691, 14.2846)\n",
      "('AND', 'And or ra', 'Europe', 'Southern Europe', 468, 1278, 'And or ra', 'Parliamentary Coprincipality', 'And or ra la Vella', 1.5218, 42.5075)\n",
      "('AGO', 'Angola', 'Africa', 'Central Africa', 1246700, 1975, 'Angola', 'Republic', 'Luanda', 13.242, 8.81155)\n",
      "('ATG', 'Antigua and Barbuda', 'N or th America', 'Caribbean', 442, 1981, 'Antigua and Barbuda', 'Constitutional Monarchy', \"Saint John's\", 61.8456, 17.1175)\n",
      "('ARE', 'United Arab Emirates', 'Asia', 'Middle East', 83600, 1971, 'Al Imarat al  or  Arabiya al Muttahida', 'Emirate Federation', 'Abu Dhabi', 54.3705, 24.4764)\n",
      "('ARG', 'Argentina', 'South America', 'South America', 2780400, 1816, 'Argentina', 'Federal Republic', 'Buenos Aires', 58.4173, 34.6118)\n",
      "('ARM', 'Armenia', 'Asia', 'Middle East', 29800, 1991, 'Hajastan', 'Republic', 'Yerevan', 44.509, 40.1596)\n",
      "('ABW', 'Aruba', 'N or th America', 'Caribbean', 193, 0, 'Aruba', 'Nonmetropolitan Territ or y of The Netherlands', ' or anjestad', 70.0167, 12.5167)\n",
      "('AUS', 'Australia', 'Oceania', 'Australia and New Zealand', 7741220, 1901, 'Australia', 'Constitutional Monarchy Federation', 'Canberra', 149.129, 35.282)\n",
      "('AZE', 'Azerbaijan', 'Asia', 'Middle East', 86600, 1991, 'Azarbaycan', 'Federal Republic', 'Baku', 49.8932, 40.3834)\n",
      "('BHS', 'Bahamas', 'N or th America', 'Caribbean', 13878, 1973, 'The Bahamas', 'Constitutional Monarchy', 'Nassau', 77.339, 25.0661)\n",
      "('BHR', 'Bahrain', 'Asia', 'Middle East', 694, 1971, 'Al Bahrayn', 'Monarchy (Emirate)', 'Manama', 50.5354, 26.1921)\n",
      "('BGD', 'Bangladesh', 'Asia', 'Southern and Central Asia', 143998, 1971, 'Bangladesh', 'Republic', 'Dhaka', 90.4113, 23.7055)\n",
      "('BRB', 'Barbados', 'N or th America', 'Caribbean', 430, 1966, 'Barbados', 'Constitutional Monarchy', 'Bridgetown', 59.6105, 13.0935)\n",
      "('BEL', 'Belgium', 'Europe', 'Western Europe', 30518, 1830, 'Belgie Belgique', 'Constitutional Monarchy Federation', 'Brussels', 4.36761, 50.8371)\n",
      "('BLZ', 'Belize', 'N or th America', 'Central America', 22696, 1981, 'Belize', 'Constitutional Monarchy', 'Belmopan', 88.7713, 17.2534)\n",
      "('BEN', 'Benin', 'Africa', 'Western Africa', 112622, 1960, 'Benin', 'Republic', 'P or toNovo', 2.6323, 6.4779)\n",
      "('BMU', 'Bermuda', 'N or th America', 'N or th America', 53, 0, 'Bermuda', 'Dependent Territ or y of the UK', 'Hamilton', 64.706, 32.3293)\n",
      "('BTN', 'Bhutan', 'Asia', 'Southern and Central Asia', 47000, 1910, 'DrukYul', 'Monarchy', 'Thimphu', 89.6177, 27.5768)\n",
      "('BOL', 'Bolivia', 'South America', 'South America', 1098580, 1825, 'Bolivia', 'Republic', 'La Paz', 66.1936, 13.9908)\n",
      "('BIH', 'Bosnia and Herzegovina', 'Europe', 'Southern Europe', 51197, 1992, 'Bosna i Hercegovina', 'Federal Republic', 'Sarajevo', 18.4214, 43.8607)\n",
      "('BWA', 'Botswana', 'Africa', 'Southern Africa', 581730, 1966, 'Botswana', 'Republic', 'Gab or one', 25.9201, 24.6544)\n",
      "('BRA', 'Brazil', 'South America', 'South America', 8547400, 1822, 'Brasil', 'Federal Republic', 'Brasilia', 47.9292, 15.7801)\n",
      "('GBR', 'United Kingdom', 'Europe', 'British Islands', 242900, 1066, 'United Kingdom', 'Constitutional Monarchy', 'London', 0.126236, 51.5002)\n",
      "('VGB', 'Virgin Islands British', 'N or th America', 'Caribbean', 151, 0, 'British Virgin Islands', 'Dependent Territ or y of the UK', 'Road Town', 64.623056, 18.431389)\n",
      "('BRN', 'Brunei', 'Asia', 'Southeast Asia', 5765, 1984, 'Brunei Darussalam', 'Monarchy Sultanate', 'Bandar Seri Begawan', 114.946, 4.94199)\n",
      "('BGR', 'Bulgaria', 'Europe', 'Eastern Europe', 110994, 1908, 'Balgarija', 'Republic', 'Sofia', 23.3238, 42.7105)\n",
      "('BFA', 'Burkina Faso', 'Africa', 'Western Africa', 274000, 1960, 'Burkina Faso', 'Republic', 'Ouagadougou', 1.53395, 12.3605)\n",
      "('BDI', 'Burundi', 'Africa', 'Eastern Africa', 27834, 1962, 'Burundi or Uburundi', 'Republic', 'Bujumbura', 29.3639, 3.3784)\n",
      "('CYM', 'Cayman Islands', 'N or th America', 'Caribbean', 264, 0, 'Cayman Islands', 'Dependent Territ or y of the UK', 'Ge or ge Town', 81.3857, 19.3022)\n",
      "('CHL', 'Chile', 'South America', 'South America', 756626, 1810, 'Chile', 'Republic', 'Santiago', 70.6475, 33.475)\n",
      "('CRI', 'Costa Rica', 'N or th America', 'Central America', 51100, 1821, 'Costa Rica', 'Republic', 'San Jose', 84.0089, 9.63701)\n",
      "('DJI', 'Djibouti', 'Africa', 'Eastern Africa', 23200, 1977, 'Djibouti or Jibuti', 'Republic', 'Djibouti', 43.1425, 11.5806)\n",
      "('DMA', 'Dominica', 'N or th America', 'Caribbean', 751, 1978, 'Dominica', 'Republic', 'Roseau', 61.39, 15.2976)\n",
      "('DOM', 'Dominican Republic', 'N or th America', 'Caribbean', 48511, 1844, 'Republica Dominicana', 'Republic', 'Santo Domingo', 69.8908, 18.479)\n",
      "('ECU', 'Ecuad or ', 'South America', 'South America', 283561, 1822, 'Ecuad or ', 'Republic', 'Quito', 78.5243, 0.229498)\n",
      "('EGY', 'Egypt', 'Africa', 'N or thern Africa', 1001450, 1922, 'Misr', 'Republic', 'Cairo', 31.2461, 30.0982)\n",
      "('SLV', 'El Salvad or ', 'N or th America', 'Central America', 21041, 1841, 'El Salvad or ', 'Republic', 'San Salvad or ', 89.2073, 13.7034)\n",
      "('ERI', 'Eritrea', 'Africa', 'Eastern Africa', 117600, 1993, 'Ertra', 'Republic', 'Asmara', 38.9183, 15.3315)\n",
      "('ESP', 'Spain', 'Europe', 'Southern Europe', 505992, 1492, 'Espana', 'Constitutional Monarchy', 'Madrid', 3.70327, 40.4167)\n",
      "('ZAF', 'South Africa', 'Africa', 'Southern Africa', 1221040, 1910, 'South Africa', 'Republic', 'Pret or ia', 28.1871, 25.746)\n",
      "('ETH', 'Ethiopia', 'Africa', 'Eastern Africa', 1104300, 1000, 'YeItyop yiya', 'Republic', 'Addis Ababa', 38.7468, 9.02274)\n",
      "('FJI', 'Fiji Islands', 'Oceania', 'Melanesia', 18274, 1970, 'Fiji Islands', 'Republic', 'Suva', 178.399, 18.1149)\n",
      "('PHL', 'Philippines', 'Asia', 'Southeast Asia', 300000, 1946, 'Pilipinas', 'Republic', 'Manila', 121.035, 14.5515)\n",
      "('FRO', 'Faroe Islands', 'Europe', 'N or dic Countries', 1399, 0, 'F or oyar', 'Part of Denmark', 'T or shavn', 6.91181, 61.8926)\n",
      "('GAB', 'Gabon', 'Africa', 'Central Africa', 267668, 1960, 'Le Gabon', 'Republic', 'Libreville', 9.45162, 0.38832)\n",
      "('GMB', 'Gambia', 'Africa', 'Western Africa', 11295, 1965, 'The Gambia', 'Republic', 'Banjul', 16.5885, 13.4495)\n",
      "('GEO', 'Ge or gia', 'Asia', 'Middle East', 69700, 1991, 'Sakartvelo', 'Republic', 'Tbilisi', 44.793, 41.71)\n",
      "('GHA', 'Ghana', 'Africa', 'Western Africa', 238533, 1957, 'Ghana', 'Republic', 'Accra', 0.20795, 5.57045)\n",
      "('GIB', 'Gibraltar', 'Europe', 'Southern Europe', 6, 0, 'Gibraltar', 'Dependent Territ or y of the UK', 'NA', 36.1408, 5.3536)\n",
      "('GRD', 'Grenada', 'N or th America', 'Caribbean', 344, 1974, 'Grenada', 'Constitutional Monarchy', \"Saint Ge or ge's\", 61.7449, 12.0653)\n",
      "('GRL', 'Greenland', 'N or th America', 'N or th America', 2166090, 0, 'Kalaallit Nunaat or Gronland', 'Part of Denmark', 'Nuuk', 51.7214, 64.1836)\n",
      "('GUM', 'Guam', 'Oceania', 'Micronesia', 549, 0, 'Guam', 'US Territ or y', 'Agana', 144.794, 13.4443)\n",
      "('GTM', 'Guatemala', 'N or th America', 'Central America', 108889, 1821, 'Guatemala', 'Republic', 'Guatemala City', 90.5328, 14.6248)\n",
      "('GIN', 'Guinea', 'Africa', 'Western Africa', 245857, 1958, 'Guinee', 'Republic', 'Conakry', 13.7, 9.51667)\n",
      "('GNB', 'Guinea Bissau', 'Africa', 'Western Africa', 36125, 1974, 'GuineBissau', 'Republic', 'Bissau', 15.1804, 11.8037)\n",
      "('GUY', 'Guyana', 'South America', 'South America', 214969, 1966, 'Guyana', 'Republic', 'Ge or getown', 58.1548, 6.80461)\n",
      "('HTI', 'Haiti', 'N or th America', 'Caribbean', 27750, 1804, 'Haiti or Dayti', 'Republic', 'P or tauPrince', 72.3288, 18.5392)\n",
      "('HND', 'Honduras', 'N or th America', 'Central America', 112088, 1838, 'Honduras', 'Republic', 'Tegucigalpa', 87.4667, 15.1333)\n",
      "('IDN', 'Indonesia', 'Asia', 'Southeast Asia', 1904570, 1945, 'Indonesia', 'Republic', 'Jakarta', 106.83, 6.19752)\n",
      "('IND', 'India', 'Asia', 'Southern and Central Asia', 3287260, 1947, 'Bharat or India', 'Federal Republic', 'New Delhi', 77.225, 28.6353)\n",
      "('IRQ', 'Iraq', 'Asia', 'Middle East', 438317, 1932, 'Al yIraq', 'Republic', 'Baghdad', 44.394, 33.3302)\n",
      "('IRN', 'Iran', 'Asia', 'Southern and Central Asia', 1648200, 1906, 'Iran', 'Islamic Republic', 'Tehran', 51.4447, 35.6878)\n",
      "('IRL', 'Ireland', 'Europe', 'British Islands', 70273, 1921, 'Ireland or Eire', 'Republic', 'Dublin', 6.26749, 53.3441)\n",
      "('ISL', 'Iceland', 'Europe', 'N or dic Countries', 103000, 1944, 'Island', 'Republic', 'Reykjavik', 21.8952, 64.1353)\n",
      "('ISR', 'Israel', 'Asia', 'Middle East', 21056, 1948, 'Yisraiel or Israil', 'Republic', 'NA', 35.2035, 31.7717)\n",
      "('ITA', 'Italy', 'Europe', 'Southern Europe', 301316, 1861, 'Italia', 'Republic', 'Rome', 12.4823, 41.8955)\n",
      "('AUT', 'Austria', 'Europe', 'Western Europe', 83859, 1918, 'Osterreich', 'Federal Republic', 'Vienna', 16.3798, 48.2201)\n",
      "('JAM', 'Jamaica', 'N or th America', 'Caribbean', 10990, 1962, 'Jamaica', 'Constitutional Monarchy', 'Kingston', 76.792, 17.9927)\n",
      "('JPN', 'Japan', 'Asia', 'Eastern Asia', 377829, 660, 'Nihon or Nippon', 'Constitutional Monarchy', 'Tokyo', 139.77, 35.67)\n",
      "('YEM', 'Yemen', 'Asia', 'Middle East', 527968, 1918, 'AlYaman', 'Republic', \"Sana'a\", 44.2075, 15.352)\n",
      "('J or ', 'J or dan', 'Asia', 'Middle East', 88946, 1946, 'AlUrdunn', 'Constitutional Monarchy', 'Amman', 35.9263, 31.9497)\n",
      "('KHM', 'Cambodia', 'Asia', 'Southeast Asia', 181035, 1953, 'Kampuchea', 'Constitutional Monarchy', 'Phnom Penh', 104.874, 11.5556)\n",
      "('CMR', 'Cameroon', 'Africa', 'Central Africa', 475442, 1960, 'Cameroun or Cameroon', 'Republic', 'Yaounde', 11.5174, 3.8721)\n",
      "('CAN', 'Canada', 'N or th America', 'N or th America', 9970610, 1867, 'Canada', 'Constitutional Monarchy Federation', 'Ottawa', 75.6919, 45.4215)\n",
      "('CPV', 'Cape Verde', 'Africa', 'Western Africa', 4033, 1975, 'Cabo Verde', 'Republic', 'Praia', 23.5087, 14.9218)\n",
      "('KAZ', 'Kazakhstan', 'Asia', 'Southern and Central Asia', 2724900, 1991, 'Qazaqstan', 'Republic', 'Astana', 71.4382, 51.1879)\n",
      "('KEN', 'Kenya', 'Africa', 'Eastern Africa', 580367, 1963, 'Kenya', 'Republic', 'Nairobi', 36.8126, 1.27975)\n",
      "('CAF', 'Central African Republic', 'Africa', 'Central Africa', 622984, 1960, 'Centrafrique or BeAfrika', 'Republic', 'Bangui', 21.6407, 5.63056)\n",
      "('CHN', 'China', 'Asia', 'Eastern Asia', 9572900, 1523, 'Zhongquo', \"People'sRepublic\", 'Beijing', 116.286, 40.0495)\n",
      "('KGZ', 'Kyrgyzstan', 'Asia', 'Southern and Central Asia', 199900, 1991, 'Kyrgyzstan', 'Republic', 'Bishkek', 74.6057, 42.8851)\n",
      "('KIR', 'Kiribati', 'Oceania', 'Micronesia', 726, 1979, 'Kiribati', 'Republic', 'Tarawa', 172.979, 1.32905)\n",
      "('COL', 'Colombia', 'South America', 'South America', 1138910, 1810, 'Colombia', 'Republic', 'Bogota', 74.082, 4.60987)\n",
      "('COM', 'Com or os', 'Africa', 'Eastern Africa', 1862, 1975, 'Kom or i or Com or es', 'Republic', 'M or oni', 43.2418, 11.6986)\n",
      "('COG', 'Congo', 'Africa', 'Central Africa', 342000, 1960, 'Congo', 'Republic', 'Brazzaville', 15.2662, 4.2767)\n",
      "('COD', 'Congo The Democratic Republic of the', 'Africa', 'Central Africa', 2344860, 1960, 'Republique Democratique du Congo', 'Republic', 'Kinshasa', 15.3222, 4.325)\n",
      "('PRK', 'N or th K or ea', 'Asia', 'Eastern Asia', 120538, 1948, 'Choson Minjujuui In ymin Konghwaguk Bukhan', 'Socialistic Republic', 'Pyongyang', 125.754, 39.0319)\n",
      "('K or ', 'South K or ea', 'Asia', 'Eastern Asia', 99434, 1948, 'Taehan Miniguk or Namhan', 'Republic', 'Seoul', 126.957, 37.5323)\n",
      "('GRC', 'Greece', 'Europe', 'Southern Europe', 131626, 1830, 'Ellada', 'Republic', 'Athens', 23.7166, 37.9792)\n",
      "('HRV', 'Croatia', 'Europe', 'Southern Europe', 56538, 1991, 'Hrvatska', 'Republic', 'Zagreb', 15.9614, 45.8069)\n",
      "('CUB', 'Cuba', 'N or th America', 'Caribbean', 110861, 1902, 'Cuba', 'Socialistic Republic', 'Havana', 82.3667, 23.1333)\n",
      "('KWT', 'Kuwait', 'Asia', 'Middle East', 17818, 1961, 'AlKuwayt', 'Constitutional Monarchy or Emirate', 'Kuwait City', 47.9824, 29.3721)\n",
      "('CYP', 'Cyprus', 'Asia', 'Middle East', 9251, 1960, 'Kypros or Kibris', 'Republic', 'Nicosia', 33.3736, 35.1676)\n",
      "('LAO', 'Laos', 'Asia', 'Southeast Asia', 236800, 1953, 'Lao', 'Republic', 'Vientiane', 102.177, 18.5826)\n",
      "('LVA', 'Latvia', 'Europe', 'Baltic Countries', 64589, 1991, 'Latvija', 'Republic', 'Riga', 24.1048, 56.9465)\n",
      "('LSO', 'Lesotho', 'Africa', 'Southern Africa', 30355, 1966, 'Lesotho', 'Constitutional Monarchy', 'Maseru', 27.7167, 29.5208)\n",
      "('LBN', 'Lebanon', 'Asia', 'Middle East', 10400, 1941, 'Lubnan', 'Republic', 'Beirut', 35.5134, 33.8872)\n",
      "('LBR', 'Liberia', 'Africa', 'Western Africa', 111369, 1847, 'Liberia', 'Republic', 'Monrovia', 10.7957, 6.30039)\n",
      "('LBY', 'Libya', 'Africa', 'N or thern Africa', 1759540, 1951, 'Libiya', 'Socialistic State', 'Tripoli', 13.1072, 32.8578)\n",
      "('LIE', 'Liechtenstein', 'Europe', 'Western Europe', 160, 1806, 'Liechtenstein', 'Constitutional Monarchy', 'Vaduz', 9.52148, 47.1411)\n",
      "('LTU', 'Lithuania', 'Europe', 'Baltic Countries', 65301, 1991, 'Lietuva', 'Republic', 'Vilnius', 25.2799, 54.6896)\n",
      "('LUX', 'Luxembourg', 'Europe', 'Western Europe', 2586, 1867, 'Luxembourg or Letzebuerg', 'Constitutional Monarchy', 'Luxembourg', 6.1296, 49.61)\n",
      "('MAC', 'Macao', 'Asia', 'Eastern Asia', 18, 0, 'Macau or Aomen', 'Special Administrative Region of China', 'NA', 113.55, 22.1667)\n",
      "('MDG', 'Madagascar', 'Africa', 'Eastern Africa', 587041, 1960, 'Madagasikara or Madagascar', 'Federal Republic', 'Antananarivo', 45.7167, 20.4667)\n",
      "('MKD', 'Macedonia', 'Europe', 'Southern Europe', 25713, 1991, 'Makedonija', 'Republic', 'Skopje', 21.4361, 42.0024)\n",
      "('MWI', 'Malawi', 'Africa', 'Eastern Africa', 118484, 1964, 'Malawi', 'Republic', 'Lilongwe', 33.7703, 13.9899)\n",
      "('MDV', 'Maldives', 'Asia', 'Southern and Central Asia', 298, 1965, 'Dhivehi Raajje or Maldives', 'Republic', 'Male', 73.5109, 4.1742)\n",
      "('MYS', 'Malaysia', 'Asia', 'Southeast Asia', 329758, 1957, 'Malaysia', 'Constitutional Monarchy Federation', 'Kuala Lumpur', 101.684, 3.12433)\n",
      "('MLI', 'Mali', 'Africa', 'Western Africa', 1240190, 1960, 'Mali', 'Republic', 'Bamako', 7.50034, 13.5667)\n",
      "('MLT', 'Malta', 'Europe', 'Southern Europe', 316, 1964, 'Malta', 'Republic', 'Valletta', 14.5189, 35.9042)\n",
      "('MAR', 'M or occo', 'Africa', 'N or thern Africa', 446550, 1956, 'AlMaghrib', 'Constitutional Monarchy', 'Rabat', 6.8704, 33.9905)\n",
      "('MHL', 'Marshall Islands', 'Oceania', 'Micronesia', 181, 1990, 'Marshall Islands or Majol', 'Republic', 'Majuro', 171.135, 7.11046)\n",
      "('MRT', 'Mauritania', 'Africa', 'Western Africa', 1025520, 1960, 'Muritaniya or Mauritanie', 'Republic', 'Nouakchott', 15.9824, 18.2367)\n",
      "('MUS', 'Mauritius', 'Africa', 'Eastern Africa', 2040, 1968, 'Mauritius', 'Republic', 'P or t Louis', 57.4977, 20.1605)\n",
      "('MEX', 'Mexico', 'N or th America', 'Central America', 1958200, 1810, 'Mexico', 'Federal Republic', 'Mexico City', 99.1276, 19.427)\n",
      "('FSM', 'Micronesia  Federated States of', 'Oceania', 'Micronesia', 702, 1990, 'Micronesia', 'Federal Republic', 'Palikir', 158.185, 6.91771)\n",
      "('MDA', 'Moldova', 'Europe', 'Eastern Europe', 33851, 1991, 'Moldova', 'Republic', 'Chisinau', 28.8497, 47.0167)\n",
      "('MCO', 'Monaco', 'Europe', 'Western Europe', 2, 1861, 'Monaco', 'Constitutional Monarchy', 'Monaco', 7.41891, 43.7325)\n",
      "('MNG', 'Mongolia', 'Asia', 'Eastern Asia', 1566500, 1921, 'Mongol Uls', 'Republic', 'Ulaanbaatar', 106.937, 47.9129)\n",
      "('MOZ', 'Mozambique', 'Africa', 'Eastern Africa', 801590, 1975, 'Mocambique', 'Republic', 'Maputo', 32.5713, 25.9664)\n",
      "('MMR', 'Myanmar', 'Asia', 'Southeast Asia', 676578, 1948, 'Myanma Pye', 'Republic', 'Naypyidaw', 95.9562, 21.914)\n",
      "('NAM', 'Namibia', 'Africa', 'Southern Africa', 824292, 1990, 'Namibia', 'Republic', 'Windhoek', 17.0931, 22.5648)\n",
      "('NRU', 'Nauru', 'Oceania', 'Micronesia', 21, 1968, 'Naoero or Nauru', 'Republic', 'Yaren District', 166.920867, 0.5477)\n",
      "('NPL', 'Nepal', 'Asia', 'Southern and Central Asia', 147181, 1769, 'Nepal', 'Constitutional Monarchy', 'Kathmandu', 85.3157, 27.6939)\n",
      "('NIC', 'Nicaragua', 'N or th America', 'Central America', 130000, 1838, 'Nicaragua', 'Republic', 'Managua', 86.2734, 12.1475)\n",
      "('NER', 'Niger', 'Africa', 'Western Africa', 1267000, 1960, 'Niger', 'Republic', 'Niamey', 2.1073, 13.514)\n",
      "('NGA', 'Nigeria', 'Africa', 'Western Africa', 923768, 1960, 'Nigeria', 'Federal Republic', 'Abuja', 7.48906, 9.05804)\n",
      "('N or ', 'N or way', 'Europe', 'N or dic Countries', 323877, 1905, 'N or ge', 'Constitutional Monarchy', 'Oslo', 10.7387, 59.9138)\n",
      "('CIV', \"Cote d'Ivoire\", 'Africa', 'Western Africa', 322463, 1960, 'Cote d iIvoire', 'Republic', 'Yamoussoukro', 4.0305, 5.332)\n",
      "('OMN', 'Oman', 'Asia', 'Middle East', 309500, 1951, 'YUman', 'Monarchy or Sultanate', 'Muscat', 58.5874, 23.6105)\n",
      "('PAK', 'Pakistan', 'Asia', 'Southern and Central Asia', 796095, 1947, 'Pakistan', 'Republic', 'Islamabad', 72.8, 30.5167)\n",
      "('PLW', 'Palau', 'Oceania', 'Micronesia', 459, 1994, 'Belau or Palau', 'Republic', 'K or  or ', 134.479, 7.34194)\n",
      "('PAN', 'Panama', 'N or th America', 'Central America', 75517, 1903, 'Panama', 'Republic', 'Panama City', 79.5188, 8.99427)\n",
      "('PNG', 'Papua New Guinea', 'Oceania', 'Melanesia', 462840, 1975, 'Papua New Guinea or Papua Niugini', 'Constitutional Monarchy', 'P or t M or esby', 147.194, 9.47357)\n",
      "('PRY', 'Paraguay', 'South America', 'South America', 406752, 1811, 'Paraguay', 'Republic', 'Asuncion', 57.6362, 25.3005)\n",
      "('PER', 'Peru', 'South America', 'South America', 1285220, 1821, 'Peru or Piruw', 'Republic', 'Lima', 77.0465, 12.0931)\n",
      "('MNP', 'N or thern Mariana Islands', 'Oceania', 'Micronesia', 464, 0, 'N or thern Mariana Islands', 'Commonwealth of the US', 'Saipan', 145.765, 15.1935)\n",
      "('PRT', 'P or tugal', 'Europe', 'Southern Europe', 91982, 1143, 'P or tugal', 'Republic', 'Lisbon', 9.13552, 38.7072)\n",
      "('PRI', 'Puerto Rico', 'N or th America', 'Caribbean', 8875, 0, 'Puerto Rico', 'Commonwealth of the US', 'San Juan', 66.0, 18.23)\n",
      "('POL', 'Poland', 'Europe', 'Eastern Europe', 323250, 1918, 'Polska', 'Republic', 'Warsaw', 21.02, 52.26)\n",
      "('GNQ', 'Equat or ial Guinea', 'Africa', 'Central Africa', 28051, 1968, 'Guinea Ecuat or ial', 'Republic', 'Malabo', 8.7741, 3.7523)\n",
      "('QAT', 'Qatar', 'Asia', 'Middle East', 11000, 1971, 'Qatar', 'Monarchy', 'Doha', 51.5082, 25.2948)\n",
      "('FRA', 'France', 'Europe', 'Western Europe', 551500, 843, 'France', 'Republic', 'Paris', 2.35097, 48.8566)\n",
      "('PYF', 'French Polynesia', 'Oceania', 'Polynesia', 4000, 0, 'Polynesie francaise', 'Nonmetropolitan Territ or y of France', 'Papeete', 149.57, 17.535)\n",
      "('RWA', 'Rwanda', 'Africa', 'Eastern Africa', 26338, 1962, 'Rwanda or Urwanda', 'Republic', 'Kigali', 30.0587, 1.95325)\n",
      "('SWE', 'Sweden', 'Europe', 'N or dic Countries', 449964, 836, 'Sverige', 'Constitutional Monarchy', 'Stockholm', 18.0645, 59.3327)\n",
      "('KNA', 'Saint Kitts and Nevis', 'N or th America', 'Caribbean', 261, 1983, 'Saint Kitts and Nevis', 'Constitutional Monarchy', 'Basseterre', 62.7309, 17.3)\n",
      "('LCA', 'Saint Lucia', 'N or th America', 'Caribbean', 622, 1979, 'Saint Lucia', 'Constitutional Monarchy', 'Castries', 60.9832, 14.0)\n",
      "('VCT', 'Saint Vincent and the Grenadines', 'N or th America', 'Caribbean', 388, 1979, 'Saint Vincent and the Grenadines', 'Constitutional Monarchy', 'Kingstown', 61.2653, 13.2035)\n",
      "('DEU', 'Germany', 'Europe', 'Western Europe', 357022, 1955, 'Deutschland', 'Federal Republic', 'Berlin', 13.4115, 52.5235)\n",
      "('SLB', 'Solomon Islands', 'Oceania', 'Melanesia', 28896, 1978, 'Solomon Islands', 'Constitutional Monarchy', 'Honiara', 159.949, 9.42676)\n",
      "('ZMB', 'Zambia', 'Africa', 'Eastern Africa', 752618, 1964, 'Zambia', 'Republic', 'Lusaka', 28.2937, 15.3982)\n",
      "('WSM', 'Samoa', 'Oceania', 'Polynesia', 2831, 1962, 'Samoa', 'Parlementary Monarchy', 'Apia', 171.752, 13.8314)\n",
      "('SMR', 'San Marino', 'Europe', 'Southern Europe', 61, 885, 'San Marino', 'Republic', 'San Marino', 12.4486, 43.9322)\n",
      "('STP', 'Sao Tome and Principe', 'Africa', 'Central Africa', 964, 1975, 'Sao Tome e Principe', 'Republic', 'Sao Tome', 6.6071, 0.20618)\n",
      "('SAU', 'Saudi Arabia', 'Asia', 'Middle East', 2149690, 1932, 'Al Arabiya as Saudiya', 'Monarchy', 'Riyadh', 46.6977, 24.6748)\n",
      "('SEN', 'Senegal', 'Africa', 'Western Africa', 196722, 1960, 'Senegal or Sounougal', 'Republic', 'Dakar', 17.4734, 14.7247)\n",
      "('SYC', 'Seychelles', 'Africa', 'Eastern Africa', 455, 1976, 'Sesel or Seychelles', 'Republic', 'Vict or ia', 55.4466, 4.6309)\n",
      "('SLE', 'Sierra Leone', 'Africa', 'Western Africa', 71740, 1961, 'Sierra Leone', 'Republic', 'Freetown', 13.2134, 8.4821)\n",
      "('SGP', 'Singap or e', 'Asia', 'Southeast Asia', 618, 1965, 'Singap or e or Singapura or Xinjiapo or Singapur', 'Republic', 'Singap or e', 103.85, 1.28941)\n",
      "('SVK', 'Slovakia', 'Europe', 'Eastern Europe', 49012, 1993, 'Slovensko', 'Republic', 'Bratislava', 17.1073, 48.1484)\n",
      "('SVN', 'Slovenia', 'Europe', 'Southern Europe', 20256, 1991, 'Slovenija', 'Republic', 'Ljubljana', 14.5044, 46.0546)\n",
      "('SOM', 'Somalia', 'Africa', 'Eastern Africa', 637657, 1960, 'Soomaaliya', 'Republic', 'Mogadishu', 45.3254, 2.07515)\n",
      "('LKA', 'Sri Lanka', 'Asia', 'Southern and Central Asia', 65610, 1948, 'Sri Lanka or Ilankai', 'Republic', 'Colombo', 79.8528, 6.92148)\n",
      "('SDN', 'Sudan', 'Africa', 'N or thern Africa', 2505810, 1956, 'AsSudan', 'Islamic Republic', 'Khartoum', 32.5363, 15.5932)\n",
      "('FIN', 'Finland', 'Europe', 'N or dic Countries', 338145, 1917, 'Suomi', 'Republic', 'Helsinki', 24.9525, 60.1608)\n",
      "('SUR', 'Suriname', 'South America', 'South America', 163265, 1975, 'Suriname', 'Republic', 'Paramaribo', 55.1679, 5.8232)\n",
      "('SWZ', 'Swaziland', 'Africa', 'Southern Africa', 17364, 1968, 'kaNgwane', 'Monarchy', 'Mbabane', 31.4659, 26.5225)\n",
      "('CHE', 'Switzerland', 'Europe', 'Western Europe', 41284, 1499, 'Schweiz or Suisse or Svizzera or Svizra', 'Federation', 'Bern', 7.44821, 46.948)\n",
      "('SYR', 'Syria', 'Asia', 'Middle East', 185180, 1941, 'Suriya', 'Republic', 'Damascus', 36.3119, 33.5146)\n",
      "('TJK', 'Tajikistan', 'Asia', 'Southern and Central Asia', 143100, 1991, 'Tocikiston', 'Republic', 'Dushanbe', 68.7864, 38.5878)\n",
      "('TZA', 'Tanzania', 'Africa', 'Eastern Africa', 883749, 1961, 'Tanzania', 'Republic', 'Dodoma', 35.7382, 6.17486)\n",
      "('DNK', 'Denmark', 'Europe', 'N or dic Countries', 43094, 800, 'Danmark', 'Constitutional Monarchy', 'Copenhagen', 12.5681, 55.6763)\n",
      "('THA', 'Thailand', 'Asia', 'Southeast Asia', 513115, 1350, 'Prathet Thai', 'Constitutional Monarchy', 'Bangkok', 100.521, 13.7308)\n",
      "('TGO', 'Togo', 'Africa', 'Western Africa', 56785, 1960, 'Togo', 'Republic', 'Lome', 1.2255, 6.1228)\n",
      "('TON', 'Tonga', 'Oceania', 'Polynesia', 650, 1970, 'Tonga', 'Monarchy', \"Nuku'alofa\", 175.216, 21.136)\n",
      "('TTO', 'Trinidad and Tobago', 'N or th America', 'Caribbean', 5130, 1962, 'Trinidad and Tobago', 'Republic', 'P or tofSpain', 61.4789, 10.6596)\n",
      "('TCD', 'Chad', 'Africa', 'Central Africa', 1284000, 1960, 'Tchad or Tshad', 'Republic', \"N'Djamena\", 15.0445, 12.1048)\n",
      "('CZE', 'Czech Republic', 'Europe', 'Eastern Europe', 78866, 1993, 'lesko', 'Republic', 'Prague', 14.4205, 50.0878)\n",
      "('TUN', 'Tunisia', 'Africa', 'N or thern Africa', 163610, 1956, 'Tunis or Tunisie', 'Republic', 'Tunis', 10.21, 36.7899)\n",
      "('TUR', 'Turkey', 'Asia', 'Middle East', 774815, 1923, 'Turkiye', 'Republic', 'Ankara', 32.3606, 39.7153)\n",
      "('TKM', 'Turkmenistan', 'Asia', 'Southern and Central Asia', 488100, 1991, 'Turkmenostan', 'Republic', 'Ashgabat', 58.3794, 37.9509)\n",
      "('TCA', 'Turks and Caicos Islands', 'N or th America', 'Caribbean', 430, 0, 'The Turks and Caicos Islands', 'Dependent Territ or y of the UK', 'Grand Turk', 71.141389, 21.4602778)\n",
      "('TUV', 'Tuvalu', 'Oceania', 'Polynesia', 26, 1978, 'Tuvalu', 'Constitutional Monarchy', 'Funafuti', 179.089567, 8.6314877)\n",
      "('UGA', 'Uganda', 'Africa', 'Eastern Africa', 241038, 1962, 'Uganda', 'Republic', 'Kampala', 32.5729, 0.314269)\n",
      "('UKR', 'Ukraine', 'Europe', 'Eastern Europe', 603700, 1991, 'Ukrajina', 'Republic', 'Kiev', 30.5038, 50.4536)\n",
      "('HUN', 'Hungary', 'Europe', 'Eastern Europe', 93030, 1918, 'Magyar or szag', 'Republic', 'Budapest', 19.0408, 47.4984)\n",
      "('URY', 'Uruguay', 'South America', 'South America', 175016, 1828, 'Uruguay', 'Republic', 'Montevideo', 56.0675, 34.8941)\n",
      "('NCL', 'New Caledonia', 'Oceania', 'Melanesia', 18575, 0, 'NouvelleCaledonie', 'Nonmetropolitan Territ or y of France', \"Noum'ea\", 166.464, 22.2677)\n",
      "('NZL', 'New Zealand', 'Oceania', 'Australia and New Zealand', 270534, 1907, 'New Zealand or Aotearoa', 'Constitutional Monarchy', 'Wellington', 174.776, 41.2865)\n",
      "('UZB', 'Uzbekistan', 'Asia', 'Southern and Central Asia', 447400, 1991, 'Uzbekiston', 'Republic', 'Tashkent', 69.269, 41.3052)\n",
      "('BLR', 'Belarus', 'Europe', 'Eastern Europe', 207600, 1991, 'Belarus', 'Republic', 'Minsk', 27.5766, 53.9678)\n",
      "('VUT', 'Vanuatu', 'Oceania', 'Melanesia', 12189, 1980, 'Vanuatu', 'Republic', 'P or tVila', 168.321, 17.7404)\n",
      "('VEN', 'Venezuela', 'South America', 'South America', 912050, 1811, 'Venezuela', 'Federal Republic', 'Caracas', 69.8371, 9.08165)\n",
      "('RUS', 'Russian Federation', 'Europe', 'Eastern Europe', 17075400, 1991, 'Rossija', 'Federal Republic', 'Moscow', 37.6176, 55.7558)\n",
      "('VNM', 'Vietnam', 'Asia', 'Southeast Asia', 331689, 1945, 'Viet Nam', 'Socialistic Republic', 'Hanoi', 105.825, 21.0069)\n",
      "('EST', 'Estonia', 'Europe', 'Baltic Countries', 45227, 1991, 'Eesti', 'Republic', 'Tallinn', 24.7586, 59.4392)\n",
      "('USA', 'United States', 'N or th America', 'N or th America', 9363520, 1776, 'United States', 'Federal Republic', 'Washington D.C.', 77.032, 38.8895)\n",
      "('VIR', 'Virgin Islands U.S.', 'N or th America', 'Caribbean', 347, 0, 'Virgin Islands of the United States', 'US Territ or y', 'Charlotte Amalie', 64.8963, 18.3358)\n",
      "('ZWE', 'Zimbabwe', 'Africa', 'Eastern Africa', 390757, 1980, 'Zimbabwe', 'Republic', 'Harare', 31.0672, 17.8312)\n",
      "('PSE', 'Palestine', 'Asia', 'Middle East', 6257, 0, 'Filastin', 'Autonomous Area', 'NA', 0.0, 0.0)\n",
      "All records have been printed\n"
     ]
    }
   ],
   "source": [
    "# printing all records in 'nation' table\n",
    "cur = con.cursor()\n",
    "nation = \"SELECT * From nation\"\n",
    "cur.execute(nation)\n",
    "result = cur.fetchall()\n",
    "for rec in result:\n",
    "    print(rec)\n",
    "print(\"All records have been printed\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "0a7435f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_nation = pd.read_sql_query(\"SELECT * From nation\", con)\n",
    "df_cities = pd.read_sql_query(\"SELECT * From cities\", con)\n",
    "df_lang = pd.read_sql_query(\"SELECT * From lingo\", con)\n",
    "df_economy = pd.read_sql_query(\"SELECT * From economy\", con)\n",
    "df_customer = pd.read_sql_query(\"SELECT * From customer\", con)\n",
    "df_currency = pd.read_sql_query(\"SELECT * From currency\", con)\n",
    "df_sfeed = pd.read_sql_query(\"SELECT * From feedback\", con)\n",
    "df_population = pd.read_sql_query(\"SELECT * From population\", con)\n",
    "df_economy2019 = pd.read_sql_query(\"SELECT * From economy_2019\", con)\n",
    "df_economy2015 = pd.read_sql_query(\"SELECT * From economy_2015\", con)\n",
    "df_monarch = pd.read_sql_query(\"SELECT * From monarch\", con)\n",
    "df_states = pd.read_sql_query(\"SELECT * From states\", con)\n",
    "df_pm = pd.read_sql_query(\"SELECT * From prime_ministers\", con)\n",
    "df_president = pd.read_sql_query(\"SELECT * From president\", con)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "e0957ff0",
   "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>code</th>\n",
       "      <th>country_name</th>\n",
       "      <th>continent</th>\n",
       "      <th>region</th>\n",
       "      <th>surface_area</th>\n",
       "      <th>indep_year</th>\n",
       "      <th>local_name</th>\n",
       "      <th>gov_form</th>\n",
       "      <th>capital</th>\n",
       "      <th>cap_long</th>\n",
       "      <th>cap_lat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Southern and Central Asia</td>\n",
       "      <td>652090</td>\n",
       "      <td>1919</td>\n",
       "      <td>Afganistan and Afqanestan</td>\n",
       "      <td>Islamic Emirate</td>\n",
       "      <td>Kabul</td>\n",
       "      <td>69.17610</td>\n",
       "      <td>34.5228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NLD</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>41526</td>\n",
       "      <td>1581</td>\n",
       "      <td>Nederland</td>\n",
       "      <td>Constitutional Monarchy</td>\n",
       "      <td>Amsterdam</td>\n",
       "      <td>4.89095</td>\n",
       "      <td>52.3738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ALB</td>\n",
       "      <td>Albania</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Southern Europe</td>\n",
       "      <td>28748</td>\n",
       "      <td>1912</td>\n",
       "      <td>Shqiperia</td>\n",
       "      <td>Republic</td>\n",
       "      <td>Tirane</td>\n",
       "      <td>19.81720</td>\n",
       "      <td>41.3317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DZA</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>Africa</td>\n",
       "      <td>N or thern Africa</td>\n",
       "      <td>2381740</td>\n",
       "      <td>1962</td>\n",
       "      <td>Al Jaza or   or  Algerie</td>\n",
       "      <td>Republic</td>\n",
       "      <td>Algiers</td>\n",
       "      <td>3.05097</td>\n",
       "      <td>36.7397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ASM</td>\n",
       "      <td>American Samoa</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>Polynesia</td>\n",
       "      <td>199</td>\n",
       "      <td>0</td>\n",
       "      <td>Amerika Samoa</td>\n",
       "      <td>US Territ or y</td>\n",
       "      <td>Pago Pago</td>\n",
       "      <td>170.69100</td>\n",
       "      <td>14.2846</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  code    country_name continent                     region  surface_area  \\\n",
       "0  AFG     Afghanistan      Asia  Southern and Central Asia        652090   \n",
       "1  NLD     Netherlands    Europe             Western Europe         41526   \n",
       "2  ALB         Albania    Europe            Southern Europe         28748   \n",
       "3  DZA         Algeria    Africa          N or thern Africa       2381740   \n",
       "4  ASM  American Samoa   Oceania                  Polynesia           199   \n",
       "\n",
       "   indep_year                 local_name                 gov_form    capital  \\\n",
       "0        1919  Afganistan and Afqanestan          Islamic Emirate      Kabul   \n",
       "1        1581                  Nederland  Constitutional Monarchy  Amsterdam   \n",
       "2        1912                  Shqiperia                 Republic     Tirane   \n",
       "3        1962   Al Jaza or   or  Algerie                 Republic    Algiers   \n",
       "4           0              Amerika Samoa           US Territ or y  Pago Pago   \n",
       "\n",
       "    cap_long  cap_lat  \n",
       "0   69.17610  34.5228  \n",
       "1    4.89095  52.3738  \n",
       "2   19.81720  41.3317  \n",
       "3    3.05097  36.7397  \n",
       "4  170.69100  14.2846  "
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# displaying the 'nation' table in dataframe format\n",
    "df_nation.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "b4adb1b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "        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>econ_id</th>\n",
       "      <th>code</th>\n",
       "      <th>year</th>\n",
       "      <th>income_group</th>\n",
       "      <th>gdp_percapita</th>\n",
       "      <th>gross_savings</th>\n",
       "      <th>inflation_rate</th>\n",
       "      <th>total_investment</th>\n",
       "      <th>unemployment_rate</th>\n",
       "      <th>exports</th>\n",
       "      <th>imports</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>AFG</td>\n",
       "      <td>2010</td>\n",
       "      <td>Low income</td>\n",
       "      <td>539.667</td>\n",
       "      <td>37.133</td>\n",
       "      <td>2.179</td>\n",
       "      <td>30.402</td>\n",
       "      <td>0</td>\n",
       "      <td>46.394</td>\n",
       "      <td>24.381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>AFG</td>\n",
       "      <td>2015</td>\n",
       "      <td>Low income</td>\n",
       "      <td>615.091</td>\n",
       "      <td>21.466</td>\n",
       "      <td>-1.549</td>\n",
       "      <td>18.602</td>\n",
       "      <td>0</td>\n",
       "      <td>-49.110</td>\n",
       "      <td>-7.294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>AGO</td>\n",
       "      <td>2010</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>3599.270</td>\n",
       "      <td>23.534</td>\n",
       "      <td>14.480</td>\n",
       "      <td>14.433</td>\n",
       "      <td>0</td>\n",
       "      <td>-3.266</td>\n",
       "      <td>-21.076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>AGO</td>\n",
       "      <td>2015</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>3876.200</td>\n",
       "      <td>-0.425</td>\n",
       "      <td>10.287</td>\n",
       "      <td>9.552</td>\n",
       "      <td>0</td>\n",
       "      <td>6.721</td>\n",
       "      <td>-21.778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>ALB</td>\n",
       "      <td>2010</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>4098.130</td>\n",
       "      <td>20.011</td>\n",
       "      <td>3.605</td>\n",
       "      <td>31.305</td>\n",
       "      <td>14</td>\n",
       "      <td>10.645</td>\n",
       "      <td>-8.013</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   econ_id code  year         income_group  gdp_percapita  gross_savings  \\\n",
       "0        1  AFG  2010           Low income        539.667         37.133   \n",
       "1        2  AFG  2015           Low income        615.091         21.466   \n",
       "2        3  AGO  2010  Upper middle income       3599.270         23.534   \n",
       "3        4  AGO  2015  Upper middle income       3876.200         -0.425   \n",
       "4        5  ALB  2010  Upper middle income       4098.130         20.011   \n",
       "\n",
       "   inflation_rate  total_investment  unemployment_rate  exports  imports  \n",
       "0           2.179            30.402                  0   46.394   24.381  \n",
       "1          -1.549            18.602                  0  -49.110   -7.294  \n",
       "2          14.480            14.433                  0   -3.266  -21.076  \n",
       "3          10.287             9.552                  0    6.721  -21.778  \n",
       "4           3.605            31.305                 14   10.645   -8.013  "
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# displaying the 'economy' table in dataframe format\n",
    "df_economy.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "53718855",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>code</th>\n",
       "      <th>country_name</th>\n",
       "      <th>continent</th>\n",
       "      <th>region</th>\n",
       "      <th>surface_area</th>\n",
       "      <th>indep_year</th>\n",
       "      <th>local_name</th>\n",
       "      <th>gov_form</th>\n",
       "      <th>capital</th>\n",
       "      <th>cap_long</th>\n",
       "      <th>...</th>\n",
       "      <th>econ_id</th>\n",
       "      <th>year</th>\n",
       "      <th>income_group</th>\n",
       "      <th>gdp_percapita</th>\n",
       "      <th>gross_savings</th>\n",
       "      <th>inflation_rate</th>\n",
       "      <th>total_investment</th>\n",
       "      <th>unemployment_rate</th>\n",
       "      <th>exports</th>\n",
       "      <th>imports</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Southern and Central Asia</td>\n",
       "      <td>652090</td>\n",
       "      <td>1919</td>\n",
       "      <td>Afganistan and Afqanestan</td>\n",
       "      <td>Islamic Emirate</td>\n",
       "      <td>Kabul</td>\n",
       "      <td>69.17610</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>2010</td>\n",
       "      <td>Low income</td>\n",
       "      <td>539.667</td>\n",
       "      <td>37.133</td>\n",
       "      <td>2.179</td>\n",
       "      <td>30.402</td>\n",
       "      <td>0</td>\n",
       "      <td>46.394</td>\n",
       "      <td>24.381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Southern and Central Asia</td>\n",
       "      <td>652090</td>\n",
       "      <td>1919</td>\n",
       "      <td>Afganistan and Afqanestan</td>\n",
       "      <td>Islamic Emirate</td>\n",
       "      <td>Kabul</td>\n",
       "      <td>69.17610</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>2015</td>\n",
       "      <td>Low income</td>\n",
       "      <td>615.091</td>\n",
       "      <td>21.466</td>\n",
       "      <td>-1.549</td>\n",
       "      <td>18.602</td>\n",
       "      <td>0</td>\n",
       "      <td>-49.110</td>\n",
       "      <td>-7.294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NLD</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>41526</td>\n",
       "      <td>1581</td>\n",
       "      <td>Nederland</td>\n",
       "      <td>Constitutional Monarchy</td>\n",
       "      <td>Amsterdam</td>\n",
       "      <td>4.89095</td>\n",
       "      <td>...</td>\n",
       "      <td>253</td>\n",
       "      <td>2010</td>\n",
       "      <td>High income</td>\n",
       "      <td>50433.310</td>\n",
       "      <td>27.798</td>\n",
       "      <td>0.932</td>\n",
       "      <td>20.420</td>\n",
       "      <td>5</td>\n",
       "      <td>9.029</td>\n",
       "      <td>7.918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NLD</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>41526</td>\n",
       "      <td>1581</td>\n",
       "      <td>Nederland</td>\n",
       "      <td>Constitutional Monarchy</td>\n",
       "      <td>Amsterdam</td>\n",
       "      <td>4.89095</td>\n",
       "      <td>...</td>\n",
       "      <td>254</td>\n",
       "      <td>2015</td>\n",
       "      <td>High income</td>\n",
       "      <td>44322.830</td>\n",
       "      <td>27.854</td>\n",
       "      <td>0.220</td>\n",
       "      <td>19.272</td>\n",
       "      <td>7</td>\n",
       "      <td>4.957</td>\n",
       "      <td>5.749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ALB</td>\n",
       "      <td>Albania</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Southern Europe</td>\n",
       "      <td>28748</td>\n",
       "      <td>1912</td>\n",
       "      <td>Shqiperia</td>\n",
       "      <td>Republic</td>\n",
       "      <td>Tirane</td>\n",
       "      <td>19.81720</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>4098.130</td>\n",
       "      <td>20.011</td>\n",
       "      <td>3.605</td>\n",
       "      <td>31.305</td>\n",
       "      <td>14</td>\n",
       "      <td>10.645</td>\n",
       "      <td>-8.013</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  code country_name continent                     region  surface_area  \\\n",
       "0  AFG  Afghanistan      Asia  Southern and Central Asia        652090   \n",
       "1  AFG  Afghanistan      Asia  Southern and Central Asia        652090   \n",
       "2  NLD  Netherlands    Europe             Western Europe         41526   \n",
       "3  NLD  Netherlands    Europe             Western Europe         41526   \n",
       "4  ALB      Albania    Europe            Southern Europe         28748   \n",
       "\n",
       "   indep_year                 local_name                 gov_form    capital  \\\n",
       "0        1919  Afganistan and Afqanestan          Islamic Emirate      Kabul   \n",
       "1        1919  Afganistan and Afqanestan          Islamic Emirate      Kabul   \n",
       "2        1581                  Nederland  Constitutional Monarchy  Amsterdam   \n",
       "3        1581                  Nederland  Constitutional Monarchy  Amsterdam   \n",
       "4        1912                  Shqiperia                 Republic     Tirane   \n",
       "\n",
       "   cap_long  ...  econ_id  year         income_group gdp_percapita  \\\n",
       "0  69.17610  ...        1  2010           Low income       539.667   \n",
       "1  69.17610  ...        2  2015           Low income       615.091   \n",
       "2   4.89095  ...      253  2010          High income     50433.310   \n",
       "3   4.89095  ...      254  2015          High income     44322.830   \n",
       "4  19.81720  ...        5  2010  Upper middle income      4098.130   \n",
       "\n",
       "   gross_savings  inflation_rate  total_investment  unemployment_rate  \\\n",
       "0         37.133           2.179            30.402                  0   \n",
       "1         21.466          -1.549            18.602                  0   \n",
       "2         27.798           0.932            20.420                  5   \n",
       "3         27.854           0.220            19.272                  7   \n",
       "4         20.011           3.605            31.305                 14   \n",
       "\n",
       "   exports  imports  \n",
       "0   46.394   24.381  \n",
       "1  -49.110   -7.294  \n",
       "2    9.029    7.918  \n",
       "3    4.957    5.749  \n",
       "4   10.645   -8.013  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# inner joining 'nation' & 'economy' ON 'code' \n",
    "df_nation_economy = pd.merge(df_nation, df_economy, how ='inner', on ='code')\n",
    "df_nation_economy.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "81b506fd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape:  (362, 21)\n"
     ]
    }
   ],
   "source": [
    "print(\"Shape: \", df_nation_economy.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "f736cc67",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "code                 0\n",
       "country_name         0\n",
       "continent            0\n",
       "region               0\n",
       "surface_area         0\n",
       "indep_year           0\n",
       "local_name           0\n",
       "gov_form             0\n",
       "capital              0\n",
       "cap_long             0\n",
       "cap_lat              0\n",
       "econ_id              0\n",
       "year                 0\n",
       "income_group         0\n",
       "gdp_percapita        0\n",
       "gross_savings        0\n",
       "inflation_rate       0\n",
       "total_investment     0\n",
       "unemployment_rate    0\n",
       "exports              0\n",
       "imports              0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking null values in each column\n",
    "df_nation_economy.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "96779fc5",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Southern and Central Asia</td>\n",
       "      <td>652090</td>\n",
       "      <td>1919</td>\n",
       "      <td>Afganistan and Afqanestan</td>\n",
       "      <td>Islamic Emirate</td>\n",
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       "      <td>69.17610</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>2015</td>\n",
       "      <td>Low income</td>\n",
       "      <td>615.091</td>\n",
       "      <td>21.466</td>\n",
       "      <td>-1.549</td>\n",
       "      <td>18.602</td>\n",
       "      <td>0</td>\n",
       "      <td>-49.110</td>\n",
       "      <td>-7.294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NLD</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>41526</td>\n",
       "      <td>1581</td>\n",
       "      <td>Nederland</td>\n",
       "      <td>Constitutional Monarchy</td>\n",
       "      <td>Amsterdam</td>\n",
       "      <td>4.89095</td>\n",
       "      <td>...</td>\n",
       "      <td>254</td>\n",
       "      <td>2015</td>\n",
       "      <td>High income</td>\n",
       "      <td>44322.830</td>\n",
       "      <td>27.854</td>\n",
       "      <td>0.220</td>\n",
       "      <td>19.272</td>\n",
       "      <td>7</td>\n",
       "      <td>4.957</td>\n",
       "      <td>5.749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>ALB</td>\n",
       "      <td>Albania</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Southern Europe</td>\n",
       "      <td>28748</td>\n",
       "      <td>1912</td>\n",
       "      <td>Shqiperia</td>\n",
       "      <td>Republic</td>\n",
       "      <td>Tirane</td>\n",
       "      <td>19.81720</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>2015</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>3943.220</td>\n",
       "      <td>13.840</td>\n",
       "      <td>1.896</td>\n",
       "      <td>24.598</td>\n",
       "      <td>17</td>\n",
       "      <td>1.827</td>\n",
       "      <td>0.574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>DZA</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>Africa</td>\n",
       "      <td>N or thern Africa</td>\n",
       "      <td>2381740</td>\n",
       "      <td>1962</td>\n",
       "      <td>Al Jaza or   or  Algerie</td>\n",
       "      <td>Republic</td>\n",
       "      <td>Algiers</td>\n",
       "      <td>3.05097</td>\n",
       "      <td>...</td>\n",
       "      <td>96</td>\n",
       "      <td>2015</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>4123.300</td>\n",
       "      <td>34.738</td>\n",
       "      <td>4.784</td>\n",
       "      <td>51.299</td>\n",
       "      <td>11</td>\n",
       "      <td>5.733</td>\n",
       "      <td>-7.507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>AGO</td>\n",
       "      <td>Angola</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Central Africa</td>\n",
       "      <td>1246700</td>\n",
       "      <td>1975</td>\n",
       "      <td>Angola</td>\n",
       "      <td>Republic</td>\n",
       "      <td>Luanda</td>\n",
       "      <td>13.24200</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>2015</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>3876.200</td>\n",
       "      <td>-0.425</td>\n",
       "      <td>10.287</td>\n",
       "      <td>9.552</td>\n",
       "      <td>0</td>\n",
       "      <td>6.721</td>\n",
       "      <td>-21.778</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  code country_name continent                     region  surface_area  \\\n",
       "1  AFG  Afghanistan      Asia  Southern and Central Asia        652090   \n",
       "3  NLD  Netherlands    Europe             Western Europe         41526   \n",
       "5  ALB      Albania    Europe            Southern Europe         28748   \n",
       "7  DZA      Algeria    Africa          N or thern Africa       2381740   \n",
       "9  AGO       Angola    Africa             Central Africa       1246700   \n",
       "\n",
       "   indep_year                 local_name                 gov_form    capital  \\\n",
       "1        1919  Afganistan and Afqanestan          Islamic Emirate      Kabul   \n",
       "3        1581                  Nederland  Constitutional Monarchy  Amsterdam   \n",
       "5        1912                  Shqiperia                 Republic     Tirane   \n",
       "7        1962   Al Jaza or   or  Algerie                 Republic    Algiers   \n",
       "9        1975                     Angola                 Republic     Luanda   \n",
       "\n",
       "   cap_long  ...  econ_id  year         income_group gdp_percapita  \\\n",
       "1  69.17610  ...        2  2015           Low income       615.091   \n",
       "3   4.89095  ...      254  2015          High income     44322.830   \n",
       "5  19.81720  ...        6  2015  Upper middle income      3943.220   \n",
       "7   3.05097  ...       96  2015  Upper middle income      4123.300   \n",
       "9  13.24200  ...        4  2015  Upper middle income      3876.200   \n",
       "\n",
       "   gross_savings  inflation_rate  total_investment  unemployment_rate  \\\n",
       "1         21.466          -1.549            18.602                  0   \n",
       "3         27.854           0.220            19.272                  7   \n",
       "5         13.840           1.896            24.598                 17   \n",
       "7         34.738           4.784            51.299                 11   \n",
       "9         -0.425          10.287             9.552                  0   \n",
       "\n",
       "   exports  imports  \n",
       "1  -49.110   -7.294  \n",
       "3    4.957    5.749  \n",
       "5    1.827    0.574  \n",
       "7    5.733   -7.507  \n",
       "9    6.721  -21.778  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# removing the 2010 year data under new dataframe for further analysis\n",
    "df_nation_economy2015 = df_nation_economy \n",
    "df_nation_economy2015 = df_nation_economy2015[df_nation_economy2015.year != 2010]\n",
    "df_nation_economy2015.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "a3cc59ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(181, 21)"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# taking selected columns for the analysis\n",
    "df_nation_economy2015.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "568657b6",
   "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>region</th>\n",
       "      <th>exports</th>\n",
       "      <th>imports</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Southern and Central Asia</td>\n",
       "      <td>-49.110</td>\n",
       "      <td>-7.294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Western Europe</td>\n",
       "      <td>4.957</td>\n",
       "      <td>5.749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Southern Europe</td>\n",
       "      <td>1.827</td>\n",
       "      <td>0.574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>N or thern Africa</td>\n",
       "      <td>5.733</td>\n",
       "      <td>-7.507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Central Africa</td>\n",
       "      <td>6.721</td>\n",
       "      <td>-21.778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>353</th>\n",
       "      <td>Eastern Europe</td>\n",
       "      <td>-0.412</td>\n",
       "      <td>-25.036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>355</th>\n",
       "      <td>Southeast Asia</td>\n",
       "      <td>9.796</td>\n",
       "      <td>15.617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>357</th>\n",
       "      <td>Baltic Countries</td>\n",
       "      <td>-0.610</td>\n",
       "      <td>-1.404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>359</th>\n",
       "      <td>N or th America</td>\n",
       "      <td>0.109</td>\n",
       "      <td>4.583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>361</th>\n",
       "      <td>Eastern Africa</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>181 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                        region  exports  imports\n",
       "1    Southern and Central Asia  -49.110   -7.294\n",
       "3               Western Europe    4.957    5.749\n",
       "5              Southern Europe    1.827    0.574\n",
       "7            N or thern Africa    5.733   -7.507\n",
       "9               Central Africa    6.721  -21.778\n",
       "..                         ...      ...      ...\n",
       "353             Eastern Europe   -0.412  -25.036\n",
       "355             Southeast Asia    9.796   15.617\n",
       "357           Baltic Countries   -0.610   -1.404\n",
       "359            N or th America    0.109    4.583\n",
       "361             Eastern Africa    0.000    0.000\n",
       "\n",
       "[181 rows x 3 columns]"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# taking selected columns for the analysis\n",
    "reg = df_nation_economy2015[['region', 'exports', 'imports']]\n",
    "reg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "01a8f2dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
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       "        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>exports</th>\n",
       "      <th>imports</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>region</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Australia and New Zealand</th>\n",
       "      <td>12.967</td>\n",
       "      <td>5.691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Baltic Countries</th>\n",
       "      <td>1.590</td>\n",
       "      <td>6.866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>British Islands</th>\n",
       "      <td>40.516</td>\n",
       "      <td>27.179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Caribbean</th>\n",
       "      <td>75.675</td>\n",
       "      <td>81.082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Central Africa</th>\n",
       "      <td>61.464</td>\n",
       "      <td>-28.192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Central America</th>\n",
       "      <td>21.776</td>\n",
       "      <td>70.246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Eastern Africa</th>\n",
       "      <td>6.333</td>\n",
       "      <td>69.680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Eastern Asia</th>\n",
       "      <td>-14.566</td>\n",
       "      <td>-26.309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Eastern Europe</th>\n",
       "      <td>25.011</td>\n",
       "      <td>-36.531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Melanesia</th>\n",
       "      <td>16.972</td>\n",
       "      <td>-27.740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Micronesia</th>\n",
       "      <td>16.935</td>\n",
       "      <td>19.430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Middle East</th>\n",
       "      <td>8.795</td>\n",
       "      <td>-14.861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N or dic Countries</th>\n",
       "      <td>18.935</td>\n",
       "      <td>23.329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N or th America</th>\n",
       "      <td>3.502</td>\n",
       "      <td>4.919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N or thern Africa</th>\n",
       "      <td>3.238</td>\n",
       "      <td>39.049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Polynesia</th>\n",
       "      <td>-9.110</td>\n",
       "      <td>5.403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South America</th>\n",
       "      <td>-13.246</td>\n",
       "      <td>-25.287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Southeast Asia</th>\n",
       "      <td>22.700</td>\n",
       "      <td>27.088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Southern Africa</th>\n",
       "      <td>10.557</td>\n",
       "      <td>10.756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Southern Europe</th>\n",
       "      <td>54.618</td>\n",
       "      <td>50.186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Southern and Central Asia</th>\n",
       "      <td>-38.363</td>\n",
       "      <td>-1.697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Western Africa</th>\n",
       "      <td>-53.323</td>\n",
       "      <td>23.904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Western Europe</th>\n",
       "      <td>35.959</td>\n",
       "      <td>39.487</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           exports  imports\n",
       "region                                     \n",
       "Australia and New Zealand   12.967    5.691\n",
       "Baltic Countries             1.590    6.866\n",
       "British Islands             40.516   27.179\n",
       "Caribbean                   75.675   81.082\n",
       "Central Africa              61.464  -28.192\n",
       "Central America             21.776   70.246\n",
       "Eastern Africa               6.333   69.680\n",
       "Eastern Asia               -14.566  -26.309\n",
       "Eastern Europe              25.011  -36.531\n",
       "Melanesia                   16.972  -27.740\n",
       "Micronesia                  16.935   19.430\n",
       "Middle East                  8.795  -14.861\n",
       "N or dic Countries          18.935   23.329\n",
       "N or th America              3.502    4.919\n",
       "N or thern Africa            3.238   39.049\n",
       "Polynesia                   -9.110    5.403\n",
       "South America              -13.246  -25.287\n",
       "Southeast Asia              22.700   27.088\n",
       "Southern Africa             10.557   10.756\n",
       "Southern Europe             54.618   50.186\n",
       "Southern and Central Asia  -38.363   -1.697\n",
       "Western Africa             -53.323   23.904\n",
       "Western Europe              35.959   39.487"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# grouping the regions to analyse the imports and exports\n",
    "reg.groupby('region').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "18078a09",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "        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>index</th>\n",
       "      <th>region</th>\n",
       "      <th>exports</th>\n",
       "      <th>imports</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>115</td>\n",
       "      <td>British Islands</td>\n",
       "      <td>34.452</td>\n",
       "      <td>21.674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>323</td>\n",
       "      <td>Central Africa</td>\n",
       "      <td>29.216</td>\n",
       "      <td>-21.443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>113</td>\n",
       "      <td>Southern and Central Asia</td>\n",
       "      <td>23.770</td>\n",
       "      <td>-8.313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>125</td>\n",
       "      <td>Caribbean</td>\n",
       "      <td>21.670</td>\n",
       "      <td>20.353</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index                     region  exports  imports\n",
       "0    115            British Islands   34.452   21.674\n",
       "1    323             Central Africa   29.216  -21.443\n",
       "2    113  Southern and Central Asia   23.770   -8.313\n",
       "3    125                  Caribbean   21.670   20.353"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# displaying the top 4 'exports' region in 2015\n",
    "exp = reg.sort_values(by='exports', ascending = False).reset_index()\n",
    "exp = exp[:4]\n",
    "exp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "25ce98d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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pLpO0rqRtI+LsTgZmZmad0cyQDROAM9och5mZDZBa/fgv6+OhNzSyY0nDgeuAZfJxfhURR0paDTgfGA3cB7wvIp7sR8xmZtaCWk0976rxWCNX7r4I7BgRz0oaBlwv6XfAe4CrI+J4SYcDhwOH1dqRmZm1T62mnkeBk0kndquXrzSy40iezavD8hLAJOCsXH4WsGe/ozYzs6bVqvGfT5qa8f7qQkm3kZpw6pI0FJgJrAf8KCL+JmmNiJgDEBFzJK3ex3MnA5MB1lprrUYOZ2ZmDajVnfPgPsp/C/y2kZ1HxEJgnKRVgIsljW00sIiYShoagvHjx3tQODOzNhmQ0Tkj4ilJ04FdgEckjcq1/VGkJiUzMxsgfbbx59E5/w38kVTzPjXf/7ekN9fbsaSRuaaPpGWBtwN3AZex+MrfA4BLW4jfzMz6qZOjc44Czsrt/EOACyLickl/AS6Q9BHgAWCf1l6CmZn1R8dG54yIWcDmvZQ/DuzUvzDNusfow68oOoSG3Hf87kWHYE3y6JxmZl3Go3OamXWZRkbn3I00vAJ4dE4zs9JrZHTOiwcoFjMzGwC1unOOkXS1pIclnV+5wlbS+yUtHLgQzcysnWqN1fNTYCLpV8E+wJ9z04+ZmZVYrcS/FfD9iBgBbAksC0wDnPzNzEqsVuJ/idSlk4iYAexISv7HDkBcZmbWIbUS/21UXVUbEf8kXXj1VIdjMjOzDqrVq+djwOslvSoiXgaIiDvyOD3rDkh0ZmbWdrX68d8N3N1L+WzS2D1mZlZCzUy2bmZmJebEb2bWZWomfklDJX1O0hYDFZCZmXVWzcSfp078MrDJwIRjZmadVnOsnuwy4BBJjwBzKoURcVPHojIzs45pJPF/gjQs82U9yoe2PxwzM+u0RhL/2Swej9/MzEqubuKPiAMHIA4zMxsgdRO/pOWBz5FO8A7PxRERe3cyMDMz64xGmnp+Bryf1NyjXOamHzOzkmrkAq63Az/M998P/Ar4Wr0nSXqDpGmS7pT0D0kH5/JvSHpQ0i152a3p6M3MrN8aSfwrALNItf1VgL8Dn27geS8DX4iIDUlj+39a0kb5sZMiYlxeftv/sM3MrFmNNPXMJiX//wA/IX0B/LfekyJiDrnff0TMk3Qn8PrmQzUzs3ZopMb/CeAvpGGaZ5Bq/Af15yCSRgObA3/LRZ+RNEvS6ZJW7eM5kyXNkDTjscce68/hzMyshrqJPyKuBm4mTcCyS0RsFRHTGj2ApBWAi4BDIuIZ0q+GdYFxpF8E3+3juFMjYnxEjB85cmSjhzMzszrqJn5Jm5OaeWYA4yXdIenURnYuaRgp6Z8bEb8GiIhHImJhRCwCTiXN52tmZgOkkaaeHwLPkdr2FwHnknr61CRJwGnAnRHxvaryUVWb7QXc3p+AzcysNY0k/s2AM6vWHwJWb+B52wIfBHbs0XXzREm3SZoFTAQO7WfMZmbWgkZ79eyQ728K7AvcV+9JEXE9iy/4qubum2ZmBWok8Z9IunoX0olYAQd2KiAzM+usPhO/pF+ReuKcLukeYDdS0r88Iq4dqADNzKy9atX43wO8U9I3ge9ExPSBCcnMrH1GH35F0SE05L7jdx+wY9U6uXsg8AzwLeA2SZMkvbmyDEh0ZmbWdn3W+CPi7Nzc8yPgAODXPTbxDFxmZiVUq41/WeAwYJ9cNB14YABiMjOzDqrVxv8v4HWkrpuHRMRvBiQiMzPrqFpt/COAY4CNnfTNzAaPWjX+jSPingGLxMzMBkSfNX4nfTOzwamRsXrMzGwQceI3M+syDSV+SZ+SdImkt0g6XNKOnQ7MzMw6o5GJWI4jjcn/bmBlYEPgqA7HZWZmHdLI6JwfAk4BJuf164FJHYvISqkM46EM5FgoZkuzRpp6liXNjVvxemBBZ8IxM7NOa6TGPw34fL7/HWATXjluj5mZlUQjNf7PAjfn+5sBf8LTJZqZlVbdGn9EPARMlLR8Xn+u41GZmVnH1E38ktYHvgiMBoZKAoiI2KmzoZmZWSc00sZ/CbB+j7JofyhmZjYQGmnjXwU4CRgFjMzL6vWeJOkNkqZJulPSPyQdnMtXk3SVpH/n21VbiN/MzPqpz8SfE/RqwI+B9YAVSDX9ylLPy8AXImJDYCvg05I2Ag4Hro6INwJX53UzMxsgtZp65rJkgn9X1f2o81wiYg65/39EzJN0J+kagEnAhLzZWaSZvQ7rT9BmZta8Wsn7OtrUli9pNLA58DdgjfylQETMkdRrs5GkyeSrhddaa612hGFmZtRO/EcAd0bEY60cQNIKwEWk6Rufyb2C6oqIqcBUgPHjx/tksplZm9Q6uTsNmNjKziUNIyX9cyOicrXvI5JG5cdHAY+2cgwzM+ufWom/sap5X09OVfvTSL8avlf10GXAAfn+AcClrRzHzMz6p14//iMkfaKX8kYu4NoW+CBwm6RbctlXgeOBCyR9BHgA2Kcf8ZqZWYvqJf6N8tJT3Tb3iLievn81+KpfM7OC1Ev8HyNduWtmZoNEvcQ/LyIeH5BIzMxsQNQ6uXsUcPtABWJmZgOjzxp/RHheXTOzQaiRQdrMzGwQceI3M+syjYzHD4CklYGhlfWIeKIjEZmZWUfVrfFLOkDS48ATwGN58TALZmYl1UiN/3ukmv4NwMLOhmNmZp3WSOJ/FPhBRPy408GYmVnnNZL4Pwz8RtKWwDO5LCLi4M6FZWZmndJI4j8BWA34UFVZAE78ZmYl1Eji3wg4Jy8LOhuOmZl1WiOJfyowBrgHeKqj0ZiZWcc1kvgPJzXtVI+bX3eydTMzWzo1krzbNum6mZkVr27ij4gJAxCHmZkNkEau3F1Z0pmSHsnL6Xn4BjMzK6FGBmn7Pqkr50t5ORCY0rmQzMyskxpJ/LsCJ0bEGyLiDcC3gd07G5aZmXVKM8My+0SvmVmJNZL4fwt8SdIDkh4AvgRcUe9J+VzAo5Juryr7hqQHJd2Sl92aD93MzJrRSOI/BDgXWDYv5wCHNvC8M4Fdeik/KSLG5eW3DcZpZmZtUrM7p6ShwP8DzoiID9XatqeIuE7S6BZiMzOzDqhZ44+IhcCewLptPOZnJM3KTUGrtnG/ZmbWgEaaeqYDR0j6tKT3VJYmj/cT0pfIOGAO8N2+NpQ0WdIMSTMee+yxJg9nZmY9NTJkw0H59vv5VqSePUN737xvEfFI5b6kU4HLa2w7lTRAHOPHj3dPIjOzNmkk8R/VroNJGhURc/LqXsDttbY3M7P26zPxS/ocqSvnN4FtgFsj4ulGdyzpPGACMELSbOBIYIKkcaRfDPcBH282cDMza06tGv9JwMPAk8A04B3ANY3uOCL27aX4tH5FZ2ZmbVfr5K76uG9mZiVWr41/HWDTfP+Nkp6qPBARN3UqKDMz65x6if9bVfd/3OOxfvfqMTOz4tVK/GcNWBRmZjZg+kz8EXFQX4+ZmVl5NTMss5mZlZgTv5lZl3HiNzPrMo0M2YCkTYEdgcuA1wP3RcR/OxmYmZl1Rt3EL+n/SJOvDAFmAV8BniWNtWNmZiXTSFPPUSw5VMPlpLF7zMyshBpJ/K9jycT/MmkKRjMzK6FG2vhvAyrTLn6QNI/urR2LyMzMOqqRGv8XgNeSBmo7ABgGfLGTQZmZWefUrfFHxF8krQdsTUr+f46IJzsemZmZdUQjvXo+1KPo3XmUzr9GxKMdicrMzDqmkTb+M0kzZvX0rKQ9IuLa9oZkZmad1Egb/1nAI8BxwPH5/rnAXJYcttnMzEqgkRr/WODIiDgVQNL9pLlyvwSc0cHYzMysAxpJ/KOBz0l6Oa9/htTLZx6ektHMrHQaSfzHA98GfsbiRP9FYAxwSWfCMjOzTmmkO+d3JV0FTMhF0yNiVr5/SqcCMzOzzmh0WOYngJnALcAqkrav9wRJp0t6VNLtVWWrSbpK0r/z7apNRW1mZk2rm/glnQjcD1wHTKta6jmTNLxDtcOBqyPijcDVed3MzAZQI238k4H7gD+QBmhrSERcJ2l0j+JJLG4yOguYDhzW6D7NzKx1jST+W4FfRcQP2nC8NSJiDkBEzJG0el8bSppM+tJhrbXWasOhzcwMGkv8TwLfkrRlvg8QEXFw58KCiJgKTAUYP358b1cOm5lZExpJ/Hvk2/2qygJoJvE/ImlUru2PAjzWj5nZAGsk8U9s4/EuIw3tfHy+vbSN+zYzswY00o//WgBJywDLNLpjSeeRTuSOkDQbOJKU8C+Q9BHgAWCfJmI2M7MWNDIs83bAacB6VcVR77kRsW8fD+3UcHRmZtZ2jVzANQVYnjRcw03AAlIffDMzK6FGEv+GwMmkWv7Xga8Bz3YyKDMz65xGTu6+DDwOLAIOBJYDduhgTGZm1kGNJP47SEMzXwm8P5f9sVMBmZlZZzXSq2drAEnLA/uT2vp/3uG4zMysQxoanVPStsCewAvA88BeHYzJzMw6qJHunOcC/1ddRDrRe06ngjIzs85ppI1/d9JY/BfRj9E5zcxs6dRI4r8KmBERJ3Q6GDMz67w+E7+ky/LdFYFjJb2LJUfnnNTp4MzMrP1q1fjf1WN926r7HibZzKykaiX+dQYsCjMzGzC1unMOBzaJiPsrC2lEzU2AZQckOjMza7taif8Ulpx8hYgIUtfOqZ0MyszMOqdW4t+c3kfhnA6M60QwZmbWefWu3B3TYJmZmZVErZO7fwE+L+lJ0gBtAbwTOJRU6zczsxKqlfi/BlxHmi7x+KryF4CvdjIoMzPrnD6beiJiJrAF8Avgrrz8HNgyIm4emPDMzKzd6s2bewfwwQGKxczMBkBDwzKbmdng0cggbW0n6T5gHrAQeDkixhcRh5lZNyok8WcTI2Jugcc3M+tKDTf1SNpA0rmSzpc0roMxmZlZB/Wnjf8M4EVgPeC8Fo8bwB8kzZQ0ubcNJE2WNEPSjMcee6zFw5mZWUWfiV/STyStVlW0OnAacAPw2haPu21EvBnYFfi0pO17bhARUyNifESMHzlyZIuHMzOzilo1/n8DMyUdLGko8D3SFbufZMkLuvotIh7Kt48CFwNbtrI/MzNrXK0LuL5HuoBrA+BW4B5gZWDVVqZhlLS8pBUr94Gdgdub3Z+ZmfVPvTb+lYGfki7i+gLwK+B1LR5zDeB6SbcCNwJXRMTvW9ynmZk1qNacu98lDcgWwN+B7YHdgUsl/T4iDm3mgBFxD7BZM881M7PW1arxfww4CphAavLZLCIuJo3FP6fjkZmZWUfUuoDrn6QmnneQunHeDxARLwIndj40MzPrhFo1/knA+cAM0lW2jw5MSGZm1kl91vhzl8uvDWAsZmY2ADw6p5lZl3HiNzPrMk78ZmZdxonfzKzLOPGbmXUZJ34zsy7jxG9m1mWc+M3MuowTv5lZl3HiNzPrMk78ZmZdxonfzKzLOPGbmXUZJ34zsy7jxG9m1mWc+M3MuowTv5lZlykk8UvaRdI/Jd0t6fAiYjAz61YDnvglDQV+BOwKbATsK2mjgY7DzKxbFVHj3xK4OyLuiYiXgF+SJnY3M7MBoIgY2ANK7wV2iYiP5vUPAm+NiM/02G4yMDmvrg/8c0ADbc4IYG7RQQwifj/bx+9le5Xl/Vw7Ikb2LHxVAYGol7JXfPtExFRgaufDaR9JMyJifNFxDBZ+P9vH72V7lf39LKKpZzbwhqr1NYGHCojDzKwrFZH4/w68UdI6kl4N/B9wWQFxmJl1pQFv6omIlyV9BrgSGAqcHhH/GOg4OqRUTVMl4Pezffxetlep388BP7lrZmbF8pW7ZmZdxonfzKzLOPGbmXWZIvrxDxqSTgSOBV4Afg9sBhwSET8vNDAzQNJY0rAowytlEXF2cRGVm6RVgTey5Pt5XXERNc8nd1sg6ZaIGCdpL2BP4FBgWkRsVmxk5SXp9cDaVFVKyvrhKpKkI4EJpMT/W9LYWNdHxHuLjKusJH0UOJh03dEtwFbAXyJixyLjapZr/K0Zlm93A86LiCek3i5MtkZIOgF4P3AHsDAXB+DE33/vJf0CvTkiDpK0BvCzgmMqs4OBLYC/RsRESRsARxUcU9Oc+FvzG0l3kZp6PiVpJDC/4JjKbE9g/Yh4sehABoEXImKRpJclrQQ8CowpOqgSmx8R8yUhaZmIuEvS+kUH1Swn/hZExOG5lvpMRCyU9DweabQV95B+RTnxt26GpFWAU4GZwLPAjYVGVG6z8/t5CXCVpCcp8VAzbuNvgqT31Ho8In49ULEMJpIuIjVPXE1V8o+IzxUW1CAgaTSwUkTMKjqWwUDSDsDKwO/z0PKl48TfBEln5LurA9sA1+T1icD0iKj5xWC9k3RAb+URcdZAx1J2ucPBNRHxdF5fBZgQEZcUGVfZSFopIp6RtFpvj0fEEwMdUzs48bdA0uXAxyJiTl4fBfzIid+KVulx1qPs5ojYvKCQSknS5RHxLkn3kjoaVPfeiIgo5XkTt/G3ZnQl6WePAG8qKpiyk/RG4Dhe2fe8lB+ugvV2caY/7/0UEe/Kt+sUHUs7+crd1kyXdKWkA3MzxRXAtKKDKrEzgJ8AL5Oazc4Gzik0ovKaIel7ktaVNEbSSaSTvNYESdtKWj7f3z+/t2sVHVez3NTTonyi92159bqIuLjIeMpM0syIeIuk2yJik1z2p4h4W73n2pJykvp/wNtJzRN/AI6NiOcKDaykJM0idTzYlFQZOQ14T0TsUGhgTfJPvxblHjzuxdMe8yUNAf6d52x4kHQC3fopJ/jDi45jEHk5IkLSJODkiDitr84IZeDE34Jc2z+BlJyUl4iIlQoNrLwOAZYDPgccQ2ruKe2HqwiSpkTEIZJ+Q+9zWe9RQFiDwTxJXwH2B7aXNJTFV+6Xjpt6WiDpbuDdEXFn0bEMJpKWd5NEcyS9JSJm5r7mrxAR1w50TIOBpNcCHwD+HhF/yu37E8o66J0Tfwsk3RAR2xYdx2AhaWtS2+kKEbGWpM2Aj0fEpwoOrVRybfSsiNi/6FgGK0nbAftGxKeLjqUZbuppzQxJ55Mu466+0tRt/s2ZArwTuAwgIm6VtH2hEZVQHj5kpKRXl/XK0qWRpHGkWv/7gHuBiwoNqAVO/K1ZCXge2LmqLPDJ3qZFxH97jHC6sK9trab7gBskXQb8r9ksIr5XWEQlJOlNwP8B+wKPA+eTWkomFhpYi5z4WxARBxUdwyDzX0nbACHp1aSTvD5/0pyH8jIEWLHgWMrsLuBPpHN5dwNIOrTYkFrnxN8CScOBjwAbs+SVph8uLKhy+wRwMvB6UlfOK4FStqEWRdI5EfFB4KmIOLnoeAaBvUk1/mmSfg/8kiWHbSgln9xtgaQLSTWCDwBHA/sBd0bEwYUGZl1L0h2k2bYuI83AtUSSKuugYkXLF8TtSWry2RE4C7g4Iv5QZFzNcuJvQWXQK0mzImJTScOAK8s6HVvRJI0h1fi3Ip0r+QtwaETcU2hgJSLpc8AnSZOuPMggGVRsaZJH6twHeH9ZP+seq6c1C/LtU3li65WB0cWFU3q/AC4ARgGvAy4Ezis0opKJiO9HxIbA6RExJiLWqSykIcStRRHxREScUtakD078rZoqaVXSmCiXkeaKPbHYkEpNEXFORLycl5/Ty9WnVl9EfBJA0sqSPizpj3iQNsvc1GOFq5rk4svAU6QTaEGaeH2ZiDimoNBKSdKywB6kc09vJvXq2ZM0iOCiAkOzpYQTfxMkfb7W4+4r3T99THJR4XbpfpB0LrA9aTTOX5Jmh7t7sI0nb61xd87muF90GzkptdVY4EnS9Q935at4XbtrkqR59N7cWOoBGV3jt6VGvi7iU8B2pA/bn4CfRsT8QgMrGUkbkJp53g88CmwAbBIRDxcamC01nPhbIOlE4FjgBeD3pIkaDsknJa2fJF0AzAMq79++wKoRsU9xUZWbpPGk93EfYHZEuGdPCyStzpIXaz5QYDhNc+JvQWVCa0l7kU6eHQpMi4jNio2snCTd2vO9663M+k9pAKTtPSxzcyTtAXyX1M34UWBt0sWaGxcaWJPcnbM1lYkYdgPO81WRLbtZ0laVFUlvBW4oMJ5BIxIn/eYdQ7qw8F/5nNROlPh/0yd3W/MbSXeRmno+JWkk4PbofpJ0G6lNfxjwIUkP5PW1SddGmBVtQUQ8LmmIpCERMU3SCUUH1Sw39bQoX8D1TO49sRywkk+i9Y+ktWs9HhH3D1QsZr3JF8DtCRwHjCA192xR1nMmTvxNyHPt9skTsfSPpJUi4pmqC7mW4Ca0xvkak87Ig7S9QGoe3480PMu5EfF4oYE1yU09zXl3jcc8EUv//QJ4F2lIgZ4XcgVpwDFrjK8xabM8leWlEfF2YBFpZM5Sc43flgq518kbyto9zga3PJPZByPi6aJjaQfX+G2pEBEh6WLgLUXHMhh4kqC2mw/cJukqlpzK8nPFhdQ8J35bmvxV0hYR8feiAxkEziFNEvROqiYJKjSicrsiL9VK21zixG9Lk4nAxyXdT6pVVcZD2bTYsEppvYjYR9KkiDhL0i9IU1lac1bpOZWlpNLOtOfE36I8Ofhoqt7LiDi7sIDKbdeiAxhEek4S9DCeJKgVB5Bmh6t2YC9lpeDE3wJJ5wDrArcAC3NxAE78Taj01+85Hoo1pTJJ0NdJkwStQJowyPpB0r6kAe/WySd4K1YEStmVE5z4WzUe2CjcNaot+hoPhXSC0hokaQjposIngetwd9hW/BmYQ7po67tV5fOAWYVE1AbuztkCSRcCn4uIOUXHMhhIuhXYEfhjnsR+IrBvREwuOLTSkXRdRGxfdBy2dHKNvwmSfkNq0lkRuEPSjcCLlccjYo+iYiu5QTUeSsGukvRF4HyW7H7oq6CbkK/WPwFYndTpwBOxdBtJO9R63KMgNmewjYdSpDydZU+exrJJku4G3h0Rg6JLrBN/Cyrjd0TEIklvIs109LuIWFDnqVZF0nrAGqST5NXjoawNXBERM4uLrpwkDe85c1lvZdYYSTdExLZFx9EuTvwtkDQTeBuwKvBXYAbwfETsV2hgJSPpcuCrETGrR/l44MiIqDU2kvVC0k0R8eZ6ZdYYSScDrwUuYclm3VKOy+U2/tYoIp6X9BHgBxFxoqRbig6qhEb3TPoAETFD0ugC4iktSa8FXg8sK2lzFg94txKwXGGBld9KwPPAzlVlpR2Q0Ym/NZK0NalZ4iO5bGiB8ZRVrT77yw5YFIPDO0kXFq0JVA/BPA/4ahEBDQYRcVDRMbSTE39rDgG+AlwcEf+QNAaYVmxIpfR3SR+LiFOrC/MvKbfv90NEnAWcJWnviLio6HgGi3wO7yfAGhExVtKmwB4RcWzBoTXFbfxWOElrABcDL7E40Y8HXg3s5RnN+k/SMsDevHI4kaOLiqnMJF0LfAk4JSI2z2W3R8TYYiNrjmv8TZA0JSIOqerPvwT34++fiHgE2CZfsFX5IF0REdcUGFbZXQo8TfoifbHOtlbfchFxY5o24n9eLiqYVjnxN+ecfPudQqMYZCJiGm4qa5c1I2KXooMYROZKWpdc0ZP0XtJQDqXkxN+Eqn7l4/oYqtUXcFnR/ixpk4i4rehABolPA1OBDSQ9CNwL7F9sSM1zG38L+ugrfXOlDdCsKJLuANYjJagX8dwGbZEv2hwSEfOKjqUVrvE3YbAO1WqDiuc2aANJnweejojTACLiuVz+WWBoREwpMLymucbfBElrA+uQxpQ5vOqhecCsiCjtSR8bPCRtB7wxIs6QNBJYISJ6G8PH+iDpduDNEfFSj/JlgL+X9ReUa/xNyBOG3A9sXXQsZr2RdCSpS+z6wBnAMODnwKAZb2aARM+knwtfVI8uPmUypOgAykjS9fl2nqRnqpZ5kp4pOj4zYC9gD/KQzBHxEKkp0vopX2dSt6xMnPibEBHb5dsVI2KlqmXFso7PbYPOS3lmuEr3w+ULjqesvg1cIWkHSSvmZQLwG0rcndtNPU3K09vNKuuVezboXSDpFGAVSR8DPgycWuc51kNEnC3pMeBo0sWFAfyDNGrs7woNrgU+udsCSecCX4mIB4qOxawnSe8gjSYp4MqIuKrgkGwp4cTfAknXAFsAN7Lk9HYessEKUZnUJiJu6FG+PfBgRPynmMhsaeKmntYcVXQAZj1Moffhl5/Pj3lSG3ONv10kjQAeD7+hVqBaI0ZKui0iNhnomGzp4xp/EyRtBRwPPAEcQxq0bQQwRNKHIuL3RcZnXc2T2rRRvnK3TxHxvVqPL62c+JvzQ9LP6ZWBa4BdI+KvkjYAzgOc+K0ontSmvSrXPqxPOp9XGaLl3cB1hUTUBm7qaYKkWyJiXL5/Z0RsWPWYB2mzwnhSm86Q9Adg78rgbJJWBC4s69DXrvE3Z1HV/Rd6POZvUiuMJ7XpmLVIX6YVL5FmNyslJ/7mbJaHZhCwbNUwDaJ2G6vZgPCkNm13DnCjpItJlbu9gLOLDal5buoxM2uApLcA2+XV6yLi5iLjaYUTv5lZAyQNBdZgycnrS3nVvpt6zMzqyBOvHAk8Aiwkz2gGlHI8ftf4zczqkHQ38NaIGBQz7HlYZjOz+v4LPF10EO3iph4zs/ruAaZLuoI0eT3gK3fNzAazB/Ly6ryUmtv4zcy6jGv8ZmZ1SBoJfBnYmKqLNCNix8KCaoFP7pqZ1XcucBewDmkejvuAvxcZUCvc1GNmVoekmRHxFkmzImLTXHZtROxQdGzNcFOPmVl9C/LtHEm7Aw8BaxYYT0uc+M3M6jtW0srAF4AfACsBhxYbUvPc1GNm1mV8ctfMrMs48ZuZdRknfjOzLuOTu2ZmdUhaBtibNN1i9Xj8RxcVUyuc+M3M6ruUNDrnTKoGaSsr9+oxM6tD0u0RMbb+luXgNn4zs/r+LGmTooNoF9f4zczqkHQHsB5wL6mpR0BUhm8oGyd+M7MaJAl4G3B/z8ci4hVlZeDEb2ZWR2WQtqLjaBe38ZuZ1fdXSVsUHUS7uMZvZlZHbuNfnzQO/3O4jd/MbHCTtHZv5WVt43dTj5lZHTnBvwHYMd9/nhLnT9f4zczqkHQkMB5YPyLeJOl1wIURsW3BoTWltN9YZmYDaC9gD1L7PhHxELBioRG1wInfzKy+lyI1jwSApOULjqclTvxmZvVdIOkUYBVJHwP+CJxacExNcxu/mVkDJL0D2JnUlfPKiLiq4JCa5sRvZtZl3NRjZlaHpPdI+rekpyU9I2mepGeKjqtZrvGbmdUh6W7g3RFxZ9GxtINr/GZm9T0yWJI+uMZvZtYnSe/Jd3cAXgtcQtXUixHx6wLCapkTv5lZHySdUePhiIgPD1gwbeTEb2ZWh6RtI+KGemVl4cRvZlaHpJsi4s31ysriVUUHYGa2tJK0NbANMFLS56seWgkYWkxUrXPiNzPr26uBFUi5snpQtmeA9xYSURu4qcfMrA5Ja5d10pXeOPGbmdUhaRp5ZM5qEbFjAeG0zE09Zmb1fbHq/nBgb+DlgmJpmWv8ZmZNkHRtROxQdBzNcI3fzKwOSatVrQ4B3kK6kreUnPjNzOqbSWrjF6mJ517gI4VG1AI39ZiZdRnX+M3M6pA0DPgksH0umg6cEhELCguqBa7xm5nVIelnwDDgrFz0QWBhRHy0uKia58RvZlaHpFsjYrN6ZWXhiVjMzOpbKGndyoqkMcDCAuNpidv4zczq+xIwTdI9pJ49awMHFRtS89zUY2bWAEnLAOuTEv9dEfFinacstdzUY2bWB0lbSHotQE7044CjgW/3uKirVJz4zcz6dgrwEoCk7YHjgbOBp4GpBcbVErfxm5n1bWhEPJHvvx+YGhEXARdJuqW4sFrjGr+ZWd+GSqpUkHcCrql6rLQV59IGbmY2AM4DrpU0F3gB+BOApPVIzT2l5F49ZmY1SNoKGAX8ISKey2VvAlaIiJsKDa5JTvxmZl3GbfxmZl3Gid/MrMs48VtXkjRaUuRlkaRHJH1fUtOfCUnfyPt7bztjNWs3J37rdjcDHwL+A3yWNIl2s34F7Av8tQ1xmXWME791u4ci4ufAj/L6GEkrSzpd0qOS5kqaKmk5AEmbSrpN0hxJx+Qa/vT83PeSuv9tlbedlLd9TtLtkibl8gn5eZdKuk7S05K+PbAv27qZE791u2GS1gAm5PW/A1NIE22cCfyMNLfq0fnxM4EN8zZ9jsUuaX3gQtLkHYeSrpm5MJdXvB24CHgc+KKktVp/OWb1+QIu63Y7Aw/n+1Mi4hpJ55M+G1+q3k7SSsDmwA0RcYKkNwLv7mO/7yAl/e9GxKmSgjS2y9uBf+RtLo2IkyVtCHycNNTvA+18cWa9ceK3bvc3Uu39OODTks7M5Q+Tav0VL5KG4wXoz8UvtbatjAHzcr4d2o/9mjXNid+63dyI+KWk54FLSU06lwMHAnsAtwJvARZFxJ8k3QxsLelLwNtq7PcqYAHwBUkiNfcsAP5IugrUrDBu4zcDIuIyYCap6WYKqW3/fcDJpJO1N+RNDwTuBL4I3J7Lnuplf/8E9iHV5k8GFgHvy+VmhfKQDWb9IGkCsAbphOyBwH7AZyPih8VFZdY/buox65/XACfl2weBo4AfFxqRWT+5xm9m1mXcxm9m1mWc+M3MuowTv5lZl3HiNzPrMk78ZmZd5v8D0uIrN+TfmtUAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# bar plot of top 4 exports region in 2015\n",
    "exp.groupby(['region']).sum().plot(kind='bar', y='exports')\n",
    "plt.title(\"Top 4 Exports Region in 2015\", fontweight='bold')\n",
    "plt.xlabel(\"Region\", fontweight='bold')\n",
    "plt.ylabel(\"% Change From The Year 2010\", fontweight='bold')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "ff8dc02f",
   "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>Feedback_Id</th>\n",
       "      <th>Cust_Id</th>\n",
       "      <th>Phone</th>\n",
       "      <th>Rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>53251345</td>\n",
       "      <td>Excellent</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>12233366</td>\n",
       "      <td>Excellent</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>65656589</td>\n",
       "      <td>Excellent</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>99888999</td>\n",
       "      <td>Excellent</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>13235583</td>\n",
       "      <td>Good</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Feedback_Id  Cust_Id     Phone     Rating\n",
       "0            1        5  53251345  Excellent\n",
       "1            2        8  12233366  Excellent\n",
       "2            3        1  65656589  Excellent\n",
       "3            4       10  99888999  Excellent\n",
       "4            5        2  13235583       Good"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# displaying the first five rows of 'service_feedback' table\n",
    "df_sfeed.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "e5e20ae2",
   "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>Cust_Id</th>\n",
       "      <th>Customer_First_Name</th>\n",
       "      <th>Customer_Last_Name</th>\n",
       "      <th>Customer_DOB</th>\n",
       "      <th>Street</th>\n",
       "      <th>City</th>\n",
       "      <th>Country</th>\n",
       "      <th>Postal_Code</th>\n",
       "      <th>Phone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>George</td>\n",
       "      <td>Donald</td>\n",
       "      <td>3/21/88</td>\n",
       "      <td>89, Beckam Street</td>\n",
       "      <td>New York</td>\n",
       "      <td>USA</td>\n",
       "      <td>1234</td>\n",
       "      <td>65656589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Matt</td>\n",
       "      <td>Lee</td>\n",
       "      <td>12/13/99</td>\n",
       "      <td>500 Pkey Street</td>\n",
       "      <td>California</td>\n",
       "      <td>USA</td>\n",
       "      <td>61434</td>\n",
       "      <td>13322355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Ann</td>\n",
       "      <td>Joseph</td>\n",
       "      <td>9/15/55</td>\n",
       "      <td>77 Holmes Street</td>\n",
       "      <td>London</td>\n",
       "      <td>GBR</td>\n",
       "      <td>75849</td>\n",
       "      <td>36553284, 80008065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Sam</td>\n",
       "      <td>Li</td>\n",
       "      <td>5/5/00</td>\n",
       "      <td>3, Park Street</td>\n",
       "      <td>Biejling</td>\n",
       "      <td>China</td>\n",
       "      <td>99849</td>\n",
       "      <td>99553246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Jennifer</td>\n",
       "      <td>Rose</td>\n",
       "      <td>8/24/80</td>\n",
       "      <td>99 Calum Ave</td>\n",
       "      <td>Barcelona</td>\n",
       "      <td>Spain</td>\n",
       "      <td>28902</td>\n",
       "      <td>53251345, 65656565</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Cust_Id Customer_First_Name Customer_Last_Name Customer_DOB  \\\n",
       "0        1              George             Donald      3/21/88   \n",
       "1        2                Matt                Lee     12/13/99   \n",
       "2        3                 Ann             Joseph      9/15/55   \n",
       "3        4                 Sam                 Li       5/5/00   \n",
       "4        5            Jennifer               Rose      8/24/80   \n",
       "\n",
       "              Street        City Country  Postal_Code               Phone  \n",
       "0  89, Beckam Street    New York     USA         1234            65656589  \n",
       "1    500 Pkey Street  California     USA        61434            13322355  \n",
       "2   77 Holmes Street      London     GBR        75849  36553284, 80008065  \n",
       "3     3, Park Street    Biejling   China        99849            99553246  \n",
       "4       99 Calum Ave   Barcelona   Spain        28902  53251345, 65656565  "
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# displaying the first five rows of 'customer' table\n",
    "df_customer.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "4040f92c",
   "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>index</th>\n",
       "      <th>Counts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>USA</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>India</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>GBR</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Australia</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>China</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Spain</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Singapore</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Canada</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>UAE</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>New Zealand</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>South Africa</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Switzerland</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Ireland</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           index  Counts\n",
       "0            USA       4\n",
       "1          India       3\n",
       "2            GBR       2\n",
       "3      Australia       2\n",
       "4          China       1\n",
       "5          Spain       1\n",
       "6      Singapore       1\n",
       "7         Canada       1\n",
       "8            UAE       1\n",
       "9    New Zealand       1\n",
       "10  South Africa       1\n",
       "11   Switzerland       1\n",
       "12       Ireland       1"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# getting the counts of countries to which the customers belong\n",
    "df_cust = df_customer['Country'].value_counts().reset_index(name = 'Counts')\n",
    "df_cust"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "b4ee2281",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# pie chart of 'A&S Consulting Group Customer Base'\n",
    "data = [4, 3, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n",
    "country = ['USA', 'India', 'GBR', 'Australia', 'China', 'Spain', 'Singapore', 'Canada', 'UAE', 'New Zealand', 'South Africa', 'Switzerland', 'Ireland']\n",
    "explode = [0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
    "palette_color = sns.color_palette('bright')\n",
    "plt.figure(figsize=(15,8))\n",
    "plt.pie(data, labels = country, colors = palette_color, explode = explode, autopct = '%.0f%%')\n",
    "plt.title('A&S Consulting Group Customer Base', fontweight='bold')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "396274e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# bar plot of 'A&S Consulting Group Customer Base'\n",
    "plt.figure(figsize=(15,5))\n",
    "sns.countplot(x ='Country', data = df_customer)\n",
    "plt.title(\"A&S Consulting Group Customer Base\", fontweight='bold')\n",
    "plt.xlabel(\"Customer Base\", fontweight='bold')\n",
    "plt.ylabel(\"Customer Count\", fontweight='bold')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "2a002781",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# bar plot of 'A&S Consulting Group Rating'\n",
    "plt.figure(figsize=(15,5))\n",
    "sns.countplot(x ='Rating', data = df_sfeed)\n",
    "plt.title(\"A&S Consulting Group Rating\", fontweight='bold')\n",
    "plt.xlabel(\"Rating\", fontweight='bold')\n",
    "plt.ylabel(\"Rating Count\", fontweight='bold')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "cad72ca5",
   "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>pop_id</th>\n",
       "      <th>country_code</th>\n",
       "      <th>year</th>\n",
       "      <th>fertility_rate</th>\n",
       "      <th>life_expectancy</th>\n",
       "      <th>size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20</td>\n",
       "      <td>ABW</td>\n",
       "      <td>2010</td>\n",
       "      <td>1.704</td>\n",
       "      <td>74.953537</td>\n",
       "      <td>101597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19</td>\n",
       "      <td>ABW</td>\n",
       "      <td>2015</td>\n",
       "      <td>1.647</td>\n",
       "      <td>75.573585</td>\n",
       "      <td>103889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>AFG</td>\n",
       "      <td>2010</td>\n",
       "      <td>5.746</td>\n",
       "      <td>58.970829</td>\n",
       "      <td>27962207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>AFG</td>\n",
       "      <td>2015</td>\n",
       "      <td>4.653</td>\n",
       "      <td>60.717171</td>\n",
       "      <td>32526562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12</td>\n",
       "      <td>AGO</td>\n",
       "      <td>2010</td>\n",
       "      <td>6.416</td>\n",
       "      <td>50.654171</td>\n",
       "      <td>21219954</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pop_id country_code  year  fertility_rate  life_expectancy      size\n",
       "0      20          ABW  2010           1.704        74.953537    101597\n",
       "1      19          ABW  2015           1.647        75.573585    103889\n",
       "2       2          AFG  2010           5.746        58.970829  27962207\n",
       "3       1          AFG  2015           4.653        60.717171  32526562\n",
       "4      12          AGO  2010           6.416        50.654171  21219954"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# displaying the first five rows of 'population' tabe\n",
    "df_population.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "ccd303fb",
   "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>index</th>\n",
       "      <th>pop_id</th>\n",
       "      <th>country_code</th>\n",
       "      <th>year</th>\n",
       "      <th>fertility_rate</th>\n",
       "      <th>life_expectancy</th>\n",
       "      <th>size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>165</td>\n",
       "      <td>170</td>\n",
       "      <td>HKG</td>\n",
       "      <td>2015</td>\n",
       "      <td>1.195</td>\n",
       "      <td>84.278049</td>\n",
       "      <td>7305700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>197</td>\n",
       "      <td>194</td>\n",
       "      <td>JPN</td>\n",
       "      <td>2015</td>\n",
       "      <td>1.460</td>\n",
       "      <td>83.843659</td>\n",
       "      <td>126958472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>191</td>\n",
       "      <td>190</td>\n",
       "      <td>ITA</td>\n",
       "      <td>2015</td>\n",
       "      <td>1.370</td>\n",
       "      <td>83.490244</td>\n",
       "      <td>60730582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>119</td>\n",
       "      <td>356</td>\n",
       "      <td>ESP</td>\n",
       "      <td>2015</td>\n",
       "      <td>1.320</td>\n",
       "      <td>83.380488</td>\n",
       "      <td>46443994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>67</td>\n",
       "      <td>376</td>\n",
       "      <td>CHE</td>\n",
       "      <td>2015</td>\n",
       "      <td>1.540</td>\n",
       "      <td>83.197561</td>\n",
       "      <td>8281430</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index  pop_id country_code  year  fertility_rate  life_expectancy  \\\n",
       "0    165     170          HKG  2015           1.195        84.278049   \n",
       "1    197     194          JPN  2015           1.460        83.843659   \n",
       "2    191     190          ITA  2015           1.370        83.490244   \n",
       "3    119     356          ESP  2015           1.320        83.380488   \n",
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     "execution_count": 114,
     "metadata": {},
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   "source": [
    "# removing the 2010 year data under new dataframe for further analysis\n",
    "population2015 = df_population \n",
    "population2015 = population2015[population2015.year != 2010]\n",
    "population2015.sort_values(by='life_expectancy', ascending = False).head().reset_index()"
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   "source": [
    "# histogram plot - 'Life Expectancy of Top 10 Countries'\n",
    "pop2015 = px.histogram(population2015.sort_values(by ='life_expectancy', ascending = False).head(10).reset_index(), x ='country_code', y = 'life_expectancy', template = 'plotly_white', text_auto = True, color = 'country_code', title = 'Life Expectancy of Top 10 Countries')\n",
    "pop2015.show()"
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  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "121397db",
   "metadata": {},
   "outputs": [
    {
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       "      <th>code</th>\n",
       "      <th>country_name</th>\n",
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       "      <th>1</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Asia</td>\n",
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       "      <th>3</th>\n",
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       "      <td>Netherlands</td>\n",
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       "      <td>Nederland</td>\n",
       "      <td>Constitutional Monarchy</td>\n",
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       "      <td>4.89095</td>\n",
       "      <td>...</td>\n",
       "      <td>254</td>\n",
       "      <td>2015</td>\n",
       "      <td>High income</td>\n",
       "      <td>44322.830</td>\n",
       "      <td>27.854</td>\n",
       "      <td>0.220</td>\n",
       "      <td>19.272</td>\n",
       "      <td>7</td>\n",
       "      <td>4.957</td>\n",
       "      <td>5.749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>ALB</td>\n",
       "      <td>Albania</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Southern Europe</td>\n",
       "      <td>28748</td>\n",
       "      <td>1912</td>\n",
       "      <td>Shqiperia</td>\n",
       "      <td>Republic</td>\n",
       "      <td>Tirane</td>\n",
       "      <td>19.81720</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>2015</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>3943.220</td>\n",
       "      <td>13.840</td>\n",
       "      <td>1.896</td>\n",
       "      <td>24.598</td>\n",
       "      <td>17</td>\n",
       "      <td>1.827</td>\n",
       "      <td>0.574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>DZA</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>Africa</td>\n",
       "      <td>N or thern Africa</td>\n",
       "      <td>2381740</td>\n",
       "      <td>1962</td>\n",
       "      <td>Al Jaza or   or  Algerie</td>\n",
       "      <td>Republic</td>\n",
       "      <td>Algiers</td>\n",
       "      <td>3.05097</td>\n",
       "      <td>...</td>\n",
       "      <td>96</td>\n",
       "      <td>2015</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>4123.300</td>\n",
       "      <td>34.738</td>\n",
       "      <td>4.784</td>\n",
       "      <td>51.299</td>\n",
       "      <td>11</td>\n",
       "      <td>5.733</td>\n",
       "      <td>-7.507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>AGO</td>\n",
       "      <td>Angola</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Central Africa</td>\n",
       "      <td>1246700</td>\n",
       "      <td>1975</td>\n",
       "      <td>Angola</td>\n",
       "      <td>Republic</td>\n",
       "      <td>Luanda</td>\n",
       "      <td>13.24200</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>2015</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>3876.200</td>\n",
       "      <td>-0.425</td>\n",
       "      <td>10.287</td>\n",
       "      <td>9.552</td>\n",
       "      <td>0</td>\n",
       "      <td>6.721</td>\n",
       "      <td>-21.778</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  code country_name continent                     region  surface_area  \\\n",
       "1  AFG  Afghanistan      Asia  Southern and Central Asia        652090   \n",
       "3  NLD  Netherlands    Europe             Western Europe         41526   \n",
       "5  ALB      Albania    Europe            Southern Europe         28748   \n",
       "7  DZA      Algeria    Africa          N or thern Africa       2381740   \n",
       "9  AGO       Angola    Africa             Central Africa       1246700   \n",
       "\n",
       "   indep_year                 local_name                 gov_form    capital  \\\n",
       "1        1919  Afganistan and Afqanestan          Islamic Emirate      Kabul   \n",
       "3        1581                  Nederland  Constitutional Monarchy  Amsterdam   \n",
       "5        1912                  Shqiperia                 Republic     Tirane   \n",
       "7        1962   Al Jaza or   or  Algerie                 Republic    Algiers   \n",
       "9        1975                     Angola                 Republic     Luanda   \n",
       "\n",
       "   cap_long  ...  econ_id  year         income_group gdp_percapita  \\\n",
       "1  69.17610  ...        2  2015           Low income       615.091   \n",
       "3   4.89095  ...      254  2015          High income     44322.830   \n",
       "5  19.81720  ...        6  2015  Upper middle income      3943.220   \n",
       "7   3.05097  ...       96  2015  Upper middle income      4123.300   \n",
       "9  13.24200  ...        4  2015  Upper middle income      3876.200   \n",
       "\n",
       "   gross_savings  inflation_rate  total_investment  unemployment_rate  \\\n",
       "1         21.466          -1.549            18.602                  0   \n",
       "3         27.854           0.220            19.272                  7   \n",
       "5         13.840           1.896            24.598                 17   \n",
       "7         34.738           4.784            51.299                 11   \n",
       "9         -0.425          10.287             9.552                  0   \n",
       "\n",
       "   exports  imports  \n",
       "1  -49.110   -7.294  \n",
       "3    4.957    5.749  \n",
       "5    1.827    0.574  \n",
       "7    5.733   -7.507  \n",
       "9    6.721  -21.778  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# creating a duplicate dataframe of 'df_nation'\n",
    "df_eco2015 = df_nation_economy2015 \n",
    "df_eco2015.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "39121b02",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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_code</th>\n",
       "      <th>country_name</th>\n",
       "      <th>continent</th>\n",
       "      <th>region</th>\n",
       "      <th>surface_area</th>\n",
       "      <th>indep_year</th>\n",
       "      <th>local_name</th>\n",
       "      <th>gov_form</th>\n",
       "      <th>capital</th>\n",
       "      <th>cap_long</th>\n",
       "      <th>...</th>\n",
       "      <th>econ_id</th>\n",
       "      <th>year</th>\n",
       "      <th>income_group</th>\n",
       "      <th>gdp_percapita</th>\n",
       "      <th>gross_savings</th>\n",
       "      <th>inflation_rate</th>\n",
       "      <th>total_investment</th>\n",
       "      <th>unemployment_rate</th>\n",
       "      <th>exports</th>\n",
       "      <th>imports</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Southern and Central Asia</td>\n",
       "      <td>652090</td>\n",
       "      <td>1919</td>\n",
       "      <td>Afganistan and Afqanestan</td>\n",
       "      <td>Islamic Emirate</td>\n",
       "      <td>Kabul</td>\n",
       "      <td>69.17610</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>2015</td>\n",
       "      <td>Low income</td>\n",
       "      <td>615.091</td>\n",
       "      <td>21.466</td>\n",
       "      <td>-1.549</td>\n",
       "      <td>18.602</td>\n",
       "      <td>0</td>\n",
       "      <td>-49.110</td>\n",
       "      <td>-7.294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NLD</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>41526</td>\n",
       "      <td>1581</td>\n",
       "      <td>Nederland</td>\n",
       "      <td>Constitutional Monarchy</td>\n",
       "      <td>Amsterdam</td>\n",
       "      <td>4.89095</td>\n",
       "      <td>...</td>\n",
       "      <td>254</td>\n",
       "      <td>2015</td>\n",
       "      <td>High income</td>\n",
       "      <td>44322.830</td>\n",
       "      <td>27.854</td>\n",
       "      <td>0.220</td>\n",
       "      <td>19.272</td>\n",
       "      <td>7</td>\n",
       "      <td>4.957</td>\n",
       "      <td>5.749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>ALB</td>\n",
       "      <td>Albania</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Southern Europe</td>\n",
       "      <td>28748</td>\n",
       "      <td>1912</td>\n",
       "      <td>Shqiperia</td>\n",
       "      <td>Republic</td>\n",
       "      <td>Tirane</td>\n",
       "      <td>19.81720</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>2015</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>3943.220</td>\n",
       "      <td>13.840</td>\n",
       "      <td>1.896</td>\n",
       "      <td>24.598</td>\n",
       "      <td>17</td>\n",
       "      <td>1.827</td>\n",
       "      <td>0.574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>DZA</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>Africa</td>\n",
       "      <td>N or thern Africa</td>\n",
       "      <td>2381740</td>\n",
       "      <td>1962</td>\n",
       "      <td>Al Jaza or   or  Algerie</td>\n",
       "      <td>Republic</td>\n",
       "      <td>Algiers</td>\n",
       "      <td>3.05097</td>\n",
       "      <td>...</td>\n",
       "      <td>96</td>\n",
       "      <td>2015</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>4123.300</td>\n",
       "      <td>34.738</td>\n",
       "      <td>4.784</td>\n",
       "      <td>51.299</td>\n",
       "      <td>11</td>\n",
       "      <td>5.733</td>\n",
       "      <td>-7.507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>AGO</td>\n",
       "      <td>Angola</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Central Africa</td>\n",
       "      <td>1246700</td>\n",
       "      <td>1975</td>\n",
       "      <td>Angola</td>\n",
       "      <td>Republic</td>\n",
       "      <td>Luanda</td>\n",
       "      <td>13.24200</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>2015</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>3876.200</td>\n",
       "      <td>-0.425</td>\n",
       "      <td>10.287</td>\n",
       "      <td>9.552</td>\n",
       "      <td>0</td>\n",
       "      <td>6.721</td>\n",
       "      <td>-21.778</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  country_code country_name continent                     region  \\\n",
       "1          AFG  Afghanistan      Asia  Southern and Central Asia   \n",
       "3          NLD  Netherlands    Europe             Western Europe   \n",
       "5          ALB      Albania    Europe            Southern Europe   \n",
       "7          DZA      Algeria    Africa          N or thern Africa   \n",
       "9          AGO       Angola    Africa             Central Africa   \n",
       "\n",
       "   surface_area  indep_year                 local_name  \\\n",
       "1        652090        1919  Afganistan and Afqanestan   \n",
       "3         41526        1581                  Nederland   \n",
       "5         28748        1912                  Shqiperia   \n",
       "7       2381740        1962   Al Jaza or   or  Algerie   \n",
       "9       1246700        1975                     Angola   \n",
       "\n",
       "                  gov_form    capital  cap_long  ...  econ_id  year  \\\n",
       "1          Islamic Emirate      Kabul  69.17610  ...        2  2015   \n",
       "3  Constitutional Monarchy  Amsterdam   4.89095  ...      254  2015   \n",
       "5                 Republic     Tirane  19.81720  ...        6  2015   \n",
       "7                 Republic    Algiers   3.05097  ...       96  2015   \n",
       "9                 Republic     Luanda  13.24200  ...        4  2015   \n",
       "\n",
       "          income_group gdp_percapita  gross_savings  inflation_rate  \\\n",
       "1           Low income       615.091         21.466          -1.549   \n",
       "3          High income     44322.830         27.854           0.220   \n",
       "5  Upper middle income      3943.220         13.840           1.896   \n",
       "7  Upper middle income      4123.300         34.738           4.784   \n",
       "9  Upper middle income      3876.200         -0.425          10.287   \n",
       "\n",
       "   total_investment  unemployment_rate  exports  imports  \n",
       "1            18.602                  0  -49.110   -7.294  \n",
       "3            19.272                  7    4.957    5.749  \n",
       "5            24.598                 17    1.827    0.574  \n",
       "7            51.299                 11    5.733   -7.507  \n",
       "9             9.552                  0    6.721  -21.778  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# renaming the column so as to perform merge function\n",
    "df_eco2015.rename(columns = {'code':'country_code'}, inplace = True)\n",
    "df_eco2015.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "4cf498ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pop_id</th>\n",
       "      <th>country_code</th>\n",
       "      <th>year_x</th>\n",
       "      <th>fertility_rate</th>\n",
       "      <th>life_expectancy</th>\n",
       "      <th>size</th>\n",
       "      <th>country_name</th>\n",
       "      <th>continent</th>\n",
       "      <th>region</th>\n",
       "      <th>surface_area</th>\n",
       "      <th>...</th>\n",
       "      <th>econ_id</th>\n",
       "      <th>year_y</th>\n",
       "      <th>income_group</th>\n",
       "      <th>gdp_percapita</th>\n",
       "      <th>gross_savings</th>\n",
       "      <th>inflation_rate</th>\n",
       "      <th>total_investment</th>\n",
       "      <th>unemployment_rate</th>\n",
       "      <th>exports</th>\n",
       "      <th>imports</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>AFG</td>\n",
       "      <td>2015</td>\n",
       "      <td>4.653</td>\n",
       "      <td>60.717171</td>\n",
       "      <td>32526562</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Southern and Central Asia</td>\n",
       "      <td>652090</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>2015</td>\n",
       "      <td>Low income</td>\n",
       "      <td>615.091</td>\n",
       "      <td>21.466</td>\n",
       "      <td>-1.549</td>\n",
       "      <td>18.602</td>\n",
       "      <td>0</td>\n",
       "      <td>-49.110</td>\n",
       "      <td>-7.294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11</td>\n",
       "      <td>AGO</td>\n",
       "      <td>2015</td>\n",
       "      <td>5.996</td>\n",
       "      <td>52.666098</td>\n",
       "      <td>25021974</td>\n",
       "      <td>Angola</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Central Africa</td>\n",
       "      <td>1246700</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>2015</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>3876.200</td>\n",
       "      <td>-0.425</td>\n",
       "      <td>10.287</td>\n",
       "      <td>9.552</td>\n",
       "      <td>0</td>\n",
       "      <td>6.721</td>\n",
       "      <td>-21.778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>ALB</td>\n",
       "      <td>2015</td>\n",
       "      <td>1.793</td>\n",
       "      <td>78.014463</td>\n",
       "      <td>2889167</td>\n",
       "      <td>Albania</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Southern Europe</td>\n",
       "      <td>28748</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>2015</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>3943.220</td>\n",
       "      <td>13.840</td>\n",
       "      <td>1.896</td>\n",
       "      <td>24.598</td>\n",
       "      <td>17</td>\n",
       "      <td>1.827</td>\n",
       "      <td>0.574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>408</td>\n",
       "      <td>ARE</td>\n",
       "      <td>2015</td>\n",
       "      <td>1.767</td>\n",
       "      <td>77.541244</td>\n",
       "      <td>9156963</td>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Middle East</td>\n",
       "      <td>83600</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>2015</td>\n",
       "      <td>High income</td>\n",
       "      <td>38649.910</td>\n",
       "      <td>34.106</td>\n",
       "      <td>4.070</td>\n",
       "      <td>27.477</td>\n",
       "      <td>0</td>\n",
       "      <td>7.320</td>\n",
       "      <td>2.170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>15</td>\n",
       "      <td>ARG</td>\n",
       "      <td>2015</td>\n",
       "      <td>2.308</td>\n",
       "      <td>76.334220</td>\n",
       "      <td>43416755</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>South America</td>\n",
       "      <td>South America</td>\n",
       "      <td>2780400</td>\n",
       "      <td>...</td>\n",
       "      <td>10</td>\n",
       "      <td>2015</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>14643.920</td>\n",
       "      <td>14.111</td>\n",
       "      <td>0.000</td>\n",
       "      <td>16.890</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.658</td>\n",
       "      <td>3.105</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   pop_id country_code  year_x  fertility_rate  life_expectancy      size  \\\n",
       "0       1          AFG    2015           4.653        60.717171  32526562   \n",
       "1      11          AGO    2015           5.996        52.666098  25021974   \n",
       "2       3          ALB    2015           1.793        78.014463   2889167   \n",
       "3     408          ARE    2015           1.767        77.541244   9156963   \n",
       "4      15          ARG    2015           2.308        76.334220  43416755   \n",
       "\n",
       "           country_name      continent                     region  \\\n",
       "0           Afghanistan           Asia  Southern and Central Asia   \n",
       "1                Angola         Africa             Central Africa   \n",
       "2               Albania         Europe            Southern Europe   \n",
       "3  United Arab Emirates           Asia                Middle East   \n",
       "4             Argentina  South America              South America   \n",
       "\n",
       "   surface_area  ...  econ_id year_y         income_group gdp_percapita  \\\n",
       "0        652090  ...        2   2015           Low income       615.091   \n",
       "1       1246700  ...        4   2015  Upper middle income      3876.200   \n",
       "2         28748  ...        6   2015  Upper middle income      3943.220   \n",
       "3         83600  ...        8   2015          High income     38649.910   \n",
       "4       2780400  ...       10   2015  Upper middle income     14643.920   \n",
       "\n",
       "   gross_savings  inflation_rate  total_investment  unemployment_rate exports  \\\n",
       "0         21.466          -1.549            18.602                  0 -49.110   \n",
       "1         -0.425          10.287             9.552                  0   6.721   \n",
       "2         13.840           1.896            24.598                 17   1.827   \n",
       "3         34.106           4.070            27.477                  0   7.320   \n",
       "4         14.111           0.000            16.890                  0  -1.658   \n",
       "\n",
       "   imports  \n",
       "0   -7.294  \n",
       "1  -21.778  \n",
       "2    0.574  \n",
       "3    2.170  \n",
       "4    3.105  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# joining 'population2015' & 'df_eco2015' dataframe ON 'country_code'\n",
    "df_pop_eco2015 = pd.merge(population2015, df_eco2015, how = 'inner', on = 'country_code')\n",
    "df_pop_eco2015.head()"
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  },
  {
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    "# scatter plot of 'GDP and Life Expectancy'\n",
    "fig1 = px.scatter(df_pop_eco2015, x =\"gdp_percapita\", y = \"life_expectancy\", text = \"country_code\", log_x = True, size_max = 60)\n",
    "fig1.update_traces(textposition ='top center')\n",
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    "    height = 800,\n",
    "    title_text = 'GDP and Life Expectancy')\n",
    "fig1.show()"
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    "# scatter plot of 'GDP and Life Expectancy With Focus On Quality Of Life - Top 20 Countries'\n",
    "fig2 = px.scatter(df_pop_eco2015.sort_values(by ='life_expectancy', ascending = False).head(20), x =\"gdp_percapita\", y = \"life_expectancy\", text = \"country_code\", log_x = True, size_max = 60)\n",
    "fig2.update_traces(textposition ='top center')\n",
    "fig2.update_layout(\n",
    "    height = 800,\n",
    "    title_text = 'GDP and Life Expectancy With Focus On Quality Of Life - Top 20 Countries')\n",
    "fig2.show()"
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     },
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   ],
   "source": [
    "# scatter plot of 'GDP and Life Expectancy With Focus On The Level Of GDP - Top 10 Countries'\n",
    "fig3 = px.scatter(df_pop_eco2015.sort_values(by ='gdp_percapita', ascending = False).head(10), x =\"gdp_percapita\", y = \"life_expectancy\", text = \"country_code\", log_x = True, size_max = 60)\n",
    "fig3.update_traces(textposition ='top center')\n",
    "fig3.update_layout(\n",
    "    height = 800,\n",
    "    title_text = 'GDP and Life Expectancy With Focus On The Level Of GDP - Top 10 Countries')\n",
    "fig3.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "f530f0e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>code</th>\n",
       "      <th>year</th>\n",
       "      <th>income_group</th>\n",
       "      <th>gross_savings</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AGO</td>\n",
       "      <td>2019</td>\n",
       "      <td>Lower middle income</td>\n",
       "      <td>25.524848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ALB</td>\n",
       "      <td>2019</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>14.499826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ARG</td>\n",
       "      <td>2019</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>14.285295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ARM</td>\n",
       "      <td>2019</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>9.815574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ATG</td>\n",
       "      <td>2019</td>\n",
       "      <td>High income</td>\n",
       "      <td>26.383427</td>\n",
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       "  </tbody>\n",
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      ],
      "text/plain": [
       "  code  year         income_group  gross_savings\n",
       "0  AGO  2019  Lower middle income      25.524848\n",
       "1  ALB  2019  Upper middle income      14.499826\n",
       "2  ARG  2019  Upper middle income      14.285295\n",
       "3  ARM  2019  Upper middle income       9.815574\n",
       "4  ATG  2019          High income      26.383427"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# displaying the 2019 economy data\n",
    "df_economy2019.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "09896d9a",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>code</th>\n",
       "      <th>year</th>\n",
       "      <th>income_group</th>\n",
       "      <th>gross_savings</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20</td>\n",
       "      <td>BRN</td>\n",
       "      <td>2019</td>\n",
       "      <td>High income</td>\n",
       "      <td>53.553442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>84</td>\n",
       "      <td>MAC</td>\n",
       "      <td>2019</td>\n",
       "      <td>High income</td>\n",
       "      <td>47.790588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>117</td>\n",
       "      <td>QAT</td>\n",
       "      <td>2019</td>\n",
       "      <td>High income</td>\n",
       "      <td>46.150703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>123</td>\n",
       "      <td>SGP</td>\n",
       "      <td>2019</td>\n",
       "      <td>High income</td>\n",
       "      <td>40.317130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17</td>\n",
       "      <td>BMU</td>\n",
       "      <td>2019</td>\n",
       "      <td>High income</td>\n",
       "      <td>39.173462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>64</td>\n",
       "      <td>IRL</td>\n",
       "      <td>2019</td>\n",
       "      <td>High income</td>\n",
       "      <td>35.390303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>76</td>\n",
       "      <td>KOR</td>\n",
       "      <td>2019</td>\n",
       "      <td>High income</td>\n",
       "      <td>34.780382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>121</td>\n",
       "      <td>SAU</td>\n",
       "      <td>2019</td>\n",
       "      <td>High income</td>\n",
       "      <td>33.597081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>105</td>\n",
       "      <td>NOR</td>\n",
       "      <td>2019</td>\n",
       "      <td>High income</td>\n",
       "      <td>32.730519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>77</td>\n",
       "      <td>KWT</td>\n",
       "      <td>2019</td>\n",
       "      <td>High income</td>\n",
       "      <td>32.270222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>24</td>\n",
       "      <td>CHE</td>\n",
       "      <td>2019</td>\n",
       "      <td>High income</td>\n",
       "      <td>31.863023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>104</td>\n",
       "      <td>NLD</td>\n",
       "      <td>2019</td>\n",
       "      <td>High income</td>\n",
       "      <td>31.465902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>38</td>\n",
       "      <td>DNK</td>\n",
       "      <td>2019</td>\n",
       "      <td>High income</td>\n",
       "      <td>30.773589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>130</td>\n",
       "      <td>SWE</td>\n",
       "      <td>2019</td>\n",
       "      <td>High income</td>\n",
       "      <td>30.338386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>44</td>\n",
       "      <td>EST</td>\n",
       "      <td>2019</td>\n",
       "      <td>High income</td>\n",
       "      <td>29.931715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>36</td>\n",
       "      <td>DEU</td>\n",
       "      <td>2019</td>\n",
       "      <td>High income</td>\n",
       "      <td>29.636505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>71</td>\n",
       "      <td>JPN</td>\n",
       "      <td>2019</td>\n",
       "      <td>High income</td>\n",
       "      <td>29.344835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>92</td>\n",
       "      <td>MLT</td>\n",
       "      <td>2019</td>\n",
       "      <td>High income</td>\n",
       "      <td>28.304553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>6</td>\n",
       "      <td>AUT</td>\n",
       "      <td>2019</td>\n",
       "      <td>High income</td>\n",
       "      <td>27.757990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>61</td>\n",
       "      <td>HUN</td>\n",
       "      <td>2019</td>\n",
       "      <td>High income</td>\n",
       "      <td>27.633350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>35</td>\n",
       "      <td>CZE</td>\n",
       "      <td>2019</td>\n",
       "      <td>High income</td>\n",
       "      <td>26.958780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>129</td>\n",
       "      <td>SVN</td>\n",
       "      <td>2019</td>\n",
       "      <td>High income</td>\n",
       "      <td>26.701181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>4</td>\n",
       "      <td>ATG</td>\n",
       "      <td>2019</td>\n",
       "      <td>High income</td>\n",
       "      <td>26.383427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>8</td>\n",
       "      <td>BEL</td>\n",
       "      <td>2019</td>\n",
       "      <td>High income</td>\n",
       "      <td>25.092564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>67</td>\n",
       "      <td>ISR</td>\n",
       "      <td>2019</td>\n",
       "      <td>High income</td>\n",
       "      <td>24.920249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>59</td>\n",
       "      <td>HRV</td>\n",
       "      <td>2019</td>\n",
       "      <td>High income</td>\n",
       "      <td>24.392088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>57</td>\n",
       "      <td>HKG</td>\n",
       "      <td>2019</td>\n",
       "      <td>High income</td>\n",
       "      <td>24.039231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>66</td>\n",
       "      <td>ISL</td>\n",
       "      <td>2019</td>\n",
       "      <td>High income</td>\n",
       "      <td>24.038948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>46</td>\n",
       "      <td>FIN</td>\n",
       "      <td>2019</td>\n",
       "      <td>High income</td>\n",
       "      <td>23.864513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>48</td>\n",
       "      <td>FRA</td>\n",
       "      <td>2019</td>\n",
       "      <td>High income</td>\n",
       "      <td>23.658459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>43</td>\n",
       "      <td>ESP</td>\n",
       "      <td>2019</td>\n",
       "      <td>High income</td>\n",
       "      <td>22.992832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>83</td>\n",
       "      <td>LVA</td>\n",
       "      <td>2019</td>\n",
       "      <td>High income</td>\n",
       "      <td>22.586766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>5</td>\n",
       "      <td>AUS</td>\n",
       "      <td>2019</td>\n",
       "      <td>High income</td>\n",
       "      <td>22.528534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>108</td>\n",
       "      <td>OMN</td>\n",
       "      <td>2019</td>\n",
       "      <td>High income</td>\n",
       "      <td>22.026179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>68</td>\n",
       "      <td>ITA</td>\n",
       "      <td>2019</td>\n",
       "      <td>High income</td>\n",
       "      <td>21.385311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>107</td>\n",
       "      <td>NZL</td>\n",
       "      <td>2019</td>\n",
       "      <td>High income</td>\n",
       "      <td>21.057826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>81</td>\n",
       "      <td>LTU</td>\n",
       "      <td>2019</td>\n",
       "      <td>High income</td>\n",
       "      <td>21.045234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>128</td>\n",
       "      <td>SVK</td>\n",
       "      <td>2019</td>\n",
       "      <td>High income</td>\n",
       "      <td>21.030627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>113</td>\n",
       "      <td>POL</td>\n",
       "      <td>2019</td>\n",
       "      <td>High income</td>\n",
       "      <td>20.051044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>23</td>\n",
       "      <td>CAN</td>\n",
       "      <td>2019</td>\n",
       "      <td>High income</td>\n",
       "      <td>20.015422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>13</td>\n",
       "      <td>BHS</td>\n",
       "      <td>2019</td>\n",
       "      <td>High income</td>\n",
       "      <td>20.001819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>143</td>\n",
       "      <td>USA</td>\n",
       "      <td>2019</td>\n",
       "      <td>High income</td>\n",
       "      <td>19.494640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>25</td>\n",
       "      <td>CHL</td>\n",
       "      <td>2019</td>\n",
       "      <td>High income</td>\n",
       "      <td>19.206270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>114</td>\n",
       "      <td>PRT</td>\n",
       "      <td>2019</td>\n",
       "      <td>High income</td>\n",
       "      <td>18.392278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>142</td>\n",
       "      <td>URY</td>\n",
       "      <td>2019</td>\n",
       "      <td>High income</td>\n",
       "      <td>16.005674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>82</td>\n",
       "      <td>LUX</td>\n",
       "      <td>2019</td>\n",
       "      <td>High income</td>\n",
       "      <td>15.922005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>49</td>\n",
       "      <td>GBR</td>\n",
       "      <td>2019</td>\n",
       "      <td>High income</td>\n",
       "      <td>15.193879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>34</td>\n",
       "      <td>CYP</td>\n",
       "      <td>2019</td>\n",
       "      <td>High income</td>\n",
       "      <td>14.627575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>132</td>\n",
       "      <td>SYC</td>\n",
       "      <td>2019</td>\n",
       "      <td>High income</td>\n",
       "      <td>12.794453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>55</td>\n",
       "      <td>GRC</td>\n",
       "      <td>2019</td>\n",
       "      <td>High income</td>\n",
       "      <td>10.288100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    index code  year income_group  gross_savings\n",
       "0      20  BRN  2019  High income      53.553442\n",
       "1      84  MAC  2019  High income      47.790588\n",
       "2     117  QAT  2019  High income      46.150703\n",
       "3     123  SGP  2019  High income      40.317130\n",
       "4      17  BMU  2019  High income      39.173462\n",
       "5      64  IRL  2019  High income      35.390303\n",
       "6      76  KOR  2019  High income      34.780382\n",
       "7     121  SAU  2019  High income      33.597081\n",
       "8     105  NOR  2019  High income      32.730519\n",
       "9      77  KWT  2019  High income      32.270222\n",
       "10     24  CHE  2019  High income      31.863023\n",
       "11    104  NLD  2019  High income      31.465902\n",
       "12     38  DNK  2019  High income      30.773589\n",
       "13    130  SWE  2019  High income      30.338386\n",
       "14     44  EST  2019  High income      29.931715\n",
       "15     36  DEU  2019  High income      29.636505\n",
       "16     71  JPN  2019  High income      29.344835\n",
       "17     92  MLT  2019  High income      28.304553\n",
       "18      6  AUT  2019  High income      27.757990\n",
       "19     61  HUN  2019  High income      27.633350\n",
       "20     35  CZE  2019  High income      26.958780\n",
       "21    129  SVN  2019  High income      26.701181\n",
       "22      4  ATG  2019  High income      26.383427\n",
       "23      8  BEL  2019  High income      25.092564\n",
       "24     67  ISR  2019  High income      24.920249\n",
       "25     59  HRV  2019  High income      24.392088\n",
       "26     57  HKG  2019  High income      24.039231\n",
       "27     66  ISL  2019  High income      24.038948\n",
       "28     46  FIN  2019  High income      23.864513\n",
       "29     48  FRA  2019  High income      23.658459\n",
       "30     43  ESP  2019  High income      22.992832\n",
       "31     83  LVA  2019  High income      22.586766\n",
       "32      5  AUS  2019  High income      22.528534\n",
       "33    108  OMN  2019  High income      22.026179\n",
       "34     68  ITA  2019  High income      21.385311\n",
       "35    107  NZL  2019  High income      21.057826\n",
       "36     81  LTU  2019  High income      21.045234\n",
       "37    128  SVK  2019  High income      21.030627\n",
       "38    113  POL  2019  High income      20.051044\n",
       "39     23  CAN  2019  High income      20.015422\n",
       "40     13  BHS  2019  High income      20.001819\n",
       "41    143  USA  2019  High income      19.494640\n",
       "42     25  CHL  2019  High income      19.206270\n",
       "43    114  PRT  2019  High income      18.392278\n",
       "44    142  URY  2019  High income      16.005674\n",
       "45     82  LUX  2019  High income      15.922005\n",
       "46     49  GBR  2019  High income      15.193879\n",
       "47     34  CYP  2019  High income      14.627575\n",
       "48    132  SYC  2019  High income      12.794453\n",
       "49     55  GRC  2019  High income      10.288100"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# extracting only high income group\n",
    "high_inc = df_economy2019.loc[df_economy2019['income_group'] == 'High income']\n",
    "high_inc.sort_values(by = 'gross_savings', ascending = False).reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "71d6a28e",
   "metadata": {},
   "outputs": [
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       "      <th>index</th>\n",
       "      <th>Counts</th>\n",
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       "                 index  Counts\n",
       "0          High income      50\n",
       "1  Lower middle income      45\n",
       "2  Upper middle income      41\n",
       "3           Low income      15"
      ]
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     "execution_count": 124,
     "metadata": {},
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   "source": [
    "# getting the counts of various income groups\n",
    "inc_distribution = df_economy2019[['code', 'income_group']]\n",
    "inc_distribution['income_group'].value_counts().reset_index(name = 'Counts')"
   ]
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         "text": "% Of Income Group Distribution"
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   ],
   "source": [
    "# pie plot of \"Percentage of Income Group Distribution\"\n",
    "inc = ['High income', 'Lower middle income', 'Upper middle income', 'Low income']\n",
    "values = [50, 45, 41, 15]\n",
    "fig4 = go.Figure()\n",
    "fig4.add_trace(go.Pie(labels = inc, values = values, hole = 0.3, pull = [0.2, 0, 0, 0]))\n",
    "fig4.update_layout(title = '% Of Income Group Distribution')\n",
    "fig4.show()"
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