Final Project Notebook - khushboo.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib notebook\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "# Import libraries\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import requests\n",
    "from pprint import pprint\n",
    "#import gmaps\n",
    "#from config import gkey\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "# URL\n",
    "renew_energy_url = \"https://api.worldbank.org/v2/en/country/all/indicator/EG.FEC.RNEW.ZS?format=json&per_page=20000&source=2\"\n",
    "cntry_url = 'https://restcountries.eu/rest/v2/all'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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>Country</th>\n",
       "      <th>Year</th>\n",
       "      <th>Code</th>\n",
       "      <th>Percent Renewable</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2776</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2015</td>\n",
       "      <td>AFG</td>\n",
       "      <td>18.423477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2777</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2014</td>\n",
       "      <td>AFG</td>\n",
       "      <td>19.314269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2778</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2013</td>\n",
       "      <td>AFG</td>\n",
       "      <td>16.334293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2779</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2012</td>\n",
       "      <td>AFG</td>\n",
       "      <td>13.973586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2780</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2011</td>\n",
       "      <td>AFG</td>\n",
       "      <td>11.482706</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Country  Year Code  Percent Renewable\n",
       "2776  Afghanistan  2015  AFG          18.423477\n",
       "2777  Afghanistan  2014  AFG          19.314269\n",
       "2778  Afghanistan  2013  AFG          16.334293\n",
       "2779  Afghanistan  2012  AFG          13.973586\n",
       "2780  Afghanistan  2011  AFG          11.482706"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get Renewable Energy Consumption Data\n",
    "renew_energy_response = requests.get(renew_energy_url).json()\n",
    "reo = renew_energy_response[1]\n",
    "\n",
    "# building percent of renewable energy dataframe \n",
    "#starting with country\n",
    "country = [reo[i]['country']['value'] for i in range(len(reo))]\n",
    "renew_energy_df = pd.DataFrame({'Country':country})\n",
    "\n",
    "# adding year column to dataframe\n",
    "year= [reo[i]['date'] for i in range(len(reo))]\n",
    "renew_energy_df[\"Year\"]= year\n",
    "\n",
    "# adding alphacode column to dataframe\n",
    "a_code = [reo[i]['countryiso3code'] for i in range(len(reo))]\n",
    "renew_energy_df[\"alpha3code\"]= a_code\n",
    "\n",
    "# adding Percent Renewable value to dataframe\n",
    "percent_renewable = [reo[i]['value'] for i in range(len(reo))]\n",
    "renew_energy_df[\"Percent Renewable\"]= percent_renewable\n",
    "\n",
    "# dropping rows with blank alphacodes - maybe this will do the trick to remove all \"non-countries\"\n",
    "renew_energy_df = renew_energy_df[renew_energy_df.alpha3code != \"\"]\n",
    "\n",
    "# reomving all nan values for percent renewable\n",
    "renew_energy_df = renew_energy_df[np.isfinite(renew_energy_df['Percent Renewable'])]\n",
    "\n",
    "# Changing Year column dtype to float\n",
    "\n",
    "renew_energy_df[\"Year\"] = pd.to_numeric(renew_energy_df[\"Year\"])\n",
    "\n",
    "# limiting years to 2000-2015\n",
    "\n",
    "renew_energy_df = renew_energy_df.drop(renew_energy_df[(renew_energy_df.Year < 2000) | (renew_energy_df.Year > 2015)].index)\n",
    "#renew_energy_df = renew_energy_df.drop(columns=[\"alpha3code\"])\n",
    "\n",
    "renew_energy_df.columns=[\"Country\", \"Year\", \"Code\", \"Percent Renewable\"]\n",
    "renew_energy_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data not found for United States Minor Outlying Islands\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Code</th>\n",
       "      <th>Population</th>\n",
       "      <th>Area</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Lng</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>27657145</td>\n",
       "      <td>652230.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>27657145</td>\n",
       "      <td>652230.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>2001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>27657145</td>\n",
       "      <td>652230.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>2002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>27657145</td>\n",
       "      <td>652230.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>2003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>27657145</td>\n",
       "      <td>652230.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Country Code  Population      Area   Lat   Lng  Year\n",
       "0  Afghanistan  AFG    27657145  652230.0  33.0  65.0  2000\n",
       "1  Afghanistan  AFG    27657145  652230.0  33.0  65.0  2001\n",
       "2  Afghanistan  AFG    27657145  652230.0  33.0  65.0  2002\n",
       "3  Afghanistan  AFG    27657145  652230.0  33.0  65.0  2003\n",
       "4  Afghanistan  AFG    27657145  652230.0  33.0  65.0  2004"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get Countries, Lat, Long Data\n",
    "cntry_response = requests.get(cntry_url).json()\n",
    "\n",
    "# Process each country and store details in a list\n",
    "cntry_ls = []\n",
    "for index, each in enumerate(cntry_response):\n",
    "    try:\n",
    "        c_name = each['name']\n",
    "        c_code = each['alpha3Code']\n",
    "        c_lat = each['latlng'][0]\n",
    "        c_lng = each['latlng'][1]\n",
    "        c_pop = each['population']\n",
    "        c_area = each['area']\n",
    "        c_details = [c_name, c_code, c_pop, c_area, c_lat, c_lng]\n",
    "        cntry_ls.append(c_details)\n",
    "    except:\n",
    "        print(f\"Data not found for {c_name}\")\n",
    "\n",
    "# Convert list to dataframe        \n",
    "cntry_df = pd.DataFrame(cntry_ls, columns=[\"Country\", \"Code\", \"Population\", \"Area\", \"Lat\", \"Lng\"])\n",
    "\n",
    "# Convert data to numeric\n",
    "cntry_df[\"Population\"] = pd.to_numeric(cntry_df[\"Population\"])\n",
    "cntry_df[\"Area\"] = pd.to_numeric(cntry_df[\"Area\"])\n",
    "cntry_df[\"Lat\"] = pd.to_numeric(cntry_df[\"Lat\"])\n",
    "cntry_df[\"Lng\"] = pd.to_numeric(cntry_df[\"Lng\"])\n",
    "\n",
    "# Cross Join year\n",
    "years = []\n",
    "for i in range(2000, 2016, 1):\n",
    "    years.append(i)\n",
    "years_df = pd.DataFrame(years, columns=[\"Year\"])\n",
    "years_df\n",
    "\n",
    "cntry_df['dummykey'] = 0\n",
    "years_df['dummykey'] = 0\n",
    "\n",
    "countries_df = cntry_df.merge(years_df, how='outer')\n",
    "\n",
    "countries_df = countries_df.drop(columns=[\"dummykey\"])\n",
    "countries_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Code</th>\n",
       "      <th>Year</th>\n",
       "      <th>Emission</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Aruba</td>\n",
       "      <td>ABW</td>\n",
       "      <td>2000</td>\n",
       "      <td>26.194875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>2000</td>\n",
       "      <td>0.038506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Angola</td>\n",
       "      <td>AGO</td>\n",
       "      <td>2000</td>\n",
       "      <td>0.580353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Albania</td>\n",
       "      <td>ALB</td>\n",
       "      <td>2000</td>\n",
       "      <td>0.978175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Andorra</td>\n",
       "      <td>AND</td>\n",
       "      <td>2000</td>\n",
       "      <td>8.019284</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Country Code  Year   Emission\n",
       "0        Aruba  ABW  2000  26.194875\n",
       "1  Afghanistan  AFG  2000   0.038506\n",
       "2       Angola  AGO  2000   0.580353\n",
       "3      Albania  ALB  2000   0.978175\n",
       "4      Andorra  AND  2000   8.019284"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get Emission data\n",
    "emm_df = pd.read_csv(\"EmissionData.csv\", skiprows=4)\n",
    "\n",
    "# Unpivot: Change year columns to rows\n",
    "emm_df = pd.melt(emm_df, id_vars=['Country Name','Country Code'], value_vars=['2000', '2001', '2002', '2003', '2004', '2005',\n",
    "                                                       '2006', '2007', '2008', '2009', '2010', '2011',\n",
    "                                                       '2012', '2013', '2014', '2015'])\n",
    "emm_df.columns=[\"Country\", \"Code\", \"Year\", \"Emission\"]\n",
    "emm_df = emm_df.dropna()\n",
    "emm_df[\"Year\"] = pd.to_numeric(emm_df[\"Year\"])\n",
    "emm_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "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>Country</th>\n",
       "      <th>Code</th>\n",
       "      <th>Year</th>\n",
       "      <th>College Enrolment ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>2003</td>\n",
       "      <td>1.25684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>2004</td>\n",
       "      <td>1.25603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>2009</td>\n",
       "      <td>3.90339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>2011</td>\n",
       "      <td>3.75598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>2014</td>\n",
       "      <td>8.66280</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Country Code  Year  College Enrolment ratio\n",
       "14  Afghanistan  AFG  2003                  1.25684\n",
       "15  Afghanistan  AFG  2004                  1.25603\n",
       "16  Afghanistan  AFG  2009                  3.90339\n",
       "17  Afghanistan  AFG  2011                  3.75598\n",
       "18  Afghanistan  AFG  2014                  8.66280"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get Education Data\n",
    "Ed_df = pd.read_csv(\"college_ed.csv\")\n",
    "\n",
    "Ed_df.groupby(['Country']).head()\n",
    "\n",
    "Ed_df = Ed_df.drop(Ed_df[(Ed_df.Year < 2000) | (Ed_df.Year > 2015)].index)\n",
    "Ed_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\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>Country</th>\n",
       "      <th>Code</th>\n",
       "      <th>Year</th>\n",
       "      <th>GDP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Aruba</td>\n",
       "      <td>ABW</td>\n",
       "      <td>2000</td>\n",
       "      <td>1.873453e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Angola</td>\n",
       "      <td>AGO</td>\n",
       "      <td>2000</td>\n",
       "      <td>9.129595e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Albania</td>\n",
       "      <td>ALB</td>\n",
       "      <td>2000</td>\n",
       "      <td>3.480355e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Andorra</td>\n",
       "      <td>AND</td>\n",
       "      <td>2000</td>\n",
       "      <td>1.434430e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Arab World</td>\n",
       "      <td>ARB</td>\n",
       "      <td>2000</td>\n",
       "      <td>7.351881e+11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Country Code  Year           GDP\n",
       "0       Aruba  ABW  2000  1.873453e+09\n",
       "2      Angola  AGO  2000  9.129595e+09\n",
       "3     Albania  ALB  2000  3.480355e+09\n",
       "4     Andorra  AND  2000  1.434430e+09\n",
       "5  Arab World  ARB  2000  7.351881e+11"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get GDP Data\n",
    "gdp_file = 'gdp.csv'\n",
    "gdp_df = pd.read_csv(gdp_file)\n",
    "\n",
    "gdp_df.drop(['1960', '1961','1962','1963','1964',\n",
    "         '1965', '1966','1967', '1968','1969',\n",
    "         '1970','1971','1972', '1973','1974',\n",
    "         '1975','1976','1977','1978','1979',\n",
    "         '1980','1981','1982','1983','1984',\n",
    "         '1985','1986', '1987','1988','1989',\n",
    "         '1990', '1991','1992','1993','1994',\n",
    "         '1995', '1996','1997','1998','1999',\n",
    "         '2016','2017','2018'], axis=1, inplace=True)\n",
    "\n",
    "del gdp_df['Indicator Code']\n",
    "del gdp_df['Indicator Name']\n",
    "\n",
    "gdp_df =pd.melt(gdp_df, id_vars=['Country Name','Country Code'],value_vars=['2000', '2001', '2002', '2003', '2004', '2005',\n",
    "                                                                       '2006', '2007', '2008', '2009', '2010', '2011',\n",
    "                                                                       '2012', '2013', '2014', '2015'])\n",
    "\n",
    "gdp_df.drop( gdp_df[gdp_df ['value'] == 0.000000 ].index , inplace=True)\n",
    "\n",
    "gdp_df = gdp_df.rename({'Country Name': 'Country'}, axis=1)\n",
    "gdp_df = gdp_df.rename({'Country Code': 'Code'}, axis=1)\n",
    "gdp_df = gdp_df.rename({'value': 'GDP'}, axis=1)\n",
    "gdp_df = gdp_df.rename({'variable': 'Year'}, axis=1)\n",
    "gdp_df = gdp_df.dropna(how='any')\n",
    "gdp_df[\"Year\"] = pd.to_numeric(gdp_df[\"Year\"])\n",
    "gdp_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Code</th>\n",
       "      <th>Year</th>\n",
       "      <th>Renew Kwh</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Angola</td>\n",
       "      <td>AGO</td>\n",
       "      <td>2000</td>\n",
       "      <td>1824000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Albania</td>\n",
       "      <td>ALB</td>\n",
       "      <td>2000</td>\n",
       "      <td>9188000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Arab League states</td>\n",
       "      <td>ARB</td>\n",
       "      <td>2000</td>\n",
       "      <td>40546000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>ARG</td>\n",
       "      <td>2000</td>\n",
       "      <td>58968000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Armenia</td>\n",
       "      <td>ARM</td>\n",
       "      <td>2000</td>\n",
       "      <td>2522000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Country Code  Year   Renew Kwh\n",
       "2              Angola  AGO  2000   1824000.0\n",
       "3             Albania  ALB  2000   9188000.0\n",
       "6  Arab League states  ARB  2000  40546000.0\n",
       "8           Argentina  ARG  2000  58968000.0\n",
       "9             Armenia  ARM  2000   2522000.0"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get Renew Energy Kwh data\n",
    "renew_kwh_df = pd.read_csv(\"RenewKwh.csv\", skiprows=4)\n",
    "\n",
    "# Unpivot: Change year columns to rows\n",
    "renew_kwh_df = pd.melt(renew_kwh_df, id_vars=['Country Name','Country Code'], value_vars=['2000', '2001', '2002', '2003', '2004', '2005',\n",
    "                                                       '2006', '2007', '2008', '2009', '2010', '2011',\n",
    "                                                       '2012', '2013', '2014', '2015'])\n",
    "renew_kwh_df.columns=[\"Country\", \"Code\", \"Year\", \"Renew Kwh\"]\n",
    "renew_kwh_df = renew_kwh_df.dropna()\n",
    "renew_kwh_df[\"Year\"] = pd.to_numeric(renew_kwh_df[\"Year\"])\n",
    "renew_kwh_df[\"Renew Kwh\"]=renew_kwh_df[\"Renew Kwh\"]/10000\n",
    "renew_kwh_df.drop(renew_kwh_df[renew_kwh_df ['Renew Kwh'] == 0.0 ].index , inplace=True)\n",
    "renew_kwh_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Code</th>\n",
       "      <th>Population</th>\n",
       "      <th>Area</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Lng</th>\n",
       "      <th>Year</th>\n",
       "      <th>Percent Renewable</th>\n",
       "      <th>Emission</th>\n",
       "      <th>College Enrolment ratio</th>\n",
       "      <th>GDP</th>\n",
       "      <th>Renew Kwh</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>27657145</td>\n",
       "      <td>652230.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>2000</td>\n",
       "      <td>54.243126</td>\n",
       "      <td>0.038506</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>27657145</td>\n",
       "      <td>652230.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>2001</td>\n",
       "      <td>54.055055</td>\n",
       "      <td>0.039002</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>27657145</td>\n",
       "      <td>652230.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>2002</td>\n",
       "      <td>43.771149</td>\n",
       "      <td>0.048716</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4055.179566</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>27657145</td>\n",
       "      <td>652230.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>2003</td>\n",
       "      <td>42.276141</td>\n",
       "      <td>0.051830</td>\n",
       "      <td>1.25684</td>\n",
       "      <td>4515.558808</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>27657145</td>\n",
       "      <td>652230.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>2004</td>\n",
       "      <td>49.843148</td>\n",
       "      <td>0.039378</td>\n",
       "      <td>1.25603</td>\n",
       "      <td>5226.778809</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Country Code  Population      Area   Lat   Lng  Year  \\\n",
       "0  Afghanistan  AFG    27657145  652230.0  33.0  65.0  2000   \n",
       "1  Afghanistan  AFG    27657145  652230.0  33.0  65.0  2001   \n",
       "2  Afghanistan  AFG    27657145  652230.0  33.0  65.0  2002   \n",
       "3  Afghanistan  AFG    27657145  652230.0  33.0  65.0  2003   \n",
       "4  Afghanistan  AFG    27657145  652230.0  33.0  65.0  2004   \n",
       "\n",
       "   Percent Renewable  Emission  College Enrolment ratio          GDP  \\\n",
       "0          54.243126  0.038506                      NaN          NaN   \n",
       "1          54.055055  0.039002                      NaN          NaN   \n",
       "2          43.771149  0.048716                      NaN  4055.179566   \n",
       "3          42.276141  0.051830                  1.25684  4515.558808   \n",
       "4          49.843148  0.039378                  1.25603  5226.778809   \n",
       "\n",
       "   Renew Kwh  \n",
       "0        NaN  \n",
       "1        NaN  \n",
       "2        NaN  \n",
       "3        NaN  \n",
       "4        NaN  "
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create results with all data combined\n",
    "result1 = countries_df.merge(renew_energy_df, how='left', on=['Code','Year'])\n",
    "result1 = result1.drop(columns=[\"Country_y\"])\n",
    "result1 = result1.rename({'Country_x': 'Country'}, axis=1)\n",
    "\n",
    "result2 = result1.merge(emm_df, how='left', on=['Code','Year'])\n",
    "result2 = result2.drop(columns=[\"Country_y\"])\n",
    "result2 = result2.rename({'Country_x': 'Country'}, axis=1)\n",
    "\n",
    "result3 = result2.merge(Ed_df, how='left', on=['Code','Year'])\n",
    "result3 = result3.drop(columns=[\"Country_y\"])\n",
    "result3 = result3.rename({'Country_x': 'Country'}, axis=1)\n",
    "\n",
    "result4 = result3.merge(gdp_df, how='left', on=['Code','Year'])\n",
    "result4 = result4.drop(columns=[\"Country_y\"])\n",
    "result4 = result4.rename({'Country_x': 'Country'}, axis=1)\n",
    "\n",
    "result = result4.merge(renew_kwh_df, how='left', on=['Code','Year'])\n",
    "result = result.drop(columns=[\"Country_y\"])\n",
    "result = result.rename({'Country_x': 'Country'}, axis=1)\n",
    "\n",
    "result['GDP'] = result['GDP']/1000000\n",
    "\n",
    "# Drop rows if Percent Renewable is NA\n",
    "result = result.dropna(subset=['Percent Renewable'])\n",
    "result.head()\n",
    "\n",
    "#result.to_csv(\"final_result.csv\")\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\agarw\\Anaconda3\\envs\\pythondata\\lib\\site-packages\\ipykernel_launcher.py:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#create a dataframe for a single year(2014)\n",
    "result14 = result.loc[result['Year'] == 2014]\n",
    "result14\n",
    "#create a scatter plot to show relationship between Percent Renewable and GDP\n",
    "fig1 = result14.plot(kind=\"scatter\", x=\"GDP\", y=\"Percent Renewable\", grid=True, figsize=(8,8),\n",
    "              title=\"Percent Renewable vs GDP for year 2014\",color=\"blue\", edgecolors=\"grey\", alpha=0.75,s = 40)\n",
    "              \n",
    "fig1.set_xscale('log')\n",
    "plt.ylabel(\"Percent Renewable(%)\")\n",
    "plt.xlabel(\"GDP(Million Dollars)\")\n",
    "plt.style.use('seaborn')\n",
    "\n",
    "\n",
    "#find the top 10 gdp countries\n",
    "result2_gdp = result14.sort_values(by=['GDP'], ascending = False)\n",
    "top_10_gdp = result2_gdp.head(10)\n",
    "\n",
    "#find the top 5 renewable resource countries\n",
    "result3_gdp = result14.sort_values(by=['Percent Renewable'], ascending = False)\n",
    "top_5_ren = result3_gdp.head(5)\n",
    "\n",
    "#find the bottom 5 gdp countries\n",
    "result4_gdp = result14.sort_values(by=['GDP'], ascending = True)\n",
    "bottom_5_GDP = result4_gdp.head(5)\n",
    "\n",
    "#find the bottom 5 renewable countries\n",
    "result5_gdp = result14.sort_values(by=['Percent Renewable'], ascending = True)\n",
    "bottom_5_ren = result5_gdp.head(5)\n",
    "\n",
    "#rename a cloumn in the table top_10_gdp\n",
    "top_10_gdp['Country']=top_10_gdp['Country'].replace(['United Kingdom of Great Britain and Northern Ireland'],'UK')\n",
    "\n",
    "#run for loops on the above dataframes to label the points in the graph\n",
    "for index,row in top_10_gdp.iterrows():\n",
    "    fig1.annotate(row[\"Country\"], xy=(row[\"GDP\"],row[\"Percent Renewable\"]))\n",
    "\n",
    "for index,row in top_5_ren.iterrows():\n",
    "    fig1.annotate(row[\"Country\"], xy=(row[\"GDP\"],row[\"Percent Renewable\"]))\n",
    "\n",
    "for index,row in bottom_5_GDP.iterrows():\n",
    "    fig1.annotate(row[\"Country\"], xy=(row[\"GDP\"],row[\"Percent Renewable\"]))\n",
    "    \n",
    "for index,row in bottom_5_ren.iterrows():\n",
    "    fig1.annotate(row[\"Country\"], xy=(row[\"GDP\"],row[\"Percent Renewable\"]))\n",
    "\n",
    "    \n",
    "plt.savefig(\"images/gdp_final.png\")    \n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "#Remove null values from Renew column[renew column is in Mwh]\n",
    "result_na1 =result[pd.notnull(result['Renew Kwh'])]\n",
    "result_na1\n",
    "#Remove null values from Emission column\n",
    "result_na1[pd.notnull(result_na1['Emission'])]\n",
    "\n",
    "#group by year\n",
    "result_grouped_year =result_na1.groupby(\"Year\")\n",
    "result_grouped_year.count().head()\n",
    "#calculate average value of renewable\n",
    "average_renewable = result_grouped_year[\"Renew Kwh\"].mean()\n",
    "average_renewable\n",
    "#calculate average value of emission\n",
    "average_emission = result_grouped_year[\"Emission\"].mean()\n",
    "average_emission \n",
    "\n",
    "#create a dataframe to store the above data\n",
    "new_df = pd.DataFrame({\"Average Emission\":average_emission,\n",
    "                      \"Average Renewable\":average_renewable})\n",
    "new_df.dropna()\n",
    "\n",
    "new_df.to_csv(\"new_df_average.csv\")\n",
    "#Read the csv file \n",
    "new_df_average = pd.read_csv(\"new_df_average.csv\")\n",
    "new_df_average\n",
    "\n",
    "#load the csv file for oil prices\n",
    "oil_data = pd.read_csv(\"crude-oil-prices - Copy.csv\")\n",
    "oil_data\n",
    "\n",
    "#create a new df for required years for oil data\n",
    "options = ['2000','2001','2002','2003','2004','2005','2006','2007','2008','2009','2010','2011','2012']\n",
    "result_combined_oil = oil_data.loc[oil_data['Year'].isin(options)] \n",
    "result_combined_oil\n",
    "\n",
    "result_oil_final = result_combined_oil.head(13)\n",
    "result_oil_final\n",
    "\n",
    "#merge the two files \n",
    "merged_1_oil= pd.merge(new_df_average,result_oil_final,on='Year')\n",
    "merged_1_oil\n",
    "\n",
    "#plot graph with two y axis\n",
    "fig, ax1 = plt.subplots()\n",
    "plot1 = ax1.plot(merged_1_oil[\"Year\"],merged_1_oil[\"Average Renewable\"], color=\"green\",marker=\".\",label = \"Average Renewable\")\n",
    "ax1.set_title(\"Average Renewable vs year\")\n",
    "ax1.set_xlabel(\"Year\")\n",
    "ax1.set_ylabel(\"Average Renewable in MWH\")\n",
    "ax1.grid()\n",
    "\n",
    "\n",
    "ax2 = ax1.twinx()\n",
    "ax2.set_ylabel(\"Crude Oil Price(us dollar per barrel)\")\n",
    "ax2.set_label(\"Crude oil Prices\")\n",
    "plot2 = ax2.plot(merged_1[\"Year\"],merged_1[\"Crude oil prices (US dollars per barrel)\"], color=\"red\", marker=\".\",label = \"Oil prices\")\n",
    "\n",
    "#create legend for the plot\n",
    "lns = plot1+plot2\n",
    "labs = [l.get_label() for l in lns]\n",
    "ax1.legend(lns, labs, loc=0)\n",
    "\n",
    "#save as png file\n",
    "plt.savefig(\"images/twinplot.png\")\n",
    "\n",
    "plt.show()\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\agarw\\Anaconda3\\envs\\pythondata\\lib\\site-packages\\ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n",
      "C:\\Users\\agarw\\Anaconda3\\envs\\pythondata\\lib\\site-packages\\ipykernel_launcher.py:29: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "C:\\Users\\agarw\\Anaconda3\\envs\\pythondata\\lib\\site-packages\\ipykernel_launcher.py:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "C:\\Users\\agarw\\Anaconda3\\envs\\pythondata\\lib\\site-packages\\ipykernel_launcher.py:33: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#calculate gdp per capita\n",
    "result14['per capita GDP'] = result14['GDP']*1000000/result14['Population']\n",
    "result14\n",
    "\n",
    "#plot percent renewable vs per capita GDP\n",
    "fig2 = result14.plot(kind=\"scatter\", x=\"per capita GDP\", y=\"Percent Renewable\", grid=True, figsize=(8,8),\n",
    "              title=\"Percent Renewable vs per capita GDP\",s=100)\n",
    "\n",
    "plt.style.use('seaborn')\n",
    "fig2.set_xscale('log')\n",
    "\n",
    "#calculate the top 5 renewable resource country\n",
    "result2_percapita = result14.sort_values(by=['Percent Renewable'], ascending = False)\n",
    "top_5_ren = result2_percapita.head(5)\n",
    "#calculate the top per capita gdp countries\n",
    "result3_percapita = result14.sort_values(by=['per capita GDP'], ascending = False)\n",
    "top_5_percapita = result3_percapita.head(5)\n",
    "#calculate the bottom per capita gdp\n",
    "result4_percapita = result14.sort_values(by=['per capita GDP'], ascending = True)\n",
    "bottom_5_percapita = result4_percapita.head(5)\n",
    "#calculate bottom percent renewable countries\n",
    "result5_percapita = result14.sort_values(by=['Percent Renewable'], ascending = True)\n",
    "bottom_5_ren = result5_percapita.head(5)\n",
    "#calculate the top 10 gdp for the dataframe \n",
    "result6_pc = result14.sort_values(by=['GDP'], ascending = False)\n",
    "top_10_gdp_withpc = result6_pc.head(10)\n",
    "\n",
    "#rename  column \n",
    "bottom_5_ren['Country']=bottom_5_ren['Country'].replace(['Northern Mariana Islands'],'Mariana is')\n",
    "\n",
    "top_5_ren['Country']=top_5_ren['Country'].replace(['Congo (Democratic Republic of the)'],'Congo')\n",
    "\n",
    "bottom_5_percapita['Country']=bottom_5_percapita['Country'].replace(['Congo (Democratic Republic of the)'],'Congo')\n",
    "\n",
    "#run loop to label the points\n",
    "for index,row in top_5_percapita.iterrows():\n",
    "    fig2.annotate(row[\"Country\"], xy=(row[\"per capita GDP\"],row[\"Percent Renewable\"]),color=\"gold\")\n",
    "    \n",
    "for index,row in bottom_5_percapita.iterrows():\n",
    "    fig2.annotate(row[\"Country\"], xy=(row[\"per capita GDP\"],row[\"Percent Renewable\"]),color=\"red\")\n",
    "    \n",
    "for index,row in bottom_5_ren.iterrows():\n",
    "    fig2.annotate(row[\"Country\"], xy=(row[\"per capita GDP\"],row[\"Percent Renewable\"]),color=\"black\")\n",
    "    \n",
    "for index,row in top_5_ren.iterrows():\n",
    "    fig2.annotate(row[\"Country\"], xy=(row[\"per capita GDP\"],row[\"Percent Renewable\"]),color=\"green\")    \n",
    "    \n",
    "plt.xlabel(\"GDP per capita(Million Dollars)\")\n",
    "plt.ylabel(\"Percent Renewable(%)\")\n",
    "plt.title(\"Percent Renewable vs per capita GDP\") \n",
    "\n",
    "\n",
    "plt.savefig(\"images/percapita_gdp.png\")    \n",
    "plt.show()\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\agarw\\Anaconda3\\envs\\pythondata\\lib\\site-packages\\ipykernel_launcher.py:28: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#create a bubble plot where size=population\n",
    "plt.xscale('log')\n",
    "plt.xlim(200,200000)\n",
    "plt.scatter(x=top_5_percapita[\"per capita GDP\"], y=top_5_percapita[\"Percent Renewable\"],s= top_5_percapita[\"Population\"]/100000,c=\"yellow\", \n",
    "      edgecolor='black', linewidth=1, alpha=0.5,label =\"Top per capita GDP\")\n",
    "plt.scatter(x=bottom_5_percapita[\"per capita GDP\"], y=bottom_5_percapita[\"Percent Renewable\"],s= bottom_5_percapita[\"Population\"]/100000,c=\"red\", \n",
    "      edgecolor='black', linewidth=1, alpha=0.5,label =\"Low per capita GDP\")\n",
    "plt.scatter(x=bottom_5_ren[\"per capita GDP\"], y=bottom_5_ren[\"Percent Renewable\"],s= bottom_5_ren[\"Population\"]/100000,c=\"black\", \n",
    "      edgecolor='black', linewidth=1, alpha=1,label =\"Lowest Renewable\")\n",
    "plt.scatter(x=top_5_ren[\"per capita GDP\"], y=top_5_ren[\"Percent Renewable\"],s= top_5_ren[\"Population\"]/100000,c=\"green\", \n",
    "      edgecolor='black', linewidth=1, alpha=1,label =\"Top Renewable\")\n",
    "plt.scatter(x=top_10_gdp_withpc[\"per capita GDP\"], y=top_10_gdp_withpc[\"Percent Renewable\"],s= top_10_gdp_withpc[\"Population\"]/100000,c=\"blue\", \n",
    "      edgecolor='black', linewidth=1, alpha=0.5,label =\"Top GDP\")\n",
    "\n",
    "\n",
    "#run loop for creating labels of the points\n",
    "for index,row in top_5_percapita.iterrows():\n",
    "    plt.annotate(row[\"Country\"], xy=(row[\"per capita GDP\"],row[\"Percent Renewable\"]),color=\"gold\")\n",
    "for index,row in bottom_5_percapita.iterrows():\n",
    "    plt.annotate(row[\"Country\"], xy=(row[\"per capita GDP\"],row[\"Percent Renewable\"]),color=\"red\")\n",
    "for index,row in bottom_5_ren.iterrows():\n",
    "    plt.annotate(row[\"Country\"], xy=(row[\"per capita GDP\"],row[\"Percent Renewable\"]),color=\"black\")\n",
    "for index,row in top_5_ren.iterrows():\n",
    "    plt.annotate(row[\"Country\"], xy=(row[\"per capita GDP\"],row[\"Percent Renewable\"]),color=\"green\")\n",
    "for index,row in top_10_gdp_withpc.iterrows():\n",
    "    plt.annotate(row[\"Country\"], xy=(row[\"per capita GDP\"],row[\"Percent Renewable\"]),color=\"blue\")    \n",
    "#rename a column \n",
    "top_10_gdp_withpc['Country']=top_10_gdp_withpc['Country'].replace(['United Kingdom of Great Britain and Northern Ireland'],'UK')\n",
    "\n",
    "\n",
    "#create a legend \n",
    "my_legend = plt.legend(loc='upper right', title='Category for Country', \n",
    "                       scatterpoints=1)\n",
    "\n",
    "for handle in my_legend.legendHandles:\n",
    "    handle.set_sizes([30])\n",
    "\n",
    "plt.xlabel(\"per capital GDP(Million Dollars)\")\n",
    "plt.ylabel(\"Percent Renewable(%)\")\n",
    "plt.title(\"Trend by Country Category\") \n",
    "plt.suptitle(\"Note:circle size correlates with the population\")\n",
    "   \n",
    "plt.style.use('seaborn')\n",
    "plt.savefig(\"images/bubble.png\")    \n",
    "plt.show()\n",
    "\n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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