Project Code - Analysis of factors that influence Food Insecurity.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "7f420b94",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# <center> PPOL564 | Data Science 1: Foundations </center>\n",
    "### <center> Final Project </center>\n",
    "### <center> Analysis of factors that influence Food Insecurity </center> <br>\n",
    "\n",
    "\n",
    "\n",
    "#### By      : Sonali Subbu Rathinam \n",
    "#### NetID : ss4608\n",
    "#### Date   : December 16th, 2021"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "id": "133cd5e7",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "#Importing the required modules \n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "#For Web-scraping \n",
    "import requests\n",
    "from bs4 import BeautifulSoup\n",
    "import wbdata #Importing world bank data\n",
    "\n",
    "#For standardizing country names\n",
    "import country_converter as cc\n",
    "\n",
    "#For Imputing missing data\n",
    "from sklearn.impute import KNNImputer\n",
    "\n",
    "\n",
    "# Plotting libraries\n",
    "from plotnine import *\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "#To export table\n",
    "import dataframe_image as dfi\n",
    "\n",
    "# For pre-processing data \n",
    "from sklearn import preprocessing as pp \n",
    "from sklearn.compose import ColumnTransformer \n",
    "\n",
    "# For splits and CV\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import KFold # Cross validation \n",
    "from sklearn.model_selection import cross_validate # Cross validation \n",
    "from sklearn.model_selection import GridSearchCV # Cross validation + param. tuning.\n",
    "\n",
    "# Machine learning methods \n",
    "from sklearn.linear_model import LinearRegression as LM\n",
    "from sklearn.svm import SVR\n",
    "from sklearn.tree import DecisionTreeRegressor as DR\n",
    "from sklearn.ensemble import RandomForestRegressor as RR\n",
    "\n",
    "# For evaluating our model's performance\n",
    "import sklearn.metrics as m\n",
    "\n",
    "# Pipeline to combine modeling elements\n",
    "from sklearn.pipeline import Pipeline\n",
    "\n",
    "# For model interpretation\n",
    "from sklearn.inspection import (\n",
    "    permutation_importance,\n",
    "    partial_dependence, \n",
    "    PartialDependenceDisplay, \n",
    "    plot_partial_dependence\n",
    ")\n",
    "\n",
    "# Misc\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b6ed06fb",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "#To display the entire dataframe\n",
    "#pd.set_option(\"display.max_rows\", None, \"display.max_columns\", None)\n",
    "\n",
    "#To be able to make modifications on subsets of dataframes\n",
    "pd.options.mode.chained_assignment = None"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58d6ee78",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "#### Scraping the Target Variable - Global Food Insecurity Index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8c882a41",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "#This is the link of our target variable - Global Food Insecurity Index\n",
    "url_target = \"https://impact.economist.com/sustainability/project/food-security-index/Index/YoY\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0c9f321a",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "200"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Downloading the web page\n",
    "page_target = requests.get(url_target) \n",
    "page_target.status_code #Checking status - we got 200, so its good"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5da5e37c",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "soup_target = BeautifulSoup(page_target.content, 'html.parser') # Parsing\n",
    "target_content = [i.get_text() for i in soup_target.find_all('table')] #Obtaining the table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3e4d701b",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[\"\\n\\n\\nGlobal ranking\\nCountry\\n2012\\n2013\\n2014\\n2015\\n2016\\n2017\\n2018\\n2019\\n2020\\n2021\\n\\nLast 10 Year change\\n\\n\\n\\n\\n54th\\nAlgeria\\n\\n53.2\\n51.3\\n57.4\\n58.2\\n62.9\\n63.5\\n63.3\\n63.7\\n61.6\\n63.9\\n\\n\\n\\n98th\\nAngola\\n\\n40.1\\n40.9\\n38.5\\n40.2\\n38.4\\n38.1\\n39.1\\n40.6\\n41.7\\n41.1\\n\\n\\n\\n53rd\\nArgentina\\n\\n61.0\\n61.2\\n61.5\\n59.1\\n64.2\\n65.6\\n66.9\\n63.1\\n63.1\\n64.2\\n\\n\\n\\n=32nd\\nAustralia\\n\\n70.9\\n74.4\\n76.5\\n75.9\\n77.1\\n76.1\\n78.1\\n75.9\\n73.9\\n71.6\\n\\n\\n\\n2nd\\nAustria\\n\\n79.6\\n79.0\\n79.0\\n79.9\\n79.6\\n79.2\\n78.7\\n79.2\\n80.1\\n81.3\\n\\n\\n\\n56th\\nAzerbaijan\\n\\n57.5\\n61.2\\n62.8\\n64.5\\n61.6\\n58.9\\n58.8\\n61.9\\n61.5\\n62.6\\n\\n\\n\\n43rd\\nBahrain\\n\\n63.2\\n64.9\\n67.1\\n65.4\\n65.8\\n67.1\\n67.8\\n68.2\\n68.4\\n68.5\\n\\n\\n\\n84th\\nBangladesh\\n\\n44.4\\n47.7\\n49.1\\n50.2\\n51.0\\n53.4\\n53.0\\n50.8\\n50.5\\n49.1\\n\\n\\n\\n=36th\\nBelarus\\n\\n64.0\\n64.6\\n67.5\\n64.0\\n67.9\\n66.3\\n71.5\\n73.5\\n70.4\\n70.9\\n\\n\\n\\n19th\\nBelgium\\n\\n76.1\\n76.4\\n77.1\\n77.9\\n77.5\\n78.3\\n78.1\\n76.9\\n76.9\\n76.1\\n\\n\\n\\n93rd\\nBenin\\n\\n39.9\\n40.3\\n41.8\\n45.2\\n45.8\\n46.4\\n45.4\\n45.4\\n46.1\\n45.2\\n\\n\\n\\n65th\\nBolivia\\n\\n53.1\\n58.2\\n58.5\\n61.2\\n62.1\\n61.0\\n61.2\\n60.3\\n59.0\\n59.9\\n\\n\\n\\n74th\\nBotswana\\n\\n53.7\\n53.8\\n53.4\\n53.4\\n54.0\\n53.7\\n54.7\\n56.1\\n56.1\\n55.5\\n\\n\\n\\n63rd\\nBrazil\\n\\n65.4\\n66.5\\n65.4\\n68.4\\n68.8\\n67.7\\n70.7\\n66.7\\n62.8\\n60.6\\n\\n\\n\\n38th\\nBulgaria\\n\\n63.1\\n63.9\\n63.7\\n66.8\\n67.3\\n67.7\\n66.0\\n66.9\\n68.7\\n70.5\\n\\n\\n\\n85th\\nBurkina Faso\\n\\n40.4\\n42.7\\n43.1\\n44.1\\n43.9\\n46.3\\n48.1\\n49.5\\n46.8\\n48.1\\n\\n\\n\\n113th\\nBurundi\\n\\n39.2\\n38.7\\n38.8\\n41.4\\n42.7\\n41.5\\n31.1\\n37.2\\n38.0\\n34.7\\n\\n\\n\\n81st\\nCambodia\\n\\n43.8\\n46.4\\n48.3\\n50.5\\n51.1\\n51.6\\n53.1\\n54.0\\n51.3\\n53.0\\n\\n\\n\\n92nd\\nCameroon\\n\\n44.2\\n41.4\\n42.1\\n46.4\\n45.4\\n45.2\\n46.2\\n44.4\\n43.9\\n45.5\\n\\n\\n\\n7th\\nCanada\\n\\n73.7\\n74.4\\n74.7\\n73.1\\n73.3\\n73.6\\n75.6\\n77.8\\n78.0\\n79.8\\n\\n\\n\\n99th\\nChad\\n\\n33.2\\n32.3\\n35.9\\n39.0\\n39.2\\n39.9\\n40.2\\n43.8\\n41.7\\n40.6\\n\\n\\n\\n28th\\nChile\\n\\n67.8\\n67.8\\n69.8\\n68.1\\n67.7\\n70.8\\n73.4\\n73.3\\n72.1\\n73.2\\n\\n\\n\\n34th\\nChina\\n\\n61.7\\n68.2\\n69.6\\n72.1\\n72.5\\n70.9\\n72.6\\n74.4\\n70.9\\n71.3\\n\\n\\n\\n52nd\\nColombia\\n\\n63.2\\n61.1\\n65.7\\n67.4\\n64.7\\n63.5\\n67.5\\n67.4\\n60.5\\n64.4\\n\\n\\n\\n103rd\\nCongo (Dem. Rep.)\\n\\n32.3\\n34.8\\n34.6\\n35.1\\n34.3\\n35.4\\n32.8\\n38.0\\n38.1\\n39.1\\n\\n\\n\\n=24th\\nCosta Rica\\n\\n69.5\\n72.1\\n71.0\\n70.4\\n71.2\\n69.6\\n72.5\\n70.8\\n71.4\\n73.6\\n\\n\\n\\n=86th\\nCôte d'Ivoire\\n\\n43.8\\n44.3\\n43.8\\n47.1\\n44.4\\n46.8\\n49.7\\n50.3\\n50.4\\n48.0\\n\\n\\n\\n14th\\nCzech Republic\\n\\n74.9\\n74.3\\n74.5\\n75.6\\n79.7\\n78.8\\n77.6\\n79.4\\n78.8\\n77.8\\n\\n\\n\\n17th\\nDenmark\\n\\n76.5\\n77.1\\n78.2\\n78.5\\n77.7\\n78.4\\n77.8\\n78.0\\n76.7\\n76.5\\n\\n\\n\\n47th\\nDominican Republic\\n\\n61.7\\n60.0\\n59.8\\n63.8\\n65.1\\n63.1\\n65.8\\n65.6\\n66.2\\n65.4\\n\\n\\n\\n66th\\nEcuador\\n\\n56.3\\n54.5\\n58.5\\n57.2\\n57.7\\n60.6\\n60.2\\n60.8\\n58.9\\n59.6\\n\\n\\n\\n62nd\\nEgypt\\n\\n58.9\\n58.5\\n59.5\\n62.4\\n59.8\\n58.0\\n57.0\\n61.3\\n59.8\\n60.8\\n\\n\\n\\n67th\\nEl Salvador\\n\\n57.7\\n60.3\\n59.7\\n60.8\\n57.2\\n57.5\\n58.8\\n58.9\\n57.8\\n59.5\\n\\n\\n\\n108th\\nEthiopia\\n\\n33.7\\n35.4\\n41.6\\n42.1\\n41.9\\n44.5\\n41.3\\n41.5\\n36.7\\n37.6\\n\\n\\n\\n4th\\nFinland\\n\\n80.6\\n79.6\\n80.2\\n81.9\\n84.1\\n84.1\\n83.9\\n84.5\\n85.2\\n80.9\\n\\n\\n\\n=9th\\nFrance\\n\\n79.1\\n78.7\\n79.0\\n79.7\\n78.6\\n80.7\\n79.6\\n78.5\\n78.4\\n79.1\\n\\n\\n\\n11th\\nGermany\\n\\n77.6\\n77.6\\n77.1\\n78.6\\n80.1\\n80.4\\n79.4\\n79.7\\n79.6\\n78.7\\n\\n\\n\\n82nd\\nGhana\\n\\n48.8\\n50.4\\n50.5\\n51.9\\n50.7\\n52.8\\n53.5\\n52.9\\n52.8\\n52.0\\n\\n\\n\\n27th\\nGreece\\n\\n71.0\\n72.0\\n75.1\\n76.6\\n75.4\\n75.9\\n75.5\\n74.6\\n74.1\\n73.3\\n\\n\\n\\n80th\\nGuatemala\\n\\n52.3\\n53.3\\n56.1\\n56.1\\n58.2\\n58.0\\n57.9\\n55.5\\n55.1\\n53.5\\n\\n\\n\\n96th\\nGuinea\\n\\n34.7\\n36.0\\n39.8\\n41.6\\n38.5\\n40.0\\n40.4\\n40.6\\n42.8\\n43.0\\n\\n\\n\\n=106th\\nHaiti\\n\\n38.7\\n40.0\\n41.4\\n42.7\\n41.2\\n39.4\\n38.7\\n35.8\\n35.7\\n37.8\\n\\n\\n\\n68th\\nHonduras\\n\\n54.3\\n56.8\\n58.7\\n56.7\\n57.3\\n56.6\\n60.2\\n59.7\\n58.1\\n59.4\\n\\n\\n\\n31st\\nHungary\\n\\n68.2\\n67.9\\n68.9\\n71.7\\n72.5\\n72.5\\n70.6\\n71.3\\n72.0\\n72.1\\n\\n\\n\\n71st\\nIndia\\n\\n54.5\\n54.7\\n53.3\\n56.0\\n58.4\\n58.9\\n59.9\\n59.8\\n58.6\\n57.2\\n\\n\\n\\n69th\\nIndonesia\\n\\n52.5\\n54.2\\n54.7\\n55.6\\n55.8\\n57.1\\n62.4\\n60.4\\n61.4\\n59.2\\n\\n\\n\\n1st\\nIreland\\n\\n80.0\\n80.6\\n80.4\\n80.9\\n82.6\\n83.7\\n84.8\\n84.7\\n84.5\\n84.0\\n\\n\\n\\n12th\\nIsrael\\n\\n72.6\\n73.5\\n73.4\\n76.4\\n77.5\\n76.3\\n78.6\\n78.2\\n78.1\\n78.0\\n\\n\\n\\n18th\\nItaly\\n\\n76.4\\n75.9\\n77.2\\n77.6\\n77.3\\n76.9\\n76.1\\n76.1\\n76.5\\n76.4\\n\\n\\n\\n8th\\nJapan\\n\\n77.1\\n78.1\\n78.2\\n78.6\\n79.0\\n79.8\\n79.8\\n79.8\\n80.3\\n79.3\\n\\n\\n\\n=49th\\nJordan\\n\\n65.0\\n64.7\\n61.9\\n64.2\\n64.3\\n64.6\\n64.2\\n63.4\\n64.9\\n64.6\\n\\n\\n\\n41st\\nKazakhstan\\n\\n61.5\\n60.4\\n63.2\\n63.1\\n64.4\\n63.6\\n65.1\\n69.9\\n71.9\\n69.2\\n\\n\\n\\n90th\\nKenya\\n\\n38.3\\n40.5\\n43.4\\n43.6\\n43.2\\n45.9\\n45.3\\n48.6\\n46.7\\n46.8\\n\\n\\n\\n30th\\nKuwait\\n\\n68.9\\n70.5\\n70.5\\n67.6\\n66.3\\n68.0\\n69.7\\n71.3\\n72.9\\n72.2\\n\\n\\n\\n91st\\nLaos\\n\\n41.5\\n45.4\\n44.6\\n48.4\\n48.9\\n50.7\\n52.5\\n49.5\\n51.0\\n46.4\\n\\n\\n\\n100th\\nMadagascar\\n\\n36.7\\n38.3\\n38.6\\n38.7\\n39.0\\n36.8\\n35.9\\n35.1\\n38.0\\n40.4\\n\\n\\n\\n109th\\nMalawi\\n\\n39.5\\n33.9\\n35.3\\n36.9\\n36.3\\n35.4\\n39.5\\n40.0\\n39.1\\n37.3\\n\\n\\n\\n39th\\nMalaysia\\n\\n65.5\\n66.8\\n69.2\\n68.6\\n65.0\\n64.2\\n64.1\\n66.8\\n65.0\\n70.1\\n\\n\\n\\n76th\\nMali\\n\\n46.6\\n48.7\\n49.9\\n50.4\\n48.5\\n49.9\\n52.7\\n53.1\\n52.7\\n54.5\\n\\n\\n\\n46th\\nMexico\\n\\n63.0\\n60.0\\n64.4\\n62.9\\n64.2\\n67.3\\n64.1\\n68.2\\n66.1\\n66.9\\n\\n\\n\\n57th\\nMorocco\\n\\n54.1\\n54.8\\n55.7\\n58.4\\n57.8\\n58.2\\n62.0\\n59.2\\n62.1\\n62.5\\n\\n\\n\\n111th\\nMozambique\\n\\n37.7\\n43.0\\n42.5\\n41.6\\n39.8\\n36.3\\n37.3\\n41.7\\n37.2\\n35.9\\n\\n\\n\\n72nd\\nMyanmar\\n\\n49.8\\n49.8\\n52.8\\n52.3\\n53.2\\n52.5\\n55.1\\n55.2\\n54.0\\n56.7\\n\\n\\n\\n79th\\nNepal\\n\\n46.7\\n47.1\\n50.3\\n49.7\\n50.7\\n52.4\\n54.9\\n55.3\\n54.8\\n53.7\\n\\n\\n\\n6th\\nNetherlands\\n\\n77.5\\n77.7\\n76.9\\n78.5\\n79.0\\n78.7\\n81.0\\n81.2\\n80.0\\n79.9\\n\\n\\n\\n16th\\nNew Zealand\\n\\n75.4\\n77.2\\n77.2\\n77.0\\n78.2\\n78.3\\n77.3\\n78.2\\n77.7\\n76.8\\n\\n\\n\\n73rd\\nNicaragua\\n\\n54.6\\n54.5\\n55.6\\n56.5\\n55.8\\n58.2\\n56.3\\n54.8\\n53.0\\n56.0\\n\\n\\n\\n88th\\nNiger\\n\\n40.4\\n40.8\\n40.4\\n43.3\\n46.4\\n44.8\\n48.3\\n49.8\\n49.9\\n47.6\\n\\n\\n\\n97th\\nNigeria\\n\\n39.0\\n41.1\\n39.5\\n40.9\\n42.4\\n41.9\\n40.5\\n42.6\\n41.2\\n41.3\\n\\n\\n\\n20th\\nNorway\\n\\n79.4\\n80.5\\n80.2\\n80.3\\n79.9\\n79.3\\n80.5\\n79.7\\n78.5\\n76.0\\n\\n\\n\\n40th\\nOman\\n\\n58.1\\n59.1\\n66.0\\n66.1\\n68.9\\n70.3\\n70.9\\n69.9\\n70.5\\n70.0\\n\\n\\n\\n75th\\nPakistan\\n\\n45.7\\n47.5\\n48.0\\n51.1\\n53.8\\n55.0\\n57.5\\n58.2\\n55.7\\n54.7\\n\\n\\n\\n=36th\\nPanama\\n\\n62.5\\n64.9\\n64.7\\n65.5\\n66.2\\n71.2\\n69.3\\n72.1\\n72.8\\n70.9\\n\\n\\n\\n59th\\nParaguay\\n\\n53.1\\n56.2\\n56.3\\n53.9\\n58.2\\n58.6\\n58.2\\n58.4\\n60.4\\n61.6\\n\\n\\n\\n=49th\\nPeru\\n\\n59.8\\n61.8\\n62.7\\n65.5\\n65.1\\n65.4\\n65.7\\n68.4\\n66.2\\n64.6\\n\\n\\n\\n64th\\nPhilippines\\n\\n52.9\\n53.1\\n54.2\\n56.1\\n57.4\\n55.7\\n57.7\\n60.1\\n61.0\\n60.0\\n\\n\\n\\n22nd\\nPoland\\n\\n70.5\\n72.7\\n74.6\\n74.1\\n74.5\\n74.6\\n74.4\\n76.9\\n75.2\\n74.9\\n\\n\\n\\n21st\\nPortugal\\n\\n76.7\\n75.7\\n74.8\\n76.1\\n76.9\\n76.2\\n77.4\\n76.6\\n76.5\\n75.2\\n\\n\\n\\n=24th\\nQatar\\n\\n66.2\\n69.2\\n72.4\\n72.5\\n72.0\\n74.5\\n73.1\\n74.8\\n73.7\\n73.6\\n\\n\\n\\n29th\\nRomania\\n\\n66.2\\n67.3\\n69.2\\n70.3\\n71.2\\n71.3\\n69.9\\n71.9\\n73.8\\n72.4\\n\\n\\n\\n23rd\\nRussia\\n\\n65.4\\n66.5\\n65.9\\n65.6\\n63.5\\n69.8\\n72.6\\n73.4\\n73.9\\n74.8\\n\\n\\n\\n101st\\nRwanda\\n\\n43.6\\n39.9\\n42.5\\n44.2\\n42.6\\n37.4\\n38.8\\n43.7\\n45.2\\n40.3\\n\\n\\n\\n44th\\nSaudi Arabia\\n\\n63.6\\n65.7\\n66.6\\n68.6\\n67.6\\n69.3\\n70.2\\n68.7\\n71.5\\n68.1\\n\\n\\n\\n89th\\nSenegal\\n\\n41.4\\n42.6\\n45.9\\n48.1\\n48.7\\n46.7\\n48.5\\n48.1\\n45.5\\n47.4\\n\\n\\n\\n60th\\nSerbia\\n\\n56.8\\n57.9\\n61.1\\n63.9\\n60.6\\n62.2\\n60.6\\n61.3\\n61.2\\n61.4\\n\\n\\n\\n104th\\nSierra Leone\\n\\n33.8\\n35.2\\n41.7\\n43.1\\n40.7\\n38.0\\n33.9\\n36.6\\n39.8\\n38.1\\n\\n\\n\\n15th\\nSingapore\\n\\n72.3\\n73.9\\n76.1\\n75.9\\n75.8\\n76.3\\n76.1\\n78.4\\n78.3\\n77.4\\n\\n\\n\\n42nd\\nSlovakia\\n\\n67.5\\n69.0\\n68.5\\n71.1\\n71.7\\n71.4\\n68.5\\n68.6\\n68.1\\n68.7\\n\\n\\n\\n70th\\nSouth Africa\\n\\n55.8\\n56.3\\n56.8\\n59.5\\n63.4\\n60.0\\n61.6\\n59.4\\n58.0\\n57.8\\n\\n\\n\\n=32nd\\nSouth Korea\\n\\n67.5\\n68.9\\n70.1\\n70.4\\n72.2\\n72.8\\n73.1\\n72.3\\n73.4\\n71.6\\n\\n\\n\\n=24th\\nSpain\\n\\n73.8\\n74.7\\n74.2\\n74.9\\n75.2\\n74.1\\n74.8\\n74.3\\n74.8\\n73.6\\n\\n\\n\\n77th\\nSri Lanka\\n\\n53.1\\n55.3\\n53.9\\n55.5\\n57.5\\n59.3\\n56.9\\n59.3\\n57.1\\n54.1\\n\\n\\n\\n110th\\nSudan\\n\\n34.7\\n33.4\\n36.0\\n36.8\\n39.1\\n40.7\\n38.9\\n39.3\\n36.4\\n37.1\\n\\n\\n\\n13th\\nSweden\\n\\n77.3\\n77.9\\n78.0\\n79.5\\n80.3\\n82.1\\n80.8\\n80.5\\n78.3\\n77.9\\n\\n\\n\\n5th\\nSwitzerland\\n\\n78.9\\n78.9\\n78.5\\n77.7\\n78.8\\n78.3\\n81.1\\n79.7\\n80.2\\n80.4\\n\\n\\n\\n=106th\\nSyria\\n\\n41.2\\n38.1\\n43.0\\n42.8\\n42.5\\n39.3\\n40.3\\n45.4\\n39.5\\n37.8\\n\\n\\n\\n83rd\\nTajikistan\\n\\n44.2\\n48.0\\n47.4\\n46.0\\n48.3\\n50.3\\n50.2\\n54.8\\n52.5\\n51.6\\n\\n\\n\\n=86th\\nTanzania\\n\\n34.7\\n36.3\\n40.0\\n38.8\\n44.5\\n45.6\\n43.1\\n45.3\\n47.7\\n48.0\\n\\n\\n\\n51st\\nThailand\\n\\n59.7\\n62.5\\n63.7\\n63.5\\n64.7\\n63.5\\n63.7\\n63.8\\n63.6\\n64.5\\n\\n\\n\\n94th\\nTogo\\n\\n39.0\\n39.6\\n42.0\\n43.8\\n38.4\\n45.4\\n44.3\\n46.2\\n45.7\\n44.2\\n\\n\\n\\n55th\\nTunisia\\n\\n60.0\\n57.7\\n58.4\\n59.2\\n59.8\\n63.2\\n62.2\\n61.8\\n60.2\\n62.7\\n\\n\\n\\n48th\\nTurkey\\n\\n64.0\\n63.5\\n65.0\\n64.0\\n64.5\\n65.9\\n64.0\\n63.5\\n61.2\\n65.1\\n\\n\\n\\n95th\\nUganda\\n\\n40.3\\n42.3\\n46.7\\n47.9\\n46.5\\n46.3\\n40.8\\n43.7\\n43.2\\n43.9\\n\\n\\n\\n58th\\nUkraine\\n\\n57.8\\n56.1\\n58.2\\n54.9\\n50.7\\n54.1\\n53.2\\n56.3\\n58.8\\n62.0\\n\\n\\n\\n35th\\nUnited Arab Emirates\\n\\n61.4\\n60.3\\n61.5\\n63.8\\n63.1\\n65.8\\n70.1\\n70.1\\n71.4\\n71.0\\n\\n\\n\\n3rd\\nUnited Kingdom\\n\\n74.9\\n76.7\\n77.3\\n80.2\\n79.7\\n80.1\\n79.3\\n80.6\\n80.6\\n81.0\\n\\n\\n\\n=9th\\nUnited States\\n\\n77.8\\n77.9\\n80.0\\n77.7\\n79.8\\n80.0\\n79.5\\n79.3\\n80.0\\n79.1\\n\\n\\n\\n45th\\nUruguay\\n\\n62.0\\n66.3\\n66.2\\n66.1\\n67.4\\n68.2\\n73.4\\n72.7\\n72.7\\n68.0\\n\\n\\n\\n78th\\nUzbekistan\\n\\n53.3\\n54.7\\n53.1\\n55.5\\n58.6\\n60.2\\n55.0\\n54.5\\n54.1\\n53.8\\n\\n\\n\\n102nd\\nVenezuela\\n\\n49.3\\n49.1\\n48.3\\n47.8\\n49.8\\n46.2\\n45.8\\n41.7\\n40.5\\n39.4\\n\\n\\n\\n61st\\nVietnam\\n\\n53.9\\n58.9\\n62.1\\n62.2\\n63.0\\n59.6\\n62.9\\n62.3\\n62.7\\n61.1\\n\\n\\n\\n112th\\nYemen\\n\\n35.7\\n35.2\\n34.9\\n40.2\\n36.4\\n36.0\\n34.7\\n35.2\\n35.2\\n35.7\\n\\n\\n\\n105th\\nZambia\\n\\n38.2\\n41.4\\n40.5\\n40.1\\n42.3\\n38.0\\n41.9\\n41.1\\n38.9\\n38.0\\n\\n\\n\\n\"]\n"
     ]
    }
   ],
   "source": [
    "print(target_content) #Checking what we got"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c322e58b",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "#Output is one long string inside a list, so we are going to split it using a for loop\n",
    "for i in target_content:\n",
    "    temp1 = i.split(\"\\n\") #separating by the new line character\n",
    "       "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "130c1a98",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['', '', '', 'Global ranking', 'Country', '2012', '2013', '2014', '2015', '2016', '2017', '2018', '2019', '2020', '2021', '', 'Last 10 Year change', '', '', '', '', '54th', 'Algeria', '', '53.2', '51.3', '57.4', '58.2', '62.9', '63.5', '63.3', '63.7', '61.6', '63.9', '', '', '', '98th', 'Angola', '', '40.1', '40.9', '38.5', '40.2', '38.4', '38.1', '39.1', '40.6', '41.7', '41.1', '', '', '', '53rd', 'Argentina', '', '61.0', '61.2', '61.5', '59.1', '64.2', '65.6', '66.9', '63.1', '63.1', '64.2', '', '', '', '=32nd', 'Australia', '', '70.9', '74.4', '76.5', '75.9', '77.1', '76.1', '78.1', '75.9', '73.9', '71.6', '', '', '', '2nd', 'Austria', '', '79.6', '79.0', '79.0', '79.9', '79.6', '79.2', '78.7', '79.2', '80.1', '81.3', '', '', '', '56th', 'Azerbaijan', '', '57.5', '61.2', '62.8', '64.5', '61.6', '58.9', '58.8', '61.9', '61.5', '62.6', '', '', '', '43rd', 'Bahrain', '', '63.2', '64.9', '67.1', '65.4', '65.8', '67.1', '67.8', '68.2', '68.4', '68.5', '', '', '', '84th', 'Bangladesh', '', '44.4', '47.7', '49.1', '50.2', '51.0', '53.4', '53.0', '50.8', '50.5', '49.1', '', '', '', '=36th', 'Belarus', '', '64.0', '64.6', '67.5', '64.0', '67.9', '66.3', '71.5', '73.5', '70.4', '70.9', '', '', '', '19th', 'Belgium', '', '76.1', '76.4', '77.1', '77.9', '77.5', '78.3', '78.1', '76.9', '76.9', '76.1', '', '', '', '93rd', 'Benin', '', '39.9', '40.3', '41.8', '45.2', '45.8', '46.4', '45.4', '45.4', '46.1', '45.2', '', '', '', '65th', 'Bolivia', '', '53.1', '58.2', '58.5', '61.2', '62.1', '61.0', '61.2', '60.3', '59.0', '59.9', '', '', '', '74th', 'Botswana', '', '53.7', '53.8', '53.4', '53.4', '54.0', '53.7', '54.7', '56.1', '56.1', '55.5', '', '', '', '63rd', 'Brazil', '', '65.4', '66.5', '65.4', '68.4', '68.8', '67.7', '70.7', '66.7', '62.8', '60.6', '', '', '', '38th', 'Bulgaria', '', '63.1', '63.9', '63.7', '66.8', '67.3', '67.7', '66.0', '66.9', '68.7', '70.5', '', '', '', '85th', 'Burkina Faso', '', '40.4', '42.7', '43.1', '44.1', '43.9', '46.3', '48.1', '49.5', '46.8', '48.1', '', '', '', '113th', 'Burundi', '', '39.2', '38.7', '38.8', '41.4', '42.7', '41.5', '31.1', '37.2', '38.0', '34.7', '', '', '', '81st', 'Cambodia', '', '43.8', '46.4', '48.3', '50.5', '51.1', '51.6', '53.1', '54.0', '51.3', '53.0', '', '', '', '92nd', 'Cameroon', '', '44.2', '41.4', '42.1', '46.4', '45.4', '45.2', '46.2', '44.4', '43.9', '45.5', '', '', '', '7th', 'Canada', '', '73.7', '74.4', '74.7', '73.1', '73.3', '73.6', '75.6', '77.8', '78.0', '79.8', '', '', '', '99th', 'Chad', '', '33.2', '32.3', '35.9', '39.0', '39.2', '39.9', '40.2', '43.8', '41.7', '40.6', '', '', '', '28th', 'Chile', '', '67.8', '67.8', '69.8', '68.1', '67.7', '70.8', '73.4', '73.3', '72.1', '73.2', '', '', '', '34th', 'China', '', '61.7', '68.2', '69.6', '72.1', '72.5', '70.9', '72.6', '74.4', '70.9', '71.3', '', '', '', '52nd', 'Colombia', '', '63.2', '61.1', '65.7', '67.4', '64.7', '63.5', '67.5', '67.4', '60.5', '64.4', '', '', '', '103rd', 'Congo (Dem. Rep.)', '', '32.3', '34.8', '34.6', '35.1', '34.3', '35.4', '32.8', '38.0', '38.1', '39.1', '', '', '', '=24th', 'Costa Rica', '', '69.5', '72.1', '71.0', '70.4', '71.2', '69.6', '72.5', '70.8', '71.4', '73.6', '', '', '', '=86th', \"Côte d'Ivoire\", '', '43.8', '44.3', '43.8', '47.1', '44.4', '46.8', '49.7', '50.3', '50.4', '48.0', '', '', '', '14th', 'Czech Republic', '', '74.9', '74.3', '74.5', '75.6', '79.7', '78.8', '77.6', '79.4', '78.8', '77.8', '', '', '', '17th', 'Denmark', '', '76.5', '77.1', '78.2', '78.5', '77.7', '78.4', '77.8', '78.0', '76.7', '76.5', '', '', '', '47th', 'Dominican Republic', '', '61.7', '60.0', '59.8', '63.8', '65.1', '63.1', '65.8', '65.6', '66.2', '65.4', '', '', '', '66th', 'Ecuador', '', '56.3', '54.5', '58.5', '57.2', '57.7', '60.6', '60.2', '60.8', '58.9', '59.6', '', '', '', '62nd', 'Egypt', '', '58.9', '58.5', '59.5', '62.4', '59.8', '58.0', '57.0', '61.3', '59.8', '60.8', '', '', '', '67th', 'El Salvador', '', '57.7', '60.3', '59.7', '60.8', '57.2', '57.5', '58.8', '58.9', '57.8', '59.5', '', '', '', '108th', 'Ethiopia', '', '33.7', '35.4', '41.6', '42.1', '41.9', '44.5', '41.3', '41.5', '36.7', '37.6', '', '', '', '4th', 'Finland', '', '80.6', '79.6', '80.2', '81.9', '84.1', '84.1', '83.9', '84.5', '85.2', '80.9', '', '', '', '=9th', 'France', '', '79.1', '78.7', '79.0', '79.7', '78.6', '80.7', '79.6', '78.5', '78.4', '79.1', '', '', '', '11th', 'Germany', '', '77.6', '77.6', '77.1', '78.6', '80.1', '80.4', '79.4', '79.7', '79.6', '78.7', '', '', '', '82nd', 'Ghana', '', '48.8', '50.4', '50.5', '51.9', '50.7', '52.8', '53.5', '52.9', '52.8', '52.0', '', '', '', '27th', 'Greece', '', '71.0', '72.0', '75.1', '76.6', '75.4', '75.9', '75.5', '74.6', '74.1', '73.3', '', '', '', '80th', 'Guatemala', '', '52.3', '53.3', '56.1', '56.1', '58.2', '58.0', '57.9', '55.5', '55.1', '53.5', '', '', '', '96th', 'Guinea', '', '34.7', '36.0', '39.8', '41.6', '38.5', '40.0', '40.4', '40.6', '42.8', '43.0', '', '', '', '=106th', 'Haiti', '', '38.7', '40.0', '41.4', '42.7', '41.2', '39.4', '38.7', '35.8', '35.7', '37.8', '', '', '', '68th', 'Honduras', '', '54.3', '56.8', '58.7', '56.7', '57.3', '56.6', '60.2', '59.7', '58.1', '59.4', '', '', '', '31st', 'Hungary', '', '68.2', '67.9', '68.9', '71.7', '72.5', '72.5', '70.6', '71.3', '72.0', '72.1', '', '', '', '71st', 'India', '', '54.5', '54.7', '53.3', '56.0', '58.4', '58.9', '59.9', '59.8', '58.6', '57.2', '', '', '', '69th', 'Indonesia', '', '52.5', '54.2', '54.7', '55.6', '55.8', '57.1', '62.4', '60.4', '61.4', '59.2', '', '', '', '1st', 'Ireland', '', '80.0', '80.6', '80.4', '80.9', '82.6', '83.7', '84.8', '84.7', '84.5', '84.0', '', '', '', '12th', 'Israel', '', '72.6', '73.5', '73.4', '76.4', '77.5', '76.3', '78.6', '78.2', '78.1', '78.0', '', '', '', '18th', 'Italy', '', '76.4', '75.9', '77.2', '77.6', '77.3', '76.9', '76.1', '76.1', '76.5', '76.4', '', '', '', '8th', 'Japan', '', '77.1', '78.1', '78.2', '78.6', '79.0', '79.8', '79.8', '79.8', '80.3', '79.3', '', '', '', '=49th', 'Jordan', '', '65.0', '64.7', '61.9', '64.2', '64.3', '64.6', '64.2', '63.4', '64.9', '64.6', '', '', '', '41st', 'Kazakhstan', '', '61.5', '60.4', '63.2', '63.1', '64.4', '63.6', '65.1', '69.9', '71.9', '69.2', '', '', '', '90th', 'Kenya', '', '38.3', '40.5', '43.4', '43.6', '43.2', '45.9', '45.3', '48.6', '46.7', '46.8', '', '', '', '30th', 'Kuwait', '', '68.9', '70.5', '70.5', '67.6', '66.3', '68.0', '69.7', '71.3', '72.9', '72.2', '', '', '', '91st', 'Laos', '', '41.5', '45.4', '44.6', '48.4', '48.9', '50.7', '52.5', '49.5', '51.0', '46.4', '', '', '', '100th', 'Madagascar', '', '36.7', '38.3', '38.6', '38.7', '39.0', '36.8', '35.9', '35.1', '38.0', '40.4', '', '', '', '109th', 'Malawi', '', '39.5', '33.9', '35.3', '36.9', '36.3', '35.4', '39.5', '40.0', '39.1', '37.3', '', '', '', '39th', 'Malaysia', '', '65.5', '66.8', '69.2', '68.6', '65.0', '64.2', '64.1', '66.8', '65.0', '70.1', '', '', '', '76th', 'Mali', '', '46.6', '48.7', '49.9', '50.4', '48.5', '49.9', '52.7', '53.1', '52.7', '54.5', '', '', '', '46th', 'Mexico', '', '63.0', '60.0', '64.4', '62.9', '64.2', '67.3', '64.1', '68.2', '66.1', '66.9', '', '', '', '57th', 'Morocco', '', '54.1', '54.8', '55.7', '58.4', '57.8', '58.2', '62.0', '59.2', '62.1', '62.5', '', '', '', '111th', 'Mozambique', '', '37.7', '43.0', '42.5', '41.6', '39.8', '36.3', '37.3', '41.7', '37.2', '35.9', '', '', '', '72nd', 'Myanmar', '', '49.8', '49.8', '52.8', '52.3', '53.2', '52.5', '55.1', '55.2', '54.0', '56.7', '', '', '', '79th', 'Nepal', '', '46.7', '47.1', '50.3', '49.7', '50.7', '52.4', '54.9', '55.3', '54.8', '53.7', '', '', '', '6th', 'Netherlands', '', '77.5', '77.7', '76.9', '78.5', '79.0', '78.7', '81.0', '81.2', '80.0', '79.9', '', '', '', '16th', 'New Zealand', '', '75.4', '77.2', '77.2', '77.0', '78.2', '78.3', '77.3', '78.2', '77.7', '76.8', '', '', '', '73rd', 'Nicaragua', '', '54.6', '54.5', '55.6', '56.5', '55.8', '58.2', '56.3', '54.8', '53.0', '56.0', '', '', '', '88th', 'Niger', '', '40.4', '40.8', '40.4', '43.3', '46.4', '44.8', '48.3', '49.8', '49.9', '47.6', '', '', '', '97th', 'Nigeria', '', '39.0', '41.1', '39.5', '40.9', '42.4', '41.9', '40.5', '42.6', '41.2', '41.3', '', '', '', '20th', 'Norway', '', '79.4', '80.5', '80.2', '80.3', '79.9', '79.3', '80.5', '79.7', '78.5', '76.0', '', '', '', '40th', 'Oman', '', '58.1', '59.1', '66.0', '66.1', '68.9', '70.3', '70.9', '69.9', '70.5', '70.0', '', '', '', '75th', 'Pakistan', '', '45.7', '47.5', '48.0', '51.1', '53.8', '55.0', '57.5', '58.2', '55.7', '54.7', '', '', '', '=36th', 'Panama', '', '62.5', '64.9', '64.7', '65.5', '66.2', '71.2', '69.3', '72.1', '72.8', '70.9', '', '', '', '59th', 'Paraguay', '', '53.1', '56.2', '56.3', '53.9', '58.2', '58.6', '58.2', '58.4', '60.4', '61.6', '', '', '', '=49th', 'Peru', '', '59.8', '61.8', '62.7', '65.5', '65.1', '65.4', '65.7', '68.4', '66.2', '64.6', '', '', '', '64th', 'Philippines', '', '52.9', '53.1', '54.2', '56.1', '57.4', '55.7', '57.7', '60.1', '61.0', '60.0', '', '', '', '22nd', 'Poland', '', '70.5', '72.7', '74.6', '74.1', '74.5', '74.6', '74.4', '76.9', '75.2', '74.9', '', '', '', '21st', 'Portugal', '', '76.7', '75.7', '74.8', '76.1', '76.9', '76.2', '77.4', '76.6', '76.5', '75.2', '', '', '', '=24th', 'Qatar', '', '66.2', '69.2', '72.4', '72.5', '72.0', '74.5', '73.1', '74.8', '73.7', '73.6', '', '', '', '29th', 'Romania', '', '66.2', '67.3', '69.2', '70.3', '71.2', '71.3', '69.9', '71.9', '73.8', '72.4', '', '', '', '23rd', 'Russia', '', '65.4', '66.5', '65.9', '65.6', '63.5', '69.8', '72.6', '73.4', '73.9', '74.8', '', '', '', '101st', 'Rwanda', '', '43.6', '39.9', '42.5', '44.2', '42.6', '37.4', '38.8', '43.7', '45.2', '40.3', '', '', '', '44th', 'Saudi Arabia', '', '63.6', '65.7', '66.6', '68.6', '67.6', '69.3', '70.2', '68.7', '71.5', '68.1', '', '', '', '89th', 'Senegal', '', '41.4', '42.6', '45.9', '48.1', '48.7', '46.7', '48.5', '48.1', '45.5', '47.4', '', '', '', '60th', 'Serbia', '', '56.8', '57.9', '61.1', '63.9', '60.6', '62.2', '60.6', '61.3', '61.2', '61.4', '', '', '', '104th', 'Sierra Leone', '', '33.8', '35.2', '41.7', '43.1', '40.7', '38.0', '33.9', '36.6', '39.8', '38.1', '', '', '', '15th', 'Singapore', '', '72.3', '73.9', '76.1', '75.9', '75.8', '76.3', '76.1', '78.4', '78.3', '77.4', '', '', '', '42nd', 'Slovakia', '', '67.5', '69.0', '68.5', '71.1', '71.7', '71.4', '68.5', '68.6', '68.1', '68.7', '', '', '', '70th', 'South Africa', '', '55.8', '56.3', '56.8', '59.5', '63.4', '60.0', '61.6', '59.4', '58.0', '57.8', '', '', '', '=32nd', 'South Korea', '', '67.5', '68.9', '70.1', '70.4', '72.2', '72.8', '73.1', '72.3', '73.4', '71.6', '', '', '', '=24th', 'Spain', '', '73.8', '74.7', '74.2', '74.9', '75.2', '74.1', '74.8', '74.3', '74.8', '73.6', '', '', '', '77th', 'Sri Lanka', '', '53.1', '55.3', '53.9', '55.5', '57.5', '59.3', '56.9', '59.3', '57.1', '54.1', '', '', '', '110th', 'Sudan', '', '34.7', '33.4', '36.0', '36.8', '39.1', '40.7', '38.9', '39.3', '36.4', '37.1', '', '', '', '13th', 'Sweden', '', '77.3', '77.9', '78.0', '79.5', '80.3', '82.1', '80.8', '80.5', '78.3', '77.9', '', '', '', '5th', 'Switzerland', '', '78.9', '78.9', '78.5', '77.7', '78.8', '78.3', '81.1', '79.7', '80.2', '80.4', '', '', '', '=106th', 'Syria', '', '41.2', '38.1', '43.0', '42.8', '42.5', '39.3', '40.3', '45.4', '39.5', '37.8', '', '', '', '83rd', 'Tajikistan', '', '44.2', '48.0', '47.4', '46.0', '48.3', '50.3', '50.2', '54.8', '52.5', '51.6', '', '', '', '=86th', 'Tanzania', '', '34.7', '36.3', '40.0', '38.8', '44.5', '45.6', '43.1', '45.3', '47.7', '48.0', '', '', '', '51st', 'Thailand', '', '59.7', '62.5', '63.7', '63.5', '64.7', '63.5', '63.7', '63.8', '63.6', '64.5', '', '', '', '94th', 'Togo', '', '39.0', '39.6', '42.0', '43.8', '38.4', '45.4', '44.3', '46.2', '45.7', '44.2', '', '', '', '55th', 'Tunisia', '', '60.0', '57.7', '58.4', '59.2', '59.8', '63.2', '62.2', '61.8', '60.2', '62.7', '', '', '', '48th', 'Turkey', '', '64.0', '63.5', '65.0', '64.0', '64.5', '65.9', '64.0', '63.5', '61.2', '65.1', '', '', '', '95th', 'Uganda', '', '40.3', '42.3', '46.7', '47.9', '46.5', '46.3', '40.8', '43.7', '43.2', '43.9', '', '', '', '58th', 'Ukraine', '', '57.8', '56.1', '58.2', '54.9', '50.7', '54.1', '53.2', '56.3', '58.8', '62.0', '', '', '', '35th', 'United Arab Emirates', '', '61.4', '60.3', '61.5', '63.8', '63.1', '65.8', '70.1', '70.1', '71.4', '71.0', '', '', '', '3rd', 'United Kingdom', '', '74.9', '76.7', '77.3', '80.2', '79.7', '80.1', '79.3', '80.6', '80.6', '81.0', '', '', '', '=9th', 'United States', '', '77.8', '77.9', '80.0', '77.7', '79.8', '80.0', '79.5', '79.3', '80.0', '79.1', '', '', '', '45th', 'Uruguay', '', '62.0', '66.3', '66.2', '66.1', '67.4', '68.2', '73.4', '72.7', '72.7', '68.0', '', '', '', '78th', 'Uzbekistan', '', '53.3', '54.7', '53.1', '55.5', '58.6', '60.2', '55.0', '54.5', '54.1', '53.8', '', '', '', '102nd', 'Venezuela', '', '49.3', '49.1', '48.3', '47.8', '49.8', '46.2', '45.8', '41.7', '40.5', '39.4', '', '', '', '61st', 'Vietnam', '', '53.9', '58.9', '62.1', '62.2', '63.0', '59.6', '62.9', '62.3', '62.7', '61.1', '', '', '', '112th', 'Yemen', '', '35.7', '35.2', '34.9', '40.2', '36.4', '36.0', '34.7', '35.2', '35.2', '35.7', '', '', '', '105th', 'Zambia', '', '38.2', '41.4', '40.5', '40.1', '42.3', '38.0', '41.9', '41.1', '38.9', '38.0', '', '', '', '']\n"
     ]
    }
   ],
   "source": [
    "#Checking if we got desired result\n",
    "print(temp1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "be3e0d46",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "#We have split the contents of the string, now we are going to remove unnecessary content\n",
    "\n",
    "temp_list1 =[] #Empty list that will later store the content \n",
    "\n",
    "for i in temp1: #For loop to iterate through our list\n",
    "    if(i!=''):   #To remove spaces \n",
    "        if(i!=\"Last 10 Year change\"):  #To remove this string\n",
    "            temp_list1.append(i)       #Only appending the required content\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "0493351d",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Global ranking', 'Country', '2012', '2013', '2014', '2015', '2016', '2017', '2018', '2019', '2020', '2021', '54th', 'Algeria', '53.2', '51.3', '57.4', '58.2', '62.9', '63.5', '63.3', '63.7', '61.6', '63.9', '98th', 'Angola', '40.1', '40.9', '38.5', '40.2', '38.4', '38.1', '39.1', '40.6', '41.7', '41.1', '53rd', 'Argentina', '61.0', '61.2', '61.5', '59.1', '64.2', '65.6', '66.9', '63.1', '63.1', '64.2', '=32nd', 'Australia', '70.9', '74.4', '76.5', '75.9', '77.1', '76.1', '78.1', '75.9', '73.9', '71.6', '2nd', 'Austria', '79.6', '79.0', '79.0', '79.9', '79.6', '79.2', '78.7', '79.2', '80.1', '81.3', '56th', 'Azerbaijan', '57.5', '61.2', '62.8', '64.5', '61.6', '58.9', '58.8', '61.9', '61.5', '62.6', '43rd', 'Bahrain', '63.2', '64.9', '67.1', '65.4', '65.8', '67.1', '67.8', '68.2', '68.4', '68.5', '84th', 'Bangladesh', '44.4', '47.7', '49.1', '50.2', '51.0', '53.4', '53.0', '50.8', '50.5', '49.1', '=36th', 'Belarus', '64.0', '64.6', '67.5', '64.0', '67.9', '66.3', '71.5', '73.5', '70.4', '70.9', '19th', 'Belgium', '76.1', '76.4', '77.1', '77.9', '77.5', '78.3', '78.1', '76.9', '76.9', '76.1', '93rd', 'Benin', '39.9', '40.3', '41.8', '45.2', '45.8', '46.4', '45.4', '45.4', '46.1', '45.2', '65th', 'Bolivia', '53.1', '58.2', '58.5', '61.2', '62.1', '61.0', '61.2', '60.3', '59.0', '59.9', '74th', 'Botswana', '53.7', '53.8', '53.4', '53.4', '54.0', '53.7', '54.7', '56.1', '56.1', '55.5', '63rd', 'Brazil', '65.4', '66.5', '65.4', '68.4', '68.8', '67.7', '70.7', '66.7', '62.8', '60.6', '38th', 'Bulgaria', '63.1', '63.9', '63.7', '66.8', '67.3', '67.7', '66.0', '66.9', '68.7', '70.5', '85th', 'Burkina Faso', '40.4', '42.7', '43.1', '44.1', '43.9', '46.3', '48.1', '49.5', '46.8', '48.1', '113th', 'Burundi', '39.2', '38.7', '38.8', '41.4', '42.7', '41.5', '31.1', '37.2', '38.0', '34.7', '81st', 'Cambodia', '43.8', '46.4', '48.3', '50.5', '51.1', '51.6', '53.1', '54.0', '51.3', '53.0', '92nd', 'Cameroon', '44.2', '41.4', '42.1', '46.4', '45.4', '45.2', '46.2', '44.4', '43.9', '45.5', '7th', 'Canada', '73.7', '74.4', '74.7', '73.1', '73.3', '73.6', '75.6', '77.8', '78.0', '79.8', '99th', 'Chad', '33.2', '32.3', '35.9', '39.0', '39.2', '39.9', '40.2', '43.8', '41.7', '40.6', '28th', 'Chile', '67.8', '67.8', '69.8', '68.1', '67.7', '70.8', '73.4', '73.3', '72.1', '73.2', '34th', 'China', '61.7', '68.2', '69.6', '72.1', '72.5', '70.9', '72.6', '74.4', '70.9', '71.3', '52nd', 'Colombia', '63.2', '61.1', '65.7', '67.4', '64.7', '63.5', '67.5', '67.4', '60.5', '64.4', '103rd', 'Congo (Dem. Rep.)', '32.3', '34.8', '34.6', '35.1', '34.3', '35.4', '32.8', '38.0', '38.1', '39.1', '=24th', 'Costa Rica', '69.5', '72.1', '71.0', '70.4', '71.2', '69.6', '72.5', '70.8', '71.4', '73.6', '=86th', \"Côte d'Ivoire\", '43.8', '44.3', '43.8', '47.1', '44.4', '46.8', '49.7', '50.3', '50.4', '48.0', '14th', 'Czech Republic', '74.9', '74.3', '74.5', '75.6', '79.7', '78.8', '77.6', '79.4', '78.8', '77.8', '17th', 'Denmark', '76.5', '77.1', '78.2', '78.5', '77.7', '78.4', '77.8', '78.0', '76.7', '76.5', '47th', 'Dominican Republic', '61.7', '60.0', '59.8', '63.8', '65.1', '63.1', '65.8', '65.6', '66.2', '65.4', '66th', 'Ecuador', '56.3', '54.5', '58.5', '57.2', '57.7', '60.6', '60.2', '60.8', '58.9', '59.6', '62nd', 'Egypt', '58.9', '58.5', '59.5', '62.4', '59.8', '58.0', '57.0', '61.3', '59.8', '60.8', '67th', 'El Salvador', '57.7', '60.3', '59.7', '60.8', '57.2', '57.5', '58.8', '58.9', '57.8', '59.5', '108th', 'Ethiopia', '33.7', '35.4', '41.6', '42.1', '41.9', '44.5', '41.3', '41.5', '36.7', '37.6', '4th', 'Finland', '80.6', '79.6', '80.2', '81.9', '84.1', '84.1', '83.9', '84.5', '85.2', '80.9', '=9th', 'France', '79.1', '78.7', '79.0', '79.7', '78.6', '80.7', '79.6', '78.5', '78.4', '79.1', '11th', 'Germany', '77.6', '77.6', '77.1', '78.6', '80.1', '80.4', '79.4', '79.7', '79.6', '78.7', '82nd', 'Ghana', '48.8', '50.4', '50.5', '51.9', '50.7', '52.8', '53.5', '52.9', '52.8', '52.0', '27th', 'Greece', '71.0', '72.0', '75.1', '76.6', '75.4', '75.9', '75.5', '74.6', '74.1', '73.3', '80th', 'Guatemala', '52.3', '53.3', '56.1', '56.1', '58.2', '58.0', '57.9', '55.5', '55.1', '53.5', '96th', 'Guinea', '34.7', '36.0', '39.8', '41.6', '38.5', '40.0', '40.4', '40.6', '42.8', '43.0', '=106th', 'Haiti', '38.7', '40.0', '41.4', '42.7', '41.2', '39.4', '38.7', '35.8', '35.7', '37.8', '68th', 'Honduras', '54.3', '56.8', '58.7', '56.7', '57.3', '56.6', '60.2', '59.7', '58.1', '59.4', '31st', 'Hungary', '68.2', '67.9', '68.9', '71.7', '72.5', '72.5', '70.6', '71.3', '72.0', '72.1', '71st', 'India', '54.5', '54.7', '53.3', '56.0', '58.4', '58.9', '59.9', '59.8', '58.6', '57.2', '69th', 'Indonesia', '52.5', '54.2', '54.7', '55.6', '55.8', '57.1', '62.4', '60.4', '61.4', '59.2', '1st', 'Ireland', '80.0', '80.6', '80.4', '80.9', '82.6', '83.7', '84.8', '84.7', '84.5', '84.0', '12th', 'Israel', '72.6', '73.5', '73.4', '76.4', '77.5', '76.3', '78.6', '78.2', '78.1', '78.0', '18th', 'Italy', '76.4', '75.9', '77.2', '77.6', '77.3', '76.9', '76.1', '76.1', '76.5', '76.4', '8th', 'Japan', '77.1', '78.1', '78.2', '78.6', '79.0', '79.8', '79.8', '79.8', '80.3', '79.3', '=49th', 'Jordan', '65.0', '64.7', '61.9', '64.2', '64.3', '64.6', '64.2', '63.4', '64.9', '64.6', '41st', 'Kazakhstan', '61.5', '60.4', '63.2', '63.1', '64.4', '63.6', '65.1', '69.9', '71.9', '69.2', '90th', 'Kenya', '38.3', '40.5', '43.4', '43.6', '43.2', '45.9', '45.3', '48.6', '46.7', '46.8', '30th', 'Kuwait', '68.9', '70.5', '70.5', '67.6', '66.3', '68.0', '69.7', '71.3', '72.9', '72.2', '91st', 'Laos', '41.5', '45.4', '44.6', '48.4', '48.9', '50.7', '52.5', '49.5', '51.0', '46.4', '100th', 'Madagascar', '36.7', '38.3', '38.6', '38.7', '39.0', '36.8', '35.9', '35.1', '38.0', '40.4', '109th', 'Malawi', '39.5', '33.9', '35.3', '36.9', '36.3', '35.4', '39.5', '40.0', '39.1', '37.3', '39th', 'Malaysia', '65.5', '66.8', '69.2', '68.6', '65.0', '64.2', '64.1', '66.8', '65.0', '70.1', '76th', 'Mali', '46.6', '48.7', '49.9', '50.4', '48.5', '49.9', '52.7', '53.1', '52.7', '54.5', '46th', 'Mexico', '63.0', '60.0', '64.4', '62.9', '64.2', '67.3', '64.1', '68.2', '66.1', '66.9', '57th', 'Morocco', '54.1', '54.8', '55.7', '58.4', '57.8', '58.2', '62.0', '59.2', '62.1', '62.5', '111th', 'Mozambique', '37.7', '43.0', '42.5', '41.6', '39.8', '36.3', '37.3', '41.7', '37.2', '35.9', '72nd', 'Myanmar', '49.8', '49.8', '52.8', '52.3', '53.2', '52.5', '55.1', '55.2', '54.0', '56.7', '79th', 'Nepal', '46.7', '47.1', '50.3', '49.7', '50.7', '52.4', '54.9', '55.3', '54.8', '53.7', '6th', 'Netherlands', '77.5', '77.7', '76.9', '78.5', '79.0', '78.7', '81.0', '81.2', '80.0', '79.9', '16th', 'New Zealand', '75.4', '77.2', '77.2', '77.0', '78.2', '78.3', '77.3', '78.2', '77.7', '76.8', '73rd', 'Nicaragua', '54.6', '54.5', '55.6', '56.5', '55.8', '58.2', '56.3', '54.8', '53.0', '56.0', '88th', 'Niger', '40.4', '40.8', '40.4', '43.3', '46.4', '44.8', '48.3', '49.8', '49.9', '47.6', '97th', 'Nigeria', '39.0', '41.1', '39.5', '40.9', '42.4', '41.9', '40.5', '42.6', '41.2', '41.3', '20th', 'Norway', '79.4', '80.5', '80.2', '80.3', '79.9', '79.3', '80.5', '79.7', '78.5', '76.0', '40th', 'Oman', '58.1', '59.1', '66.0', '66.1', '68.9', '70.3', '70.9', '69.9', '70.5', '70.0', '75th', 'Pakistan', '45.7', '47.5', '48.0', '51.1', '53.8', '55.0', '57.5', '58.2', '55.7', '54.7', '=36th', 'Panama', '62.5', '64.9', '64.7', '65.5', '66.2', '71.2', '69.3', '72.1', '72.8', '70.9', '59th', 'Paraguay', '53.1', '56.2', '56.3', '53.9', '58.2', '58.6', '58.2', '58.4', '60.4', '61.6', '=49th', 'Peru', '59.8', '61.8', '62.7', '65.5', '65.1', '65.4', '65.7', '68.4', '66.2', '64.6', '64th', 'Philippines', '52.9', '53.1', '54.2', '56.1', '57.4', '55.7', '57.7', '60.1', '61.0', '60.0', '22nd', 'Poland', '70.5', '72.7', '74.6', '74.1', '74.5', '74.6', '74.4', '76.9', '75.2', '74.9', '21st', 'Portugal', '76.7', '75.7', '74.8', '76.1', '76.9', '76.2', '77.4', '76.6', '76.5', '75.2', '=24th', 'Qatar', '66.2', '69.2', '72.4', '72.5', '72.0', '74.5', '73.1', '74.8', '73.7', '73.6', '29th', 'Romania', '66.2', '67.3', '69.2', '70.3', '71.2', '71.3', '69.9', '71.9', '73.8', '72.4', '23rd', 'Russia', '65.4', '66.5', '65.9', '65.6', '63.5', '69.8', '72.6', '73.4', '73.9', '74.8', '101st', 'Rwanda', '43.6', '39.9', '42.5', '44.2', '42.6', '37.4', '38.8', '43.7', '45.2', '40.3', '44th', 'Saudi Arabia', '63.6', '65.7', '66.6', '68.6', '67.6', '69.3', '70.2', '68.7', '71.5', '68.1', '89th', 'Senegal', '41.4', '42.6', '45.9', '48.1', '48.7', '46.7', '48.5', '48.1', '45.5', '47.4', '60th', 'Serbia', '56.8', '57.9', '61.1', '63.9', '60.6', '62.2', '60.6', '61.3', '61.2', '61.4', '104th', 'Sierra Leone', '33.8', '35.2', '41.7', '43.1', '40.7', '38.0', '33.9', '36.6', '39.8', '38.1', '15th', 'Singapore', '72.3', '73.9', '76.1', '75.9', '75.8', '76.3', '76.1', '78.4', '78.3', '77.4', '42nd', 'Slovakia', '67.5', '69.0', '68.5', '71.1', '71.7', '71.4', '68.5', '68.6', '68.1', '68.7', '70th', 'South Africa', '55.8', '56.3', '56.8', '59.5', '63.4', '60.0', '61.6', '59.4', '58.0', '57.8', '=32nd', 'South Korea', '67.5', '68.9', '70.1', '70.4', '72.2', '72.8', '73.1', '72.3', '73.4', '71.6', '=24th', 'Spain', '73.8', '74.7', '74.2', '74.9', '75.2', '74.1', '74.8', '74.3', '74.8', '73.6', '77th', 'Sri Lanka', '53.1', '55.3', '53.9', '55.5', '57.5', '59.3', '56.9', '59.3', '57.1', '54.1', '110th', 'Sudan', '34.7', '33.4', '36.0', '36.8', '39.1', '40.7', '38.9', '39.3', '36.4', '37.1', '13th', 'Sweden', '77.3', '77.9', '78.0', '79.5', '80.3', '82.1', '80.8', '80.5', '78.3', '77.9', '5th', 'Switzerland', '78.9', '78.9', '78.5', '77.7', '78.8', '78.3', '81.1', '79.7', '80.2', '80.4', '=106th', 'Syria', '41.2', '38.1', '43.0', '42.8', '42.5', '39.3', '40.3', '45.4', '39.5', '37.8', '83rd', 'Tajikistan', '44.2', '48.0', '47.4', '46.0', '48.3', '50.3', '50.2', '54.8', '52.5', '51.6', '=86th', 'Tanzania', '34.7', '36.3', '40.0', '38.8', '44.5', '45.6', '43.1', '45.3', '47.7', '48.0', '51st', 'Thailand', '59.7', '62.5', '63.7', '63.5', '64.7', '63.5', '63.7', '63.8', '63.6', '64.5', '94th', 'Togo', '39.0', '39.6', '42.0', '43.8', '38.4', '45.4', '44.3', '46.2', '45.7', '44.2', '55th', 'Tunisia', '60.0', '57.7', '58.4', '59.2', '59.8', '63.2', '62.2', '61.8', '60.2', '62.7', '48th', 'Turkey', '64.0', '63.5', '65.0', '64.0', '64.5', '65.9', '64.0', '63.5', '61.2', '65.1', '95th', 'Uganda', '40.3', '42.3', '46.7', '47.9', '46.5', '46.3', '40.8', '43.7', '43.2', '43.9', '58th', 'Ukraine', '57.8', '56.1', '58.2', '54.9', '50.7', '54.1', '53.2', '56.3', '58.8', '62.0', '35th', 'United Arab Emirates', '61.4', '60.3', '61.5', '63.8', '63.1', '65.8', '70.1', '70.1', '71.4', '71.0', '3rd', 'United Kingdom', '74.9', '76.7', '77.3', '80.2', '79.7', '80.1', '79.3', '80.6', '80.6', '81.0', '=9th', 'United States', '77.8', '77.9', '80.0', '77.7', '79.8', '80.0', '79.5', '79.3', '80.0', '79.1', '45th', 'Uruguay', '62.0', '66.3', '66.2', '66.1', '67.4', '68.2', '73.4', '72.7', '72.7', '68.0', '78th', 'Uzbekistan', '53.3', '54.7', '53.1', '55.5', '58.6', '60.2', '55.0', '54.5', '54.1', '53.8', '102nd', 'Venezuela', '49.3', '49.1', '48.3', '47.8', '49.8', '46.2', '45.8', '41.7', '40.5', '39.4', '61st', 'Vietnam', '53.9', '58.9', '62.1', '62.2', '63.0', '59.6', '62.9', '62.3', '62.7', '61.1', '112th', 'Yemen', '35.7', '35.2', '34.9', '40.2', '36.4', '36.0', '34.7', '35.2', '35.2', '35.7', '105th', 'Zambia', '38.2', '41.4', '40.5', '40.1', '42.3', '38.0', '41.9', '41.1', '38.9', '38.0']\n"
     ]
    }
   ],
   "source": [
    "#Now checking the result again\n",
    "print(temp_list1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e93ddde3",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "#Now we have all the required content, so we are going to  store each country's data in a list to obtain a list of lists\n",
    "\n",
    "count1 = 0                         #To keep track \n",
    "temp_list2 =[]                     #An empty list to use in the loop, which will hold each country's data\n",
    "final_list =[]                     #This will be the final list of lists\n",
    "\n",
    "\n",
    "for i in temp_list1:               #Loop to iterate through the content\n",
    "    temp_list2.append(i)           #Appending the content to the empty list created above\n",
    "    count1 = count1 +1             #Incrementing the count variable\n",
    "    \n",
    "    if (count1%12==0):             #We get information about the next country after 12 observations\n",
    "        final_list.append(temp_list2)   #So each country's information stored in a list is append to the final list to obtain list of lists\n",
    "        temp_list2=[]             #Clearing the contents of temp list after append statement"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "400b3755",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[['Global ranking', 'Country', '2012', '2013', '2014', '2015', '2016', '2017', '2018', '2019', '2020', '2021'], ['54th', 'Algeria', '53.2', '51.3', '57.4', '58.2', '62.9', '63.5', '63.3', '63.7', '61.6', '63.9'], ['98th', 'Angola', '40.1', '40.9', '38.5', '40.2', '38.4', '38.1', '39.1', '40.6', '41.7', '41.1'], ['53rd', 'Argentina', '61.0', '61.2', '61.5', '59.1', '64.2', '65.6', '66.9', '63.1', '63.1', '64.2'], ['=32nd', 'Australia', '70.9', '74.4', '76.5', '75.9', '77.1', '76.1', '78.1', '75.9', '73.9', '71.6'], ['2nd', 'Austria', '79.6', '79.0', '79.0', '79.9', '79.6', '79.2', '78.7', '79.2', '80.1', '81.3'], ['56th', 'Azerbaijan', '57.5', '61.2', '62.8', '64.5', '61.6', '58.9', '58.8', '61.9', '61.5', '62.6'], ['43rd', 'Bahrain', '63.2', '64.9', '67.1', '65.4', '65.8', '67.1', '67.8', '68.2', '68.4', '68.5'], ['84th', 'Bangladesh', '44.4', '47.7', '49.1', '50.2', '51.0', '53.4', '53.0', '50.8', '50.5', '49.1'], ['=36th', 'Belarus', '64.0', '64.6', '67.5', '64.0', '67.9', '66.3', '71.5', '73.5', '70.4', '70.9'], ['19th', 'Belgium', '76.1', '76.4', '77.1', '77.9', '77.5', '78.3', '78.1', '76.9', '76.9', '76.1'], ['93rd', 'Benin', '39.9', '40.3', '41.8', '45.2', '45.8', '46.4', '45.4', '45.4', '46.1', '45.2'], ['65th', 'Bolivia', '53.1', '58.2', '58.5', '61.2', '62.1', '61.0', '61.2', '60.3', '59.0', '59.9'], ['74th', 'Botswana', '53.7', '53.8', '53.4', '53.4', '54.0', '53.7', '54.7', '56.1', '56.1', '55.5'], ['63rd', 'Brazil', '65.4', '66.5', '65.4', '68.4', '68.8', '67.7', '70.7', '66.7', '62.8', '60.6'], ['38th', 'Bulgaria', '63.1', '63.9', '63.7', '66.8', '67.3', '67.7', '66.0', '66.9', '68.7', '70.5'], ['85th', 'Burkina Faso', '40.4', '42.7', '43.1', '44.1', '43.9', '46.3', '48.1', '49.5', '46.8', '48.1'], ['113th', 'Burundi', '39.2', '38.7', '38.8', '41.4', '42.7', '41.5', '31.1', '37.2', '38.0', '34.7'], ['81st', 'Cambodia', '43.8', '46.4', '48.3', '50.5', '51.1', '51.6', '53.1', '54.0', '51.3', '53.0'], ['92nd', 'Cameroon', '44.2', '41.4', '42.1', '46.4', '45.4', '45.2', '46.2', '44.4', '43.9', '45.5'], ['7th', 'Canada', '73.7', '74.4', '74.7', '73.1', '73.3', '73.6', '75.6', '77.8', '78.0', '79.8'], ['99th', 'Chad', '33.2', '32.3', '35.9', '39.0', '39.2', '39.9', '40.2', '43.8', '41.7', '40.6'], ['28th', 'Chile', '67.8', '67.8', '69.8', '68.1', '67.7', '70.8', '73.4', '73.3', '72.1', '73.2'], ['34th', 'China', '61.7', '68.2', '69.6', '72.1', '72.5', '70.9', '72.6', '74.4', '70.9', '71.3'], ['52nd', 'Colombia', '63.2', '61.1', '65.7', '67.4', '64.7', '63.5', '67.5', '67.4', '60.5', '64.4'], ['103rd', 'Congo (Dem. Rep.)', '32.3', '34.8', '34.6', '35.1', '34.3', '35.4', '32.8', '38.0', '38.1', '39.1'], ['=24th', 'Costa Rica', '69.5', '72.1', '71.0', '70.4', '71.2', '69.6', '72.5', '70.8', '71.4', '73.6'], ['=86th', \"Côte d'Ivoire\", '43.8', '44.3', '43.8', '47.1', '44.4', '46.8', '49.7', '50.3', '50.4', '48.0'], ['14th', 'Czech Republic', '74.9', '74.3', '74.5', '75.6', '79.7', '78.8', '77.6', '79.4', '78.8', '77.8'], ['17th', 'Denmark', '76.5', '77.1', '78.2', '78.5', '77.7', '78.4', '77.8', '78.0', '76.7', '76.5'], ['47th', 'Dominican Republic', '61.7', '60.0', '59.8', '63.8', '65.1', '63.1', '65.8', '65.6', '66.2', '65.4'], ['66th', 'Ecuador', '56.3', '54.5', '58.5', '57.2', '57.7', '60.6', '60.2', '60.8', '58.9', '59.6'], ['62nd', 'Egypt', '58.9', '58.5', '59.5', '62.4', '59.8', '58.0', '57.0', '61.3', '59.8', '60.8'], ['67th', 'El Salvador', '57.7', '60.3', '59.7', '60.8', '57.2', '57.5', '58.8', '58.9', '57.8', '59.5'], ['108th', 'Ethiopia', '33.7', '35.4', '41.6', '42.1', '41.9', '44.5', '41.3', '41.5', '36.7', '37.6'], ['4th', 'Finland', '80.6', '79.6', '80.2', '81.9', '84.1', '84.1', '83.9', '84.5', '85.2', '80.9'], ['=9th', 'France', '79.1', '78.7', '79.0', '79.7', '78.6', '80.7', '79.6', '78.5', '78.4', '79.1'], ['11th', 'Germany', '77.6', '77.6', '77.1', '78.6', '80.1', '80.4', '79.4', '79.7', '79.6', '78.7'], ['82nd', 'Ghana', '48.8', '50.4', '50.5', '51.9', '50.7', '52.8', '53.5', '52.9', '52.8', '52.0'], ['27th', 'Greece', '71.0', '72.0', '75.1', '76.6', '75.4', '75.9', '75.5', '74.6', '74.1', '73.3'], ['80th', 'Guatemala', '52.3', '53.3', '56.1', '56.1', '58.2', '58.0', '57.9', '55.5', '55.1', '53.5'], ['96th', 'Guinea', '34.7', '36.0', '39.8', '41.6', '38.5', '40.0', '40.4', '40.6', '42.8', '43.0'], ['=106th', 'Haiti', '38.7', '40.0', '41.4', '42.7', '41.2', '39.4', '38.7', '35.8', '35.7', '37.8'], ['68th', 'Honduras', '54.3', '56.8', '58.7', '56.7', '57.3', '56.6', '60.2', '59.7', '58.1', '59.4'], ['31st', 'Hungary', '68.2', '67.9', '68.9', '71.7', '72.5', '72.5', '70.6', '71.3', '72.0', '72.1'], ['71st', 'India', '54.5', '54.7', '53.3', '56.0', '58.4', '58.9', '59.9', '59.8', '58.6', '57.2'], ['69th', 'Indonesia', '52.5', '54.2', '54.7', '55.6', '55.8', '57.1', '62.4', '60.4', '61.4', '59.2'], ['1st', 'Ireland', '80.0', '80.6', '80.4', '80.9', '82.6', '83.7', '84.8', '84.7', '84.5', '84.0'], ['12th', 'Israel', '72.6', '73.5', '73.4', '76.4', '77.5', '76.3', '78.6', '78.2', '78.1', '78.0'], ['18th', 'Italy', '76.4', '75.9', '77.2', '77.6', '77.3', '76.9', '76.1', '76.1', '76.5', '76.4'], ['8th', 'Japan', '77.1', '78.1', '78.2', '78.6', '79.0', '79.8', '79.8', '79.8', '80.3', '79.3'], ['=49th', 'Jordan', '65.0', '64.7', '61.9', '64.2', '64.3', '64.6', '64.2', '63.4', '64.9', '64.6'], ['41st', 'Kazakhstan', '61.5', '60.4', '63.2', '63.1', '64.4', '63.6', '65.1', '69.9', '71.9', '69.2'], ['90th', 'Kenya', '38.3', '40.5', '43.4', '43.6', '43.2', '45.9', '45.3', '48.6', '46.7', '46.8'], ['30th', 'Kuwait', '68.9', '70.5', '70.5', '67.6', '66.3', '68.0', '69.7', '71.3', '72.9', '72.2'], ['91st', 'Laos', '41.5', '45.4', '44.6', '48.4', '48.9', '50.7', '52.5', '49.5', '51.0', '46.4'], ['100th', 'Madagascar', '36.7', '38.3', '38.6', '38.7', '39.0', '36.8', '35.9', '35.1', '38.0', '40.4'], ['109th', 'Malawi', '39.5', '33.9', '35.3', '36.9', '36.3', '35.4', '39.5', '40.0', '39.1', '37.3'], ['39th', 'Malaysia', '65.5', '66.8', '69.2', '68.6', '65.0', '64.2', '64.1', '66.8', '65.0', '70.1'], ['76th', 'Mali', '46.6', '48.7', '49.9', '50.4', '48.5', '49.9', '52.7', '53.1', '52.7', '54.5'], ['46th', 'Mexico', '63.0', '60.0', '64.4', '62.9', '64.2', '67.3', '64.1', '68.2', '66.1', '66.9'], ['57th', 'Morocco', '54.1', '54.8', '55.7', '58.4', '57.8', '58.2', '62.0', '59.2', '62.1', '62.5'], ['111th', 'Mozambique', '37.7', '43.0', '42.5', '41.6', '39.8', '36.3', '37.3', '41.7', '37.2', '35.9'], ['72nd', 'Myanmar', '49.8', '49.8', '52.8', '52.3', '53.2', '52.5', '55.1', '55.2', '54.0', '56.7'], ['79th', 'Nepal', '46.7', '47.1', '50.3', '49.7', '50.7', '52.4', '54.9', '55.3', '54.8', '53.7'], ['6th', 'Netherlands', '77.5', '77.7', '76.9', '78.5', '79.0', '78.7', '81.0', '81.2', '80.0', '79.9'], ['16th', 'New Zealand', '75.4', '77.2', '77.2', '77.0', '78.2', '78.3', '77.3', '78.2', '77.7', '76.8'], ['73rd', 'Nicaragua', '54.6', '54.5', '55.6', '56.5', '55.8', '58.2', '56.3', '54.8', '53.0', '56.0'], ['88th', 'Niger', '40.4', '40.8', '40.4', '43.3', '46.4', '44.8', '48.3', '49.8', '49.9', '47.6'], ['97th', 'Nigeria', '39.0', '41.1', '39.5', '40.9', '42.4', '41.9', '40.5', '42.6', '41.2', '41.3'], ['20th', 'Norway', '79.4', '80.5', '80.2', '80.3', '79.9', '79.3', '80.5', '79.7', '78.5', '76.0'], ['40th', 'Oman', '58.1', '59.1', '66.0', '66.1', '68.9', '70.3', '70.9', '69.9', '70.5', '70.0'], ['75th', 'Pakistan', '45.7', '47.5', '48.0', '51.1', '53.8', '55.0', '57.5', '58.2', '55.7', '54.7'], ['=36th', 'Panama', '62.5', '64.9', '64.7', '65.5', '66.2', '71.2', '69.3', '72.1', '72.8', '70.9'], ['59th', 'Paraguay', '53.1', '56.2', '56.3', '53.9', '58.2', '58.6', '58.2', '58.4', '60.4', '61.6'], ['=49th', 'Peru', '59.8', '61.8', '62.7', '65.5', '65.1', '65.4', '65.7', '68.4', '66.2', '64.6'], ['64th', 'Philippines', '52.9', '53.1', '54.2', '56.1', '57.4', '55.7', '57.7', '60.1', '61.0', '60.0'], ['22nd', 'Poland', '70.5', '72.7', '74.6', '74.1', '74.5', '74.6', '74.4', '76.9', '75.2', '74.9'], ['21st', 'Portugal', '76.7', '75.7', '74.8', '76.1', '76.9', '76.2', '77.4', '76.6', '76.5', '75.2'], ['=24th', 'Qatar', '66.2', '69.2', '72.4', '72.5', '72.0', '74.5', '73.1', '74.8', '73.7', '73.6'], ['29th', 'Romania', '66.2', '67.3', '69.2', '70.3', '71.2', '71.3', '69.9', '71.9', '73.8', '72.4'], ['23rd', 'Russia', '65.4', '66.5', '65.9', '65.6', '63.5', '69.8', '72.6', '73.4', '73.9', '74.8'], ['101st', 'Rwanda', '43.6', '39.9', '42.5', '44.2', '42.6', '37.4', '38.8', '43.7', '45.2', '40.3'], ['44th', 'Saudi Arabia', '63.6', '65.7', '66.6', '68.6', '67.6', '69.3', '70.2', '68.7', '71.5', '68.1'], ['89th', 'Senegal', '41.4', '42.6', '45.9', '48.1', '48.7', '46.7', '48.5', '48.1', '45.5', '47.4'], ['60th', 'Serbia', '56.8', '57.9', '61.1', '63.9', '60.6', '62.2', '60.6', '61.3', '61.2', '61.4'], ['104th', 'Sierra Leone', '33.8', '35.2', '41.7', '43.1', '40.7', '38.0', '33.9', '36.6', '39.8', '38.1'], ['15th', 'Singapore', '72.3', '73.9', '76.1', '75.9', '75.8', '76.3', '76.1', '78.4', '78.3', '77.4'], ['42nd', 'Slovakia', '67.5', '69.0', '68.5', '71.1', '71.7', '71.4', '68.5', '68.6', '68.1', '68.7'], ['70th', 'South Africa', '55.8', '56.3', '56.8', '59.5', '63.4', '60.0', '61.6', '59.4', '58.0', '57.8'], ['=32nd', 'South Korea', '67.5', '68.9', '70.1', '70.4', '72.2', '72.8', '73.1', '72.3', '73.4', '71.6'], ['=24th', 'Spain', '73.8', '74.7', '74.2', '74.9', '75.2', '74.1', '74.8', '74.3', '74.8', '73.6'], ['77th', 'Sri Lanka', '53.1', '55.3', '53.9', '55.5', '57.5', '59.3', '56.9', '59.3', '57.1', '54.1'], ['110th', 'Sudan', '34.7', '33.4', '36.0', '36.8', '39.1', '40.7', '38.9', '39.3', '36.4', '37.1'], ['13th', 'Sweden', '77.3', '77.9', '78.0', '79.5', '80.3', '82.1', '80.8', '80.5', '78.3', '77.9'], ['5th', 'Switzerland', '78.9', '78.9', '78.5', '77.7', '78.8', '78.3', '81.1', '79.7', '80.2', '80.4'], ['=106th', 'Syria', '41.2', '38.1', '43.0', '42.8', '42.5', '39.3', '40.3', '45.4', '39.5', '37.8'], ['83rd', 'Tajikistan', '44.2', '48.0', '47.4', '46.0', '48.3', '50.3', '50.2', '54.8', '52.5', '51.6'], ['=86th', 'Tanzania', '34.7', '36.3', '40.0', '38.8', '44.5', '45.6', '43.1', '45.3', '47.7', '48.0'], ['51st', 'Thailand', '59.7', '62.5', '63.7', '63.5', '64.7', '63.5', '63.7', '63.8', '63.6', '64.5'], ['94th', 'Togo', '39.0', '39.6', '42.0', '43.8', '38.4', '45.4', '44.3', '46.2', '45.7', '44.2'], ['55th', 'Tunisia', '60.0', '57.7', '58.4', '59.2', '59.8', '63.2', '62.2', '61.8', '60.2', '62.7'], ['48th', 'Turkey', '64.0', '63.5', '65.0', '64.0', '64.5', '65.9', '64.0', '63.5', '61.2', '65.1'], ['95th', 'Uganda', '40.3', '42.3', '46.7', '47.9', '46.5', '46.3', '40.8', '43.7', '43.2', '43.9'], ['58th', 'Ukraine', '57.8', '56.1', '58.2', '54.9', '50.7', '54.1', '53.2', '56.3', '58.8', '62.0'], ['35th', 'United Arab Emirates', '61.4', '60.3', '61.5', '63.8', '63.1', '65.8', '70.1', '70.1', '71.4', '71.0'], ['3rd', 'United Kingdom', '74.9', '76.7', '77.3', '80.2', '79.7', '80.1', '79.3', '80.6', '80.6', '81.0'], ['=9th', 'United States', '77.8', '77.9', '80.0', '77.7', '79.8', '80.0', '79.5', '79.3', '80.0', '79.1'], ['45th', 'Uruguay', '62.0', '66.3', '66.2', '66.1', '67.4', '68.2', '73.4', '72.7', '72.7', '68.0'], ['78th', 'Uzbekistan', '53.3', '54.7', '53.1', '55.5', '58.6', '60.2', '55.0', '54.5', '54.1', '53.8'], ['102nd', 'Venezuela', '49.3', '49.1', '48.3', '47.8', '49.8', '46.2', '45.8', '41.7', '40.5', '39.4'], ['61st', 'Vietnam', '53.9', '58.9', '62.1', '62.2', '63.0', '59.6', '62.9', '62.3', '62.7', '61.1'], ['112th', 'Yemen', '35.7', '35.2', '34.9', '40.2', '36.4', '36.0', '34.7', '35.2', '35.2', '35.7'], ['105th', 'Zambia', '38.2', '41.4', '40.5', '40.1', '42.3', '38.0', '41.9', '41.1', '38.9', '38.0']]\n"
     ]
    }
   ],
   "source": [
    "#Checking the result\n",
    "print(final_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "6b033ec5",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Global ranking</td>\n",
       "      <td>Country</td>\n",
       "      <td>2012</td>\n",
       "      <td>2013</td>\n",
       "      <td>2014</td>\n",
       "      <td>2015</td>\n",
       "      <td>2016</td>\n",
       "      <td>2017</td>\n",
       "      <td>2018</td>\n",
       "      <td>2019</td>\n",
       "      <td>2020</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>54th</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>53.2</td>\n",
       "      <td>51.3</td>\n",
       "      <td>57.4</td>\n",
       "      <td>58.2</td>\n",
       "      <td>62.9</td>\n",
       "      <td>63.5</td>\n",
       "      <td>63.3</td>\n",
       "      <td>63.7</td>\n",
       "      <td>61.6</td>\n",
       "      <td>63.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>98th</td>\n",
       "      <td>Angola</td>\n",
       "      <td>40.1</td>\n",
       "      <td>40.9</td>\n",
       "      <td>38.5</td>\n",
       "      <td>40.2</td>\n",
       "      <td>38.4</td>\n",
       "      <td>38.1</td>\n",
       "      <td>39.1</td>\n",
       "      <td>40.6</td>\n",
       "      <td>41.7</td>\n",
       "      <td>41.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53rd</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>61.0</td>\n",
       "      <td>61.2</td>\n",
       "      <td>61.5</td>\n",
       "      <td>59.1</td>\n",
       "      <td>64.2</td>\n",
       "      <td>65.6</td>\n",
       "      <td>66.9</td>\n",
       "      <td>63.1</td>\n",
       "      <td>63.1</td>\n",
       "      <td>64.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>=32nd</td>\n",
       "      <td>Australia</td>\n",
       "      <td>70.9</td>\n",
       "      <td>74.4</td>\n",
       "      <td>76.5</td>\n",
       "      <td>75.9</td>\n",
       "      <td>77.1</td>\n",
       "      <td>76.1</td>\n",
       "      <td>78.1</td>\n",
       "      <td>75.9</td>\n",
       "      <td>73.9</td>\n",
       "      <td>71.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>78th</td>\n",
       "      <td>Uzbekistan</td>\n",
       "      <td>53.3</td>\n",
       "      <td>54.7</td>\n",
       "      <td>53.1</td>\n",
       "      <td>55.5</td>\n",
       "      <td>58.6</td>\n",
       "      <td>60.2</td>\n",
       "      <td>55.0</td>\n",
       "      <td>54.5</td>\n",
       "      <td>54.1</td>\n",
       "      <td>53.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>102nd</td>\n",
       "      <td>Venezuela</td>\n",
       "      <td>49.3</td>\n",
       "      <td>49.1</td>\n",
       "      <td>48.3</td>\n",
       "      <td>47.8</td>\n",
       "      <td>49.8</td>\n",
       "      <td>46.2</td>\n",
       "      <td>45.8</td>\n",
       "      <td>41.7</td>\n",
       "      <td>40.5</td>\n",
       "      <td>39.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>61st</td>\n",
       "      <td>Vietnam</td>\n",
       "      <td>53.9</td>\n",
       "      <td>58.9</td>\n",
       "      <td>62.1</td>\n",
       "      <td>62.2</td>\n",
       "      <td>63.0</td>\n",
       "      <td>59.6</td>\n",
       "      <td>62.9</td>\n",
       "      <td>62.3</td>\n",
       "      <td>62.7</td>\n",
       "      <td>61.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>112th</td>\n",
       "      <td>Yemen</td>\n",
       "      <td>35.7</td>\n",
       "      <td>35.2</td>\n",
       "      <td>34.9</td>\n",
       "      <td>40.2</td>\n",
       "      <td>36.4</td>\n",
       "      <td>36.0</td>\n",
       "      <td>34.7</td>\n",
       "      <td>35.2</td>\n",
       "      <td>35.2</td>\n",
       "      <td>35.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>105th</td>\n",
       "      <td>Zambia</td>\n",
       "      <td>38.2</td>\n",
       "      <td>41.4</td>\n",
       "      <td>40.5</td>\n",
       "      <td>40.1</td>\n",
       "      <td>42.3</td>\n",
       "      <td>38.0</td>\n",
       "      <td>41.9</td>\n",
       "      <td>41.1</td>\n",
       "      <td>38.9</td>\n",
       "      <td>38.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>114 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 0           1     2     3     4     5     6     7     8   \\\n",
       "0    Global ranking     Country  2012  2013  2014  2015  2016  2017  2018   \n",
       "1              54th     Algeria  53.2  51.3  57.4  58.2  62.9  63.5  63.3   \n",
       "2              98th      Angola  40.1  40.9  38.5  40.2  38.4  38.1  39.1   \n",
       "3              53rd   Argentina  61.0  61.2  61.5  59.1  64.2  65.6  66.9   \n",
       "4             =32nd   Australia  70.9  74.4  76.5  75.9  77.1  76.1  78.1   \n",
       "..              ...         ...   ...   ...   ...   ...   ...   ...   ...   \n",
       "109            78th  Uzbekistan  53.3  54.7  53.1  55.5  58.6  60.2  55.0   \n",
       "110           102nd   Venezuela  49.3  49.1  48.3  47.8  49.8  46.2  45.8   \n",
       "111            61st     Vietnam  53.9  58.9  62.1  62.2  63.0  59.6  62.9   \n",
       "112           112th       Yemen  35.7  35.2  34.9  40.2  36.4  36.0  34.7   \n",
       "113           105th      Zambia  38.2  41.4  40.5  40.1  42.3  38.0  41.9   \n",
       "\n",
       "       9     10    11  \n",
       "0    2019  2020  2021  \n",
       "1    63.7  61.6  63.9  \n",
       "2    40.6  41.7  41.1  \n",
       "3    63.1  63.1  64.2  \n",
       "4    75.9  73.9  71.6  \n",
       "..    ...   ...   ...  \n",
       "109  54.5  54.1  53.8  \n",
       "110  41.7  40.5  39.4  \n",
       "111  62.3  62.7  61.1  \n",
       "112  35.2  35.2  35.7  \n",
       "113  41.1  38.9  38.0  \n",
       "\n",
       "[114 rows x 12 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#We have now got a list of lists as desired, now we will convert this to a dataframe\n",
    "food_insecurity = pd.DataFrame(final_list)\n",
    "\n",
    "#Checking the dataframe\n",
    "display(food_insecurity)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "08a9c86b",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "#We have now got a dataframe, now we will make the first row as the header of the dataframe \n",
    "\n",
    "new_header = food_insecurity.iloc[0] #grab the first row for the header\n",
    "food_insecurity = food_insecurity[1:] #take the data less the header row\n",
    "food_insecurity.columns = new_header #set the header row as the df header"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "20b699b7",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Global ranking</th>\n",
       "      <th>Country</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "      <th>2018</th>\n",
       "      <th>2019</th>\n",
       "      <th>2020</th>\n",
       "      <th>2021</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>54th</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>53.2</td>\n",
       "      <td>51.3</td>\n",
       "      <td>57.4</td>\n",
       "      <td>58.2</td>\n",
       "      <td>62.9</td>\n",
       "      <td>63.5</td>\n",
       "      <td>63.3</td>\n",
       "      <td>63.7</td>\n",
       "      <td>61.6</td>\n",
       "      <td>63.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>98th</td>\n",
       "      <td>Angola</td>\n",
       "      <td>40.1</td>\n",
       "      <td>40.9</td>\n",
       "      <td>38.5</td>\n",
       "      <td>40.2</td>\n",
       "      <td>38.4</td>\n",
       "      <td>38.1</td>\n",
       "      <td>39.1</td>\n",
       "      <td>40.6</td>\n",
       "      <td>41.7</td>\n",
       "      <td>41.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53rd</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>61.0</td>\n",
       "      <td>61.2</td>\n",
       "      <td>61.5</td>\n",
       "      <td>59.1</td>\n",
       "      <td>64.2</td>\n",
       "      <td>65.6</td>\n",
       "      <td>66.9</td>\n",
       "      <td>63.1</td>\n",
       "      <td>63.1</td>\n",
       "      <td>64.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>=32nd</td>\n",
       "      <td>Australia</td>\n",
       "      <td>70.9</td>\n",
       "      <td>74.4</td>\n",
       "      <td>76.5</td>\n",
       "      <td>75.9</td>\n",
       "      <td>77.1</td>\n",
       "      <td>76.1</td>\n",
       "      <td>78.1</td>\n",
       "      <td>75.9</td>\n",
       "      <td>73.9</td>\n",
       "      <td>71.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2nd</td>\n",
       "      <td>Austria</td>\n",
       "      <td>79.6</td>\n",
       "      <td>79.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>79.9</td>\n",
       "      <td>79.6</td>\n",
       "      <td>79.2</td>\n",
       "      <td>78.7</td>\n",
       "      <td>79.2</td>\n",
       "      <td>80.1</td>\n",
       "      <td>81.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>78th</td>\n",
       "      <td>Uzbekistan</td>\n",
       "      <td>53.3</td>\n",
       "      <td>54.7</td>\n",
       "      <td>53.1</td>\n",
       "      <td>55.5</td>\n",
       "      <td>58.6</td>\n",
       "      <td>60.2</td>\n",
       "      <td>55.0</td>\n",
       "      <td>54.5</td>\n",
       "      <td>54.1</td>\n",
       "      <td>53.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>102nd</td>\n",
       "      <td>Venezuela</td>\n",
       "      <td>49.3</td>\n",
       "      <td>49.1</td>\n",
       "      <td>48.3</td>\n",
       "      <td>47.8</td>\n",
       "      <td>49.8</td>\n",
       "      <td>46.2</td>\n",
       "      <td>45.8</td>\n",
       "      <td>41.7</td>\n",
       "      <td>40.5</td>\n",
       "      <td>39.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>61st</td>\n",
       "      <td>Vietnam</td>\n",
       "      <td>53.9</td>\n",
       "      <td>58.9</td>\n",
       "      <td>62.1</td>\n",
       "      <td>62.2</td>\n",
       "      <td>63.0</td>\n",
       "      <td>59.6</td>\n",
       "      <td>62.9</td>\n",
       "      <td>62.3</td>\n",
       "      <td>62.7</td>\n",
       "      <td>61.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>112th</td>\n",
       "      <td>Yemen</td>\n",
       "      <td>35.7</td>\n",
       "      <td>35.2</td>\n",
       "      <td>34.9</td>\n",
       "      <td>40.2</td>\n",
       "      <td>36.4</td>\n",
       "      <td>36.0</td>\n",
       "      <td>34.7</td>\n",
       "      <td>35.2</td>\n",
       "      <td>35.2</td>\n",
       "      <td>35.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>105th</td>\n",
       "      <td>Zambia</td>\n",
       "      <td>38.2</td>\n",
       "      <td>41.4</td>\n",
       "      <td>40.5</td>\n",
       "      <td>40.1</td>\n",
       "      <td>42.3</td>\n",
       "      <td>38.0</td>\n",
       "      <td>41.9</td>\n",
       "      <td>41.1</td>\n",
       "      <td>38.9</td>\n",
       "      <td>38.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>113 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "0   Global ranking     Country  2012  2013  2014  2015  2016  2017  2018  \\\n",
       "1             54th     Algeria  53.2  51.3  57.4  58.2  62.9  63.5  63.3   \n",
       "2             98th      Angola  40.1  40.9  38.5  40.2  38.4  38.1  39.1   \n",
       "3             53rd   Argentina  61.0  61.2  61.5  59.1  64.2  65.6  66.9   \n",
       "4            =32nd   Australia  70.9  74.4  76.5  75.9  77.1  76.1  78.1   \n",
       "5              2nd     Austria  79.6  79.0  79.0  79.9  79.6  79.2  78.7   \n",
       "..             ...         ...   ...   ...   ...   ...   ...   ...   ...   \n",
       "109           78th  Uzbekistan  53.3  54.7  53.1  55.5  58.6  60.2  55.0   \n",
       "110          102nd   Venezuela  49.3  49.1  48.3  47.8  49.8  46.2  45.8   \n",
       "111           61st     Vietnam  53.9  58.9  62.1  62.2  63.0  59.6  62.9   \n",
       "112          112th       Yemen  35.7  35.2  34.9  40.2  36.4  36.0  34.7   \n",
       "113          105th      Zambia  38.2  41.4  40.5  40.1  42.3  38.0  41.9   \n",
       "\n",
       "0    2019  2020  2021  \n",
       "1    63.7  61.6  63.9  \n",
       "2    40.6  41.7  41.1  \n",
       "3    63.1  63.1  64.2  \n",
       "4    75.9  73.9  71.6  \n",
       "5    79.2  80.1  81.3  \n",
       "..    ...   ...   ...  \n",
       "109  54.5  54.1  53.8  \n",
       "110  41.7  40.5  39.4  \n",
       "111  62.3  62.7  61.1  \n",
       "112  35.2  35.2  35.7  \n",
       "113  41.1  38.9  38.0  \n",
       "\n",
       "[113 rows x 12 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Checking the dataframe\n",
    "display(food_insecurity)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "4dbc9fd4",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "#Dropping the Global Ranking column\n",
    "food_insecurity = food_insecurity.drop(['Global ranking'], axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "ac5dc74e",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "#Converting this dataframe from wide to long format\n",
    "food_insecurity = pd.melt(food_insecurity,id_vars=['Country'])\n",
    "\n",
    "#Renaming column names\n",
    "food_insecurity = food_insecurity.rename(columns={0: 'Year', \"value\" : \"GFSI_Index\"})  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "0e740535",
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Year</th>\n",
       "      <th>GFSI_Index</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>2012</td>\n",
       "      <td>53.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Angola</td>\n",
       "      <td>2012</td>\n",
       "      <td>40.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>2012</td>\n",
       "      <td>61.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Australia</td>\n",
       "      <td>2012</td>\n",
       "      <td>70.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Austria</td>\n",
       "      <td>2012</td>\n",
       "      <td>79.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Country  Year GFSI_Index\n",
       "0    Algeria  2012       53.2\n",
       "1     Angola  2012       40.1\n",
       "2  Argentina  2012       61.0\n",
       "3  Australia  2012       70.9\n",
       "4    Austria  2012       79.6"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check the dataframe after changes\n",
    "food_insecurity.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "4ffb92cf",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "#Need to ensure all the countries merge properly later, so we are going to create a function for converting to standard type\n",
    "\n",
    "def func_cnames(df=None):\n",
    "    \"\"\"\n",
    "    func_cnames takes in a dataframe, converts it into a list, changes the country names to short type, and then is converted\n",
    "    back to a dataframe\n",
    "    -------------------------------------------------------------------------------------------------------------------------\n",
    "    Arguments:\n",
    "    df -> Dataframe; its default value is None\n",
    "    -------------------------------------------------------------------------------------------------------------------------\n",
    "    Return:\n",
    "    A dataframe\n",
    "    \"\"\"\n",
    "    \n",
    "    #Converting it to a list to perform some tasks\n",
    "    list1 = df.values.tolist()\n",
    "    \n",
    "    #Converting the country name to name_short \n",
    "    for i in range(1, df.shape[0]):\n",
    "        list1[i][0] = cc.convert(names=list1[i][0], to='name_short')\n",
    "    \n",
    "    df1 = pd.DataFrame(list1)\n",
    "    return(df1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "8464de38",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "#Calling function to standardize country-names\n",
    "fi_final = func_cnames(food_insecurity)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "3b81e41b",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>2012</td>\n",
       "      <td>53.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Angola</td>\n",
       "      <td>2012</td>\n",
       "      <td>40.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>2012</td>\n",
       "      <td>61.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Australia</td>\n",
       "      <td>2012</td>\n",
       "      <td>70.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Austria</td>\n",
       "      <td>2012</td>\n",
       "      <td>79.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1125</th>\n",
       "      <td>Uzbekistan</td>\n",
       "      <td>2021</td>\n",
       "      <td>53.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1126</th>\n",
       "      <td>Venezuela</td>\n",
       "      <td>2021</td>\n",
       "      <td>39.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1127</th>\n",
       "      <td>Vietnam</td>\n",
       "      <td>2021</td>\n",
       "      <td>61.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1128</th>\n",
       "      <td>Yemen</td>\n",
       "      <td>2021</td>\n",
       "      <td>35.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1129</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>2021</td>\n",
       "      <td>38.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1130 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               0     1     2\n",
       "0        Algeria  2012  53.2\n",
       "1         Angola  2012  40.1\n",
       "2      Argentina  2012  61.0\n",
       "3      Australia  2012  70.9\n",
       "4        Austria  2012  79.6\n",
       "...          ...   ...   ...\n",
       "1125  Uzbekistan  2021  53.8\n",
       "1126   Venezuela  2021  39.4\n",
       "1127     Vietnam  2021  61.1\n",
       "1128       Yemen  2021  35.7\n",
       "1129      Zambia  2021  38.0\n",
       "\n",
       "[1130 rows x 3 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#check the result\n",
    "display(fi_final)\n",
    "\n",
    "#Renaming column names\n",
    "fi_final = fi_final.rename(columns={0: 'Country', 1 : \"Year\", 2 : \"GFSI_Index\"})  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "52ce6052",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Country       object\n",
       "Year          object\n",
       "GFSI_Index    object\n",
       "dtype: object"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check data types \n",
    "fi_final.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "4ee0bc28",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "#Change year to int type, this is done so that we can merge dataframes easily later\n",
    "fi_final[\"Year\"] = fi_final[\"Year\"].astype(str).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "04a05ef1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "46973264",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "This is the target variable dataframe <br> <br> \n",
    "#### Let us now scrape each predictor variable"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15d99811",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "##### Obtaining World Bank Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "b9775f7e",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "  id  value\n",
       "----  -------------------------------\n",
       "   1  Agriculture & Rural Development\n",
       "   2  Aid Effectiveness\n",
       "   3  Economy & Growth\n",
       "   4  Education\n",
       "   5  Energy & Mining\n",
       "   6  Environment\n",
       "   7  Financial Sector\n",
       "   8  Health\n",
       "   9  Infrastructure\n",
       "  10  Social Protection & Labor\n",
       "  11  Poverty\n",
       "  12  Private Sector\n",
       "  13  Public Sector\n",
       "  14  Science & Technology\n",
       "  15  Social Development\n",
       "  16  Urban Development\n",
       "  17  Gender\n",
       "  18  Millenium development goals\n",
       "  19  Climate Change\n",
       "  20  External Debt\n",
       "  21  Trade"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking the topics in world bank data\n",
    "wbdata.get_topic()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "660550e4",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                     name\n",
       "---------------------  -----------------------------------------------------------------------------------------------------------------\n",
       "1.0.HCount.1.90usd     Poverty Headcount ($1.90 a day)\n",
       "1.0.HCount.2.5usd      Poverty Headcount ($2.50 a day)\n",
       "1.0.HCount.Mid10to50   Middle Class ($10-50 a day) Headcount\n",
       "1.0.HCount.Ofcl        Official Moderate Poverty Rate-National\n",
       "1.0.HCount.Poor4uds    Poverty Headcount ($4 a day)\n",
       "1.0.HCount.Vul4to10    Vulnerable ($4-10 a day) Headcount\n",
       "1.0.PGap.1.90usd       Poverty Gap ($1.90 a day)\n",
       "1.0.PGap.2.5usd        Poverty Gap ($2.50 a day)\n",
       "1.0.PGap.Poor4uds      Poverty Gap ($4 a day)\n",
       "1.0.PSev.1.90usd       Poverty Severity ($1.90 a day)\n",
       "1.0.PSev.2.5usd        Poverty Severity ($2.50 a day)\n",
       "1.0.PSev.Poor4uds      Poverty Severity ($4 a day)\n",
       "1.1.HCount.1.90usd     Poverty Headcount ($1.90 a day)-Rural\n",
       "1.1.HCount.2.5usd      Poverty Headcount ($2.50 a day)-Rural\n",
       "1.1.HCount.Mid10to50   Middle Class ($10-50 a day) Headcount-Rural\n",
       "1.1.HCount.Ofcl        Official Moderate Poverty Rate- Rural\n",
       "1.1.HCount.Poor4uds    Poverty Headcount ($4 a day)-Rural\n",
       "1.1.HCount.Vul4to10    Vulnerable ($4-10 a day) Headcount-Rural\n",
       "1.1.PGap.1.90usd       Poverty Gap ($1.90 a day)-Rural\n",
       "1.1.PGap.2.5usd        Poverty Gap ($2.50 a day)-Rural\n",
       "1.1.PGap.Poor4uds      Poverty Gap ($4 a day)-Rural\n",
       "1.1.PSev.1.90usd       Poverty Severity ($1.90 a day)-Rural\n",
       "1.1.PSev.2.5usd        Poverty Severity ($2.50 a day)-Rural\n",
       "1.1.PSev.Poor4uds      Poverty Severity ($4 a day)-Rural\n",
       "1.2.HCount.1.90usd     Poverty Headcount ($1.90 a day)-Urban\n",
       "1.2.HCount.2.5usd      Poverty Headcount ($2.50 a day)-Urban\n",
       "1.2.HCount.Mid10to50   Middle Class ($10-50 a day) Headcount-Urban\n",
       "1.2.HCount.Ofcl        Official Moderate Poverty Rate-Urban\n",
       "1.2.HCount.Poor4uds    Poverty Headcount ($4 a day)-Urban\n",
       "1.2.HCount.Vul4to10    Vulnerable ($4-10 a day) Headcount-Urban\n",
       "1.2.PGap.1.90usd       Poverty Gap ($1.90 a day)-Urban\n",
       "1.2.PGap.2.5usd        Poverty Gap ($2.50 a day)-Urban\n",
       "1.2.PGap.Poor4uds      Poverty Gap ($4 a day)-Urban\n",
       "1.2.PSev.1.90usd       Poverty Severity ($1.90 a day)-Urban\n",
       "1.2.PSev.2.5usd        Poverty Severity ($2.50 a day)-Urban\n",
       "1.2.PSev.Poor4uds      Poverty Severity ($4 a day)-Urban\n",
       "2.0.cov.Cel            Coverage: Mobile Phone\n",
       "2.0.cov.Ele            Coverage: Electricity\n",
       "2.0.cov.Int            Coverage: Internet\n",
       "2.0.cov.Math.pl_2.all  Coverage: Mathematics Proficiency Level 2\n",
       "2.0.cov.Math.pl_2.prv  Coverage: Mathematics Proficiency Level 2, Private schools\n",
       "2.0.cov.Math.pl_2.pub  Coverage: Mathematics Proficiency Level 2, Public schools\n",
       "2.0.cov.Math.pl_3.all  Coverage: Mathematics Proficiency Level 3\n",
       "2.0.cov.Math.pl_3.prv  Coverage: Mathematics Proficiency Level 3, Private schools\n",
       "2.0.cov.Math.pl_3.pub  Coverage: Mathematics Proficiency Level 3, Public schools\n",
       "2.0.cov.Read.pl_2.all  Coverage: Reading Proficiency Level 2\n",
       "2.0.cov.Read.pl_2.prv  Coverage: Reading Proficiency Level 2, Private schools\n",
       "2.0.cov.Read.pl_2.pub  Coverage: Reading Proficiency Level 2, Public schools\n",
       "2.0.cov.Read.pl_3.all  Coverage: Reading Proficiency Level 3\n",
       "2.0.cov.Read.pl_3.prv  Coverage: Reading Proficiency Level 3, Private schools\n",
       "2.0.cov.Read.pl_3.pub  Coverage: Reading Proficiency Level 3, Public schools\n",
       "2.0.cov.San            Coverage: Sanitation\n",
       "2.0.cov.Sch            Coverage: School Enrollment\n",
       "2.0.cov.Scie.pl_2.all  Coverage: Science Proficiency Level 2\n",
       "2.0.cov.Scie.pl_2.prv  Coverage: Science Proficiency Level 2, Private schools\n",
       "2.0.cov.Scie.pl_2.pub  Coverage: Science Proficiency Level 2, Public schools\n",
       "2.0.cov.Scie.pl_3.all  Coverage: Science Proficiency Level 3\n",
       "2.0.cov.Scie.pl_3.prv  Coverage: Science Proficiency Level 3, Private schools\n",
       "2.0.cov.Scie.pl_3.pub  Coverage: Science Proficiency Level 3, Public schools\n",
       "2.0.cov.Wat            Coverage: Water\n",
       "2.0.hoi.Cel            HOI: Mobile Phone\n",
       "2.0.hoi.Ele            HOI: Electricity\n",
       "2.0.hoi.Int            HOI: Internet\n",
       "2.0.hoi.Math.pl_2.all  HOI: Mathematics Proficiency Level 2\n",
       "2.0.hoi.Math.pl_2.prv  HOI: Mathematics Proficiency Level 2, Private schools\n",
       "2.0.hoi.Math.pl_2.pub  HOI: Mathematics Proficiency Level 2, Public schools\n",
       "2.0.hoi.Math.pl_3.all  HOI: Mathematics Proficiency Level 3\n",
       "2.0.hoi.Math.pl_3.prv  HOI: Mathematics Proficiency Level 3, Private schools\n",
       "2.0.hoi.Math.pl_3.pub  HOI: Mathematics Proficiency Level 3, Public schools\n",
       "2.0.hoi.Read.pl_2.all  HOI: Reading Proficiency Level 2\n",
       "2.0.hoi.Read.pl_2.prv  HOI: Reading Proficiency Level 2, Private schools\n",
       "2.0.hoi.Read.pl_2.pub  HOI: Reading Proficiency Level 2, Public schools\n",
       "2.0.hoi.Read.pl_3.all  HOI: Reading Proficiency Level 3\n",
       "2.0.hoi.Read.pl_3.prv  HOI: Reading Proficiency Level 3, Private schools\n",
       "2.0.hoi.Read.pl_3.pub  HOI: Reading Proficiency Level 3, Public schools\n",
       "2.0.hoi.San            HOI: Sanitation\n",
       "2.0.hoi.Sch            HOI: School Enrollment\n",
       "2.0.hoi.Scie.pl_2.all  HOI: Science Proficiency Level 2\n",
       "2.0.hoi.Scie.pl_2.prv  HOI: Science Proficiency Level 2, Private schools\n",
       "2.0.hoi.Scie.pl_2.pub  HOI: Science Proficiency Level 2, Public schools\n",
       "2.0.hoi.Scie.pl_3.all  HOI: Science Proficiency Level 3\n",
       "2.0.hoi.Scie.pl_3.prv  HOI: Science Proficiency Level 3, Private schools\n",
       "2.0.hoi.Scie.pl_3.pub  HOI: Science Proficiency Level 3, Public schools\n",
       "2.0.hoi.Wat            HOI: Water\n",
       "3.0.Atkin.0.5          Atkinson, A(.5)\n",
       "3.0.Atkin.1            Atkinson, A(1)\n",
       "3.0.Atkin.2            Atkinson, A(2)\n",
       "3.0.GenEnt-1           Generalized Entrophy, GE(-1)\n",
       "3.0.GenEnt2            Generalized Entrophy, GE(2)\n",
       "3.0.Gini               Gini Coefficient\n",
       "3.0.Gini_nozero        Gini Coefficient (No Zero Income)\n",
       "3.0.IncShr.q1          Income Share of First Quintile\n",
       "3.0.IncShr.q2          Income Share of Second Quintile\n",
       "3.0.IncShr.q3          Income Share of Third Quintile\n",
       "3.0.IncShr.q4          Income Share of Fourth Quintile\n",
       "3.0.IncShr.q5          Income Share of Fifth Quintile\n",
       "3.0.MLongDev0          Mean Log Deviation, GE(0)\n",
       "3.0.Rate75-25          Rate 75/25\n",
       "3.0.Rate90-10          Rate 90/10\n",
       "3.0.TheilInd1          Theil Index, GE(1)\n",
       "3.1.Gini               Gini, Rural\n",
       "3.1.MLongDev0          Mean Log Deviation, GE(0), Rural\n",
       "3.1.TheilInd1          Theil Index, GE(1), Rural\n",
       "3.2.Gini               Gini, Urban\n",
       "3.2.MLongDev0          Mean Log Deviation, GE(0),Urban\n",
       "3.2.TheilInd1          Theil Index, GE(1),Urban\n",
       "8.0.LIPI               Labor Income Poverty Index\n",
       "EN.POP.SLUM.UR.ZS      Population living in slums (% of urban population)\n",
       "SI.DST.02ND.20         Income share held by second 20%\n",
       "SI.DST.03RD.20         Income share held by third 20%\n",
       "SI.DST.04TH.20         Income share held by fourth 20%\n",
       "SI.DST.05TH.20         Income share held by highest 20%\n",
       "SI.DST.10TH.10         Income share held by highest 10%\n",
       "SI.DST.50MD            Proportion of people living below 50 percent of median income (%)\n",
       "SI.DST.FRST.10         Income share held by lowest 10%\n",
       "SI.DST.FRST.20         Income share held by lowest 20%\n",
       "SI.POV.2DAY            Poverty headcount ratio at $3.10 a day (2011 PPP) (% of population)\n",
       "SI.POV.DDAY            Poverty headcount ratio at $1.90 a day (2011 PPP) (% of population)\n",
       "SI.POV.GAP2            Poverty gap at $3.10 a day (2011 PPP) (%)\n",
       "SI.POV.GAPS            Poverty gap at $1.90 a day (2011 PPP) (%)\n",
       "SI.POV.GINI            Gini index (World Bank estimate)\n",
       "SI.POV.LMIC            Poverty headcount ratio at $3.20 a day (2011 PPP) (% of population)\n",
       "SI.POV.LMIC.GP         Poverty gap at $3.20 a day (2011 PPP) (%)\n",
       "SI.POV.MDIM            Multidimensional poverty headcount ratio (% of total population)\n",
       "SI.POV.MDIM.17         Multidimensional poverty headcount ratio, children (% of population ages 0-17)\n",
       "SI.POV.MDIM.17.XQ      Multidimensional poverty index, children (population ages 0-17) (scale 0-1)\n",
       "SI.POV.MDIM.FE         Multidimensional poverty headcount ratio, female (% of female population)\n",
       "SI.POV.MDIM.HH         Multidimensional poverty headcount ratio, household (% of total households)\n",
       "SI.POV.MDIM.IT         Multidimensional poverty intensity (average share of deprivations experienced by the poor)\n",
       "SI.POV.MDIM.MA         Multidimensional poverty headcount ratio, male (% of male population)\n",
       "SI.POV.MDIM.XQ         Multidimensional poverty index (scale 0-1)\n",
       "SI.POV.NAGP            Poverty gap at national poverty lines (%)\n",
       "SI.POV.NAHC            Poverty headcount ratio at national poverty lines (% of population)\n",
       "SI.POV.RUGP            Rural poverty gap at national poverty lines (%)\n",
       "SI.POV.RUHC            Rural poverty headcount ratio at national poverty lines (% of rural population)\n",
       "SI.POV.UMIC            Poverty headcount ratio at $5.50 a day (2011 PPP) (% of population)\n",
       "SI.POV.UMIC.GP         Poverty gap at $5.50 a day (2011 PPP) (%)\n",
       "SI.POV.URGP            Urban poverty gap at national poverty lines (%)\n",
       "SI.POV.URHC            Urban poverty headcount ratio at national poverty lines (% of urban population)\n",
       "SI.SPR.PC40            Survey mean consumption or income per capita, bottom 40% of population (2011 PPP $ per day)\n",
       "SI.SPR.PC40.05         Survey mean consumption or income per capita, bottom 40% (2005 PPP $ per day)\n",
       "SI.SPR.PC40.ZG         Annualized average growth rate in per capita real survey mean consumption or income, bottom 40% of population (%)\n",
       "SI.SPR.PCAP            Survey mean consumption or income per capita, total population (2011 PPP $ per day)\n",
       "SI.SPR.PCAP.05         Survey mean consumption or income per capita, total population (2005 PPP $ per day)\n",
       "SI.SPR.PCAP.ZG         Annualized average growth rate in per capita real survey mean consumption or income, total population (%)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Viewing indicators that are required\n",
    "wbdata.get_indicator(topic=11)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "66e6bba4",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "#Creating a dictionary of the required indicators\n",
    "indicators = {\n",
    "    \"AG.LND.ARBL.HA.PC\" : \"Arable Land in Hectares per person\",\n",
    "    \"AG.YLD.CREL.KG\" :\"Cereal yield (kg/hectare)\",\n",
    "    \"SL.EMP.WORK.ZS\" : \"Wage and Salaried Employees\"\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "5c332f9c",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "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></th>\n",
       "      <th>Arable Land in Hectares per person</th>\n",
       "      <th>Cereal yield (kg/hectare)</th>\n",
       "      <th>Wage and Salaried Employees</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Africa Eastern and Southern</th>\n",
       "      <th>2020</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>27.844263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>0.184952</td>\n",
       "      <td>1622.882922</td>\n",
       "      <td>27.755236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>0.189571</td>\n",
       "      <td>1728.818670</td>\n",
       "      <td>27.416973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>0.195264</td>\n",
       "      <td>1498.714700</td>\n",
       "      <td>27.138512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Zimbabwe</th>\n",
       "      <th>1964</th>\n",
       "      <td>0.470754</td>\n",
       "      <td>820.500000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1963</th>\n",
       "      <td>0.475025</td>\n",
       "      <td>822.500000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962</th>\n",
       "      <td>0.479054</td>\n",
       "      <td>905.900000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961</th>\n",
       "      <td>0.482710</td>\n",
       "      <td>919.700000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>16226 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  Arable Land in Hectares per person  \\\n",
       "country                     date                                       \n",
       "Africa Eastern and Southern 2020                                 NaN   \n",
       "                            2019                                 NaN   \n",
       "                            2018                            0.184952   \n",
       "                            2017                            0.189571   \n",
       "                            2016                            0.195264   \n",
       "...                                                              ...   \n",
       "Zimbabwe                    1964                            0.470754   \n",
       "                            1963                            0.475025   \n",
       "                            1962                            0.479054   \n",
       "                            1961                            0.482710   \n",
       "                            1960                                 NaN   \n",
       "\n",
       "                                  Cereal yield (kg/hectare)  \\\n",
       "country                     date                              \n",
       "Africa Eastern and Southern 2020                        NaN   \n",
       "                            2019                        NaN   \n",
       "                            2018                1622.882922   \n",
       "                            2017                1728.818670   \n",
       "                            2016                1498.714700   \n",
       "...                                                     ...   \n",
       "Zimbabwe                    1964                 820.500000   \n",
       "                            1963                 822.500000   \n",
       "                            1962                 905.900000   \n",
       "                            1961                 919.700000   \n",
       "                            1960                        NaN   \n",
       "\n",
       "                                  Wage and Salaried Employees  \n",
       "country                     date                               \n",
       "Africa Eastern and Southern 2020                          NaN  \n",
       "                            2019                    27.844263  \n",
       "                            2018                    27.755236  \n",
       "                            2017                    27.416973  \n",
       "                            2016                    27.138512  \n",
       "...                                                       ...  \n",
       "Zimbabwe                    1964                          NaN  \n",
       "                            1963                          NaN  \n",
       "                            1962                          NaN  \n",
       "                            1961                          NaN  \n",
       "                            1960                          NaN  \n",
       "\n",
       "[16226 rows x 3 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Storing all the data in a dataframe\n",
    "df_WB=wbdata.get_dataframe(indicators,country=\"all\")\n",
    "#Checking the dataframe\n",
    "display(df_WB)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "5a43c53b",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "#creates a copy of dataframe\n",
    "df_wb=df_WB.copy() \n",
    "#Resetting the index\n",
    "df_wb.reset_index(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "55a4cb8c",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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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>date</th>\n",
       "      <th>Arable Land in Hectares per person</th>\n",
       "      <th>Cereal yield (kg/hectare)</th>\n",
       "      <th>Wage and Salaried Employees</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>14018</th>\n",
       "      <td>Sri Lanka</td>\n",
       "      <td>1971</td>\n",
       "      <td>0.064011</td>\n",
       "      <td>1939.200000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16213</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>1972</td>\n",
       "      <td>0.425632</td>\n",
       "      <td>1637.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5404</th>\n",
       "      <td>Channel Islands</td>\n",
       "      <td>1984</td>\n",
       "      <td>0.028691</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15543</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>1971</td>\n",
       "      <td>0.127594</td>\n",
       "      <td>3926.200000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2316</th>\n",
       "      <td>Other small states</td>\n",
       "      <td>1961</td>\n",
       "      <td>0.384098</td>\n",
       "      <td>736.960363</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1734</th>\n",
       "      <td>Low income</td>\n",
       "      <td>1994</td>\n",
       "      <td>0.270646</td>\n",
       "      <td>1037.210266</td>\n",
       "      <td>14.290640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8323</th>\n",
       "      <td>Hungary</td>\n",
       "      <td>1993</td>\n",
       "      <td>0.458411</td>\n",
       "      <td>3085.300000</td>\n",
       "      <td>81.889999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13274</th>\n",
       "      <td>Serbia</td>\n",
       "      <td>1983</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7628</th>\n",
       "      <td>Greece</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.198797</td>\n",
       "      <td>4317.200000</td>\n",
       "      <td>65.930000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10185</th>\n",
       "      <td>Macao SAR, China</td>\n",
       "      <td>1961</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  country  date  Arable Land in Hectares per person  \\\n",
       "14018           Sri Lanka  1971                            0.064011   \n",
       "16213            Zimbabwe  1972                            0.425632   \n",
       "5404      Channel Islands  1984                            0.028691   \n",
       "15543      United Kingdom  1971                            0.127594   \n",
       "2316   Other small states  1961                            0.384098   \n",
       "1734           Low income  1994                            0.270646   \n",
       "8323              Hungary  1993                            0.458411   \n",
       "13274              Serbia  1983                                 NaN   \n",
       "7628               Greece  2017                            0.198797   \n",
       "10185    Macao SAR, China  1961                                 NaN   \n",
       "\n",
       "       Cereal yield (kg/hectare)  Wage and Salaried Employees  \n",
       "14018                1939.200000                          NaN  \n",
       "16213                1637.000000                          NaN  \n",
       "5404                         NaN                          NaN  \n",
       "15543                3926.200000                          NaN  \n",
       "2316                  736.960363                          NaN  \n",
       "1734                 1037.210266                    14.290640  \n",
       "8323                 3085.300000                    81.889999  \n",
       "13274                        NaN                          NaN  \n",
       "7628                 4317.200000                    65.930000  \n",
       "10185                        NaN                          NaN  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#To check result\n",
    "df_wb.sample(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "2252bc1e",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>Year</th>\n",
       "      <th>Arable Land in Hectares per person</th>\n",
       "      <th>Cereal yield (kg/hectare)</th>\n",
       "      <th>Wage and Salaried Employees</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Africa Eastern and Southern</td>\n",
       "      <td>2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Africa Eastern and Southern</td>\n",
       "      <td>2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>27.844263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Africa Eastern and Southern</td>\n",
       "      <td>2018</td>\n",
       "      <td>0.184952</td>\n",
       "      <td>1622.882922</td>\n",
       "      <td>27.755236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Africa Eastern and Southern</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.189571</td>\n",
       "      <td>1728.818670</td>\n",
       "      <td>27.416973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Africa Eastern and Southern</td>\n",
       "      <td>2016</td>\n",
       "      <td>0.195264</td>\n",
       "      <td>1498.714700</td>\n",
       "      <td>27.138512</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "                       country  Year  Arable Land in Hectares per person  \\\n",
       "0  Africa Eastern and Southern  2020                                 NaN   \n",
       "1  Africa Eastern and Southern  2019                                 NaN   \n",
       "2  Africa Eastern and Southern  2018                            0.184952   \n",
       "3  Africa Eastern and Southern  2017                            0.189571   \n",
       "4  Africa Eastern and Southern  2016                            0.195264   \n",
       "\n",
       "   Cereal yield (kg/hectare)  Wage and Salaried Employees  \n",
       "0                        NaN                          NaN  \n",
       "1                        NaN                    27.844263  \n",
       "2                1622.882922                    27.755236  \n",
       "3                1728.818670                    27.416973  \n",
       "4                1498.714700                    27.138512  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#change column name\n",
    "df_wb = df_wb.rename(columns={'date': 'Year'})\n",
    "df_wb.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "9be9aa5e",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Year\n",
       "1960    266\n",
       "1961    266\n",
       "1962    266\n",
       "1963    266\n",
       "1964    266\n",
       "       ... \n",
       "2016    266\n",
       "2017    266\n",
       "2018    266\n",
       "2019    266\n",
       "2020    266\n",
       "Length: 61, dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking the number of observations for each year\n",
    "df_wb.groupby(\"Year\").size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "4dbccc90",
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "country                                object\n",
       "Year                                   object\n",
       "Arable Land in Hectares per person    float64\n",
       "Cereal yield (kg/hectare)             float64\n",
       "Wage and Salaried Employees           float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking data types\n",
    "df_wb.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "fb3d462e",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "#Converting year to int type, so that it will be easy to merge later\n",
    "df_wb[\"year\"] = df_wb[\"Year\"].astype(str).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "186de1de",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>Year</th>\n",
       "      <th>Arable Land in Hectares per person</th>\n",
       "      <th>Cereal yield (kg/hectare)</th>\n",
       "      <th>Wage and Salaried Employees</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Africa Eastern and Southern</td>\n",
       "      <td>2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Africa Eastern and Southern</td>\n",
       "      <td>2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>27.844263</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Africa Eastern and Southern</td>\n",
       "      <td>2018</td>\n",
       "      <td>0.184952</td>\n",
       "      <td>1622.882922</td>\n",
       "      <td>27.755236</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Africa Eastern and Southern</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.189571</td>\n",
       "      <td>1728.818670</td>\n",
       "      <td>27.416973</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Africa Eastern and Southern</td>\n",
       "      <td>2016</td>\n",
       "      <td>0.195264</td>\n",
       "      <td>1498.714700</td>\n",
       "      <td>27.138512</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16221</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>1964</td>\n",
       "      <td>0.470754</td>\n",
       "      <td>820.500000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16222</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>1963</td>\n",
       "      <td>0.475025</td>\n",
       "      <td>822.500000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16223</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>1962</td>\n",
       "      <td>0.479054</td>\n",
       "      <td>905.900000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16224</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>1961</td>\n",
       "      <td>0.482710</td>\n",
       "      <td>919.700000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16225</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>1960</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1960</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>16226 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                           country  Year  Arable Land in Hectares per person  \\\n",
       "0      Africa Eastern and Southern  2020                                 NaN   \n",
       "1      Africa Eastern and Southern  2019                                 NaN   \n",
       "2      Africa Eastern and Southern  2018                            0.184952   \n",
       "3      Africa Eastern and Southern  2017                            0.189571   \n",
       "4      Africa Eastern and Southern  2016                            0.195264   \n",
       "...                            ...   ...                                 ...   \n",
       "16221                     Zimbabwe  1964                            0.470754   \n",
       "16222                     Zimbabwe  1963                            0.475025   \n",
       "16223                     Zimbabwe  1962                            0.479054   \n",
       "16224                     Zimbabwe  1961                            0.482710   \n",
       "16225                     Zimbabwe  1960                                 NaN   \n",
       "\n",
       "       Cereal yield (kg/hectare)  Wage and Salaried Employees  year  \n",
       "0                            NaN                          NaN  2020  \n",
       "1                            NaN                    27.844263  2019  \n",
       "2                    1622.882922                    27.755236  2018  \n",
       "3                    1728.818670                    27.416973  2017  \n",
       "4                    1498.714700                    27.138512  2016  \n",
       "...                          ...                          ...   ...  \n",
       "16221                 820.500000                          NaN  1964  \n",
       "16222                 822.500000                          NaN  1963  \n",
       "16223                 905.900000                          NaN  1962  \n",
       "16224                 919.700000                          NaN  1961  \n",
       "16225                        NaN                          NaN  1960  \n",
       "\n",
       "[16226 rows x 6 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Check result\n",
    "display(df_wb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "7b5f5ddc",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "#only including data from 2012\n",
    "df_wb = df_wb[df_wb.year>2011]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "4cd9a44e",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "#Dropping the column not required\n",
    "df_wb = df_wb.drop([\"Year\"], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "2cc82e5c",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:country_converter.country_converter:Africa Eastern and Southern not found in regex\n",
      "WARNING:country_converter.country_converter:Africa Eastern and Southern not found in regex\n",
      "WARNING:country_converter.country_converter:Africa Eastern and Southern not found in regex\n",
      "WARNING:country_converter.country_converter:Africa Eastern and Southern not found in regex\n",
      "WARNING:country_converter.country_converter:Africa Eastern and Southern not found in regex\n",
      "WARNING:country_converter.country_converter:Africa Eastern and Southern not found in regex\n",
      "WARNING:country_converter.country_converter:Africa Eastern and Southern not found in regex\n",
      "WARNING:country_converter.country_converter:Africa Eastern and Southern not found in regex\n",
      "WARNING:country_converter.country_converter:Africa Western and Central not found in regex\n",
      "WARNING:country_converter.country_converter:Africa Western and Central not found in regex\n",
      "WARNING:country_converter.country_converter:Africa Western and Central not found in regex\n",
      "WARNING:country_converter.country_converter:Africa Western and Central not found in regex\n",
      "WARNING:country_converter.country_converter:Africa Western and Central not found in regex\n",
      "WARNING:country_converter.country_converter:Africa Western and Central not found in regex\n",
      "WARNING:country_converter.country_converter:Africa Western and Central not found in regex\n",
      "WARNING:country_converter.country_converter:Africa Western and Central not found in regex\n",
      "WARNING:country_converter.country_converter:Africa Western and Central not found in regex\n",
      "WARNING:country_converter.country_converter:Arab World not found in regex\n",
      "WARNING:country_converter.country_converter:Arab World not found in regex\n",
      "WARNING:country_converter.country_converter:Arab World not found in regex\n",
      "WARNING:country_converter.country_converter:Arab World not found in regex\n",
      "WARNING:country_converter.country_converter:Arab World not found in regex\n",
      "WARNING:country_converter.country_converter:Arab World not found in regex\n",
      "WARNING:country_converter.country_converter:Arab World not found in regex\n",
      "WARNING:country_converter.country_converter:Arab World not found in regex\n",
      "WARNING:country_converter.country_converter:Arab World not found in regex\n",
      "WARNING:country_converter.country_converter:Caribbean small states not found in regex\n",
      "WARNING:country_converter.country_converter:Caribbean small states not found in regex\n",
      "WARNING:country_converter.country_converter:Caribbean small states not found in regex\n",
      "WARNING:country_converter.country_converter:Caribbean small states not found in regex\n",
      "WARNING:country_converter.country_converter:Caribbean small states not found in regex\n",
      "WARNING:country_converter.country_converter:Caribbean small states not found in regex\n",
      "WARNING:country_converter.country_converter:Caribbean small states not found in regex\n",
      "WARNING:country_converter.country_converter:Caribbean small states not found in regex\n",
      "WARNING:country_converter.country_converter:Caribbean small states not found in regex\n",
      "WARNING:country_converter.country_converter:Central Europe and the Baltics not found in regex\n",
      "WARNING:country_converter.country_converter:Central Europe and the Baltics not found in regex\n",
      "WARNING:country_converter.country_converter:Central Europe and the Baltics not found in regex\n",
      "WARNING:country_converter.country_converter:Central Europe and the Baltics not found in regex\n",
      "WARNING:country_converter.country_converter:Central Europe and the Baltics not found in regex\n",
      "WARNING:country_converter.country_converter:Central Europe and the Baltics not found in regex\n",
      "WARNING:country_converter.country_converter:Central Europe and the Baltics not found in regex\n",
      "WARNING:country_converter.country_converter:Central Europe and the Baltics not found in regex\n",
      "WARNING:country_converter.country_converter:Central Europe and the Baltics not found in regex\n",
      "WARNING:country_converter.country_converter:Early-demographic dividend not found in regex\n",
      "WARNING:country_converter.country_converter:Early-demographic dividend not found in regex\n",
      "WARNING:country_converter.country_converter:Early-demographic dividend not found in regex\n",
      "WARNING:country_converter.country_converter:Early-demographic dividend not found in regex\n",
      "WARNING:country_converter.country_converter:Early-demographic dividend not found in regex\n",
      "WARNING:country_converter.country_converter:Early-demographic dividend not found in regex\n",
      "WARNING:country_converter.country_converter:Early-demographic dividend not found in regex\n",
      "WARNING:country_converter.country_converter:Early-demographic dividend not found in regex\n",
      "WARNING:country_converter.country_converter:Early-demographic dividend not found in regex\n",
      "WARNING:country_converter.country_converter:East Asia & Pacific not found in regex\n",
      "WARNING:country_converter.country_converter:East Asia & Pacific not found in regex\n",
      "WARNING:country_converter.country_converter:East Asia & Pacific not found in regex\n",
      "WARNING:country_converter.country_converter:East Asia & Pacific not found in regex\n",
      "WARNING:country_converter.country_converter:East Asia & Pacific not found in regex\n",
      "WARNING:country_converter.country_converter:East Asia & Pacific not found in regex\n",
      "WARNING:country_converter.country_converter:East Asia & Pacific not found in regex\n",
      "WARNING:country_converter.country_converter:East Asia & Pacific not found in regex\n",
      "WARNING:country_converter.country_converter:East Asia & Pacific not found in regex\n",
      "WARNING:country_converter.country_converter:East Asia & Pacific ( not found in regex\n",
      "WARNING:country_converter.country_converter:East Asia & Pacific ( not found in regex\n",
      "WARNING:country_converter.country_converter:East Asia & Pacific ( not found in regex\n",
      "WARNING:country_converter.country_converter:East Asia & Pacific ( not found in regex\n",
      "WARNING:country_converter.country_converter:East Asia & Pacific ( not found in regex\n",
      "WARNING:country_converter.country_converter:East Asia & Pacific ( not found in regex\n",
      "WARNING:country_converter.country_converter:East Asia & Pacific ( not found in regex\n",
      "WARNING:country_converter.country_converter:East Asia & Pacific ( not found in regex\n",
      "WARNING:country_converter.country_converter:East Asia & Pacific ( not found in regex\n",
      "WARNING:country_converter.country_converter:East Asia & Pacific (IDA & IBRD countries) not found in regex\n",
      "WARNING:country_converter.country_converter:East Asia & Pacific (IDA & IBRD countries) not found in regex\n",
      "WARNING:country_converter.country_converter:East Asia & Pacific (IDA & IBRD countries) not found in regex\n",
      "WARNING:country_converter.country_converter:East Asia & Pacific (IDA & IBRD countries) not found in regex\n",
      "WARNING:country_converter.country_converter:East Asia & Pacific (IDA & IBRD countries) not found in regex\n",
      "WARNING:country_converter.country_converter:East Asia & Pacific (IDA & IBRD countries) not found in regex\n",
      "WARNING:country_converter.country_converter:East Asia & Pacific (IDA & IBRD countries) not found in regex\n",
      "WARNING:country_converter.country_converter:East Asia & Pacific (IDA & IBRD countries) not found in regex\n",
      "WARNING:country_converter.country_converter:East Asia & Pacific (IDA & IBRD countries) not found in regex\n",
      "WARNING:country_converter.country_converter:Euro area not found in regex\n",
      "WARNING:country_converter.country_converter:Euro area not found in regex\n",
      "WARNING:country_converter.country_converter:Euro area not found in regex\n",
      "WARNING:country_converter.country_converter:Euro area not found in regex\n",
      "WARNING:country_converter.country_converter:Euro area not found in regex\n",
      "WARNING:country_converter.country_converter:Euro area not found in regex\n",
      "WARNING:country_converter.country_converter:Euro area not found in regex\n",
      "WARNING:country_converter.country_converter:Euro area not found in regex\n",
      "WARNING:country_converter.country_converter:Euro area not found in regex\n",
      "WARNING:country_converter.country_converter:Europe & Central Asia not found in regex\n",
      "WARNING:country_converter.country_converter:Europe & Central Asia not found in regex\n",
      "WARNING:country_converter.country_converter:Europe & Central Asia not found in regex\n",
      "WARNING:country_converter.country_converter:Europe & Central Asia not found in regex\n",
      "WARNING:country_converter.country_converter:Europe & Central Asia not found in regex\n",
      "WARNING:country_converter.country_converter:Europe & Central Asia not found in regex\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:country_converter.country_converter:Europe & Central Asia not found in regex\n",
      "WARNING:country_converter.country_converter:Europe & Central Asia not found in regex\n",
      "WARNING:country_converter.country_converter:Europe & Central Asia not found in regex\n",
      "WARNING:country_converter.country_converter:Europe & Central Asia ( not found in regex\n",
      "WARNING:country_converter.country_converter:Europe & Central Asia ( not found in regex\n",
      "WARNING:country_converter.country_converter:Europe & Central Asia ( not found in regex\n",
      "WARNING:country_converter.country_converter:Europe & Central Asia ( not found in regex\n",
      "WARNING:country_converter.country_converter:Europe & Central Asia ( not found in regex\n",
      "WARNING:country_converter.country_converter:Europe & Central Asia ( not found in regex\n",
      "WARNING:country_converter.country_converter:Europe & Central Asia ( not found in regex\n",
      "WARNING:country_converter.country_converter:Europe & Central Asia ( not found in regex\n",
      "WARNING:country_converter.country_converter:Europe & Central Asia ( not found in regex\n",
      "WARNING:country_converter.country_converter:Europe & Central Asia (IDA & IBRD countries) not found in regex\n",
      "WARNING:country_converter.country_converter:Europe & Central Asia (IDA & IBRD countries) not found in regex\n",
      "WARNING:country_converter.country_converter:Europe & Central Asia (IDA & IBRD countries) not found in regex\n",
      "WARNING:country_converter.country_converter:Europe & Central Asia (IDA & IBRD countries) not found in regex\n",
      "WARNING:country_converter.country_converter:Europe & Central Asia (IDA & IBRD countries) not found in regex\n",
      "WARNING:country_converter.country_converter:Europe & Central Asia (IDA & IBRD countries) not found in regex\n",
      "WARNING:country_converter.country_converter:Europe & Central Asia (IDA & IBRD countries) not found in regex\n",
      "WARNING:country_converter.country_converter:Europe & Central Asia (IDA & IBRD countries) not found in regex\n",
      "WARNING:country_converter.country_converter:Europe & Central Asia (IDA & IBRD countries) not found in regex\n",
      "WARNING:country_converter.country_converter:European Union not found in regex\n",
      "WARNING:country_converter.country_converter:European Union not found in regex\n",
      "WARNING:country_converter.country_converter:European Union not found in regex\n",
      "WARNING:country_converter.country_converter:European Union not found in regex\n",
      "WARNING:country_converter.country_converter:European Union not found in regex\n",
      "WARNING:country_converter.country_converter:European Union not found in regex\n",
      "WARNING:country_converter.country_converter:European Union not found in regex\n",
      "WARNING:country_converter.country_converter:European Union not found in regex\n",
      "WARNING:country_converter.country_converter:European Union not found in regex\n",
      "WARNING:country_converter.country_converter:Fragile and conflict affected situations not found in regex\n",
      "WARNING:country_converter.country_converter:Fragile and conflict affected situations not found in regex\n",
      "WARNING:country_converter.country_converter:Fragile and conflict affected situations not found in regex\n",
      "WARNING:country_converter.country_converter:Fragile and conflict affected situations not found in regex\n",
      "WARNING:country_converter.country_converter:Fragile and conflict affected situations not found in regex\n",
      "WARNING:country_converter.country_converter:Fragile and conflict affected situations not found in regex\n",
      "WARNING:country_converter.country_converter:Fragile and conflict affected situations not found in regex\n",
      "WARNING:country_converter.country_converter:Fragile and conflict affected situations not found in regex\n",
      "WARNING:country_converter.country_converter:Fragile and conflict affected situations not found in regex\n",
      "WARNING:country_converter.country_converter:Heavily indebted poor countries (HIPC) not found in regex\n",
      "WARNING:country_converter.country_converter:Heavily indebted poor countries (HIPC) not found in regex\n",
      "WARNING:country_converter.country_converter:Heavily indebted poor countries (HIPC) not found in regex\n",
      "WARNING:country_converter.country_converter:Heavily indebted poor countries (HIPC) not found in regex\n",
      "WARNING:country_converter.country_converter:Heavily indebted poor countries (HIPC) not found in regex\n",
      "WARNING:country_converter.country_converter:Heavily indebted poor countries (HIPC) not found in regex\n",
      "WARNING:country_converter.country_converter:Heavily indebted poor countries (HIPC) not found in regex\n",
      "WARNING:country_converter.country_converter:Heavily indebted poor countries (HIPC) not found in regex\n",
      "WARNING:country_converter.country_converter:Heavily indebted poor countries (HIPC) not found in regex\n",
      "WARNING:country_converter.country_converter:High income not found in regex\n",
      "WARNING:country_converter.country_converter:High income not found in regex\n",
      "WARNING:country_converter.country_converter:High income not found in regex\n",
      "WARNING:country_converter.country_converter:High income not found in regex\n",
      "WARNING:country_converter.country_converter:High income not found in regex\n",
      "WARNING:country_converter.country_converter:High income not found in regex\n",
      "WARNING:country_converter.country_converter:High income not found in regex\n",
      "WARNING:country_converter.country_converter:High income not found in regex\n",
      "WARNING:country_converter.country_converter:High income not found in regex\n",
      "WARNING:country_converter.country_converter:IBRD only not found in regex\n",
      "WARNING:country_converter.country_converter:IBRD only not found in regex\n",
      "WARNING:country_converter.country_converter:IBRD only not found in regex\n",
      "WARNING:country_converter.country_converter:IBRD only not found in regex\n",
      "WARNING:country_converter.country_converter:IBRD only not found in regex\n",
      "WARNING:country_converter.country_converter:IBRD only not found in regex\n",
      "WARNING:country_converter.country_converter:IBRD only not found in regex\n",
      "WARNING:country_converter.country_converter:IBRD only not found in regex\n",
      "WARNING:country_converter.country_converter:IBRD only not found in regex\n",
      "WARNING:country_converter.country_converter:IDA & IBRD total not found in regex\n",
      "WARNING:country_converter.country_converter:IDA & IBRD total not found in regex\n",
      "WARNING:country_converter.country_converter:IDA & IBRD total not found in regex\n",
      "WARNING:country_converter.country_converter:IDA & IBRD total not found in regex\n",
      "WARNING:country_converter.country_converter:IDA & IBRD total not found in regex\n",
      "WARNING:country_converter.country_converter:IDA & IBRD total not found in regex\n",
      "WARNING:country_converter.country_converter:IDA & IBRD total not found in regex\n",
      "WARNING:country_converter.country_converter:IDA & IBRD total not found in regex\n",
      "WARNING:country_converter.country_converter:IDA & IBRD total not found in regex\n",
      "WARNING:country_converter.country_converter:IDA blend not found in regex\n",
      "WARNING:country_converter.country_converter:IDA blend not found in regex\n",
      "WARNING:country_converter.country_converter:IDA blend not found in regex\n",
      "WARNING:country_converter.country_converter:IDA blend not found in regex\n",
      "WARNING:country_converter.country_converter:IDA blend not found in regex\n",
      "WARNING:country_converter.country_converter:IDA blend not found in regex\n",
      "WARNING:country_converter.country_converter:IDA blend not found in regex\n",
      "WARNING:country_converter.country_converter:IDA blend not found in regex\n",
      "WARNING:country_converter.country_converter:IDA blend not found in regex\n",
      "WARNING:country_converter.country_converter:IDA only not found in regex\n",
      "WARNING:country_converter.country_converter:IDA only not found in regex\n",
      "WARNING:country_converter.country_converter:IDA only not found in regex\n",
      "WARNING:country_converter.country_converter:IDA only not found in regex\n",
      "WARNING:country_converter.country_converter:IDA only not found in regex\n",
      "WARNING:country_converter.country_converter:IDA only not found in regex\n",
      "WARNING:country_converter.country_converter:IDA only not found in regex\n",
      "WARNING:country_converter.country_converter:IDA only not found in regex\n",
      "WARNING:country_converter.country_converter:IDA only not found in regex\n",
      "WARNING:country_converter.country_converter:IDA total not found in regex\n",
      "WARNING:country_converter.country_converter:IDA total not found in regex\n",
      "WARNING:country_converter.country_converter:IDA total not found in regex\n",
      "WARNING:country_converter.country_converter:IDA total not found in regex\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:country_converter.country_converter:IDA total not found in regex\n",
      "WARNING:country_converter.country_converter:IDA total not found in regex\n",
      "WARNING:country_converter.country_converter:IDA total not found in regex\n",
      "WARNING:country_converter.country_converter:IDA total not found in regex\n",
      "WARNING:country_converter.country_converter:IDA total not found in regex\n",
      "WARNING:country_converter.country_converter:Late-demographic dividend not found in regex\n",
      "WARNING:country_converter.country_converter:Late-demographic dividend not found in regex\n",
      "WARNING:country_converter.country_converter:Late-demographic dividend not found in regex\n",
      "WARNING:country_converter.country_converter:Late-demographic dividend not found in regex\n",
      "WARNING:country_converter.country_converter:Late-demographic dividend not found in regex\n",
      "WARNING:country_converter.country_converter:Late-demographic dividend not found in regex\n",
      "WARNING:country_converter.country_converter:Late-demographic dividend not found in regex\n",
      "WARNING:country_converter.country_converter:Late-demographic dividend not found in regex\n",
      "WARNING:country_converter.country_converter:Late-demographic dividend not found in regex\n",
      "WARNING:country_converter.country_converter:Latin America & Caribbean not found in regex\n",
      "WARNING:country_converter.country_converter:Latin America & Caribbean not found in regex\n",
      "WARNING:country_converter.country_converter:Latin America & Caribbean not found in regex\n",
      "WARNING:country_converter.country_converter:Latin America & Caribbean not found in regex\n",
      "WARNING:country_converter.country_converter:Latin America & Caribbean not found in regex\n",
      "WARNING:country_converter.country_converter:Latin America & Caribbean not found in regex\n",
      "WARNING:country_converter.country_converter:Latin America & Caribbean not found in regex\n",
      "WARNING:country_converter.country_converter:Latin America & Caribbean not found in regex\n",
      "WARNING:country_converter.country_converter:Latin America & Caribbean not found in regex\n",
      "WARNING:country_converter.country_converter:Latin America & Caribbean ( not found in regex\n",
      "WARNING:country_converter.country_converter:Latin America & Caribbean ( not found in regex\n",
      "WARNING:country_converter.country_converter:Latin America & Caribbean ( not found in regex\n",
      "WARNING:country_converter.country_converter:Latin America & Caribbean ( not found in regex\n",
      "WARNING:country_converter.country_converter:Latin America & Caribbean ( not found in regex\n",
      "WARNING:country_converter.country_converter:Latin America & Caribbean ( not found in regex\n",
      "WARNING:country_converter.country_converter:Latin America & Caribbean ( not found in regex\n",
      "WARNING:country_converter.country_converter:Latin America & Caribbean ( not found in regex\n",
      "WARNING:country_converter.country_converter:Latin America & Caribbean ( not found in regex\n",
      "WARNING:country_converter.country_converter:Latin America & the Caribbean (IDA & IBRD countries) not found in regex\n",
      "WARNING:country_converter.country_converter:Latin America & the Caribbean (IDA & IBRD countries) not found in regex\n",
      "WARNING:country_converter.country_converter:Latin America & the Caribbean (IDA & IBRD countries) not found in regex\n",
      "WARNING:country_converter.country_converter:Latin America & the Caribbean (IDA & IBRD countries) not found in regex\n",
      "WARNING:country_converter.country_converter:Latin America & the Caribbean (IDA & IBRD countries) not found in regex\n",
      "WARNING:country_converter.country_converter:Latin America & the Caribbean (IDA & IBRD countries) not found in regex\n",
      "WARNING:country_converter.country_converter:Latin America & the Caribbean (IDA & IBRD countries) not found in regex\n",
      "WARNING:country_converter.country_converter:Latin America & the Caribbean (IDA & IBRD countries) not found in regex\n",
      "WARNING:country_converter.country_converter:Latin America & the Caribbean (IDA & IBRD countries) not found in regex\n",
      "WARNING:country_converter.country_converter:Least developed countries: UN classification not found in regex\n",
      "WARNING:country_converter.country_converter:Least developed countries: UN classification not found in regex\n",
      "WARNING:country_converter.country_converter:Least developed countries: UN classification not found in regex\n",
      "WARNING:country_converter.country_converter:Least developed countries: UN classification not found in regex\n",
      "WARNING:country_converter.country_converter:Least developed countries: UN classification not found in regex\n",
      "WARNING:country_converter.country_converter:Least developed countries: UN classification not found in regex\n",
      "WARNING:country_converter.country_converter:Least developed countries: UN classification not found in regex\n",
      "WARNING:country_converter.country_converter:Least developed countries: UN classification not found in regex\n",
      "WARNING:country_converter.country_converter:Least developed countries: UN classification not found in regex\n",
      "WARNING:country_converter.country_converter:Low & middle income not found in regex\n",
      "WARNING:country_converter.country_converter:Low & middle income not found in regex\n",
      "WARNING:country_converter.country_converter:Low & middle income not found in regex\n",
      "WARNING:country_converter.country_converter:Low & middle income not found in regex\n",
      "WARNING:country_converter.country_converter:Low & middle income not found in regex\n",
      "WARNING:country_converter.country_converter:Low & middle income not found in regex\n",
      "WARNING:country_converter.country_converter:Low & middle income not found in regex\n",
      "WARNING:country_converter.country_converter:Low & middle income not found in regex\n",
      "WARNING:country_converter.country_converter:Low & middle income not found in regex\n",
      "WARNING:country_converter.country_converter:Low income not found in regex\n",
      "WARNING:country_converter.country_converter:Low income not found in regex\n",
      "WARNING:country_converter.country_converter:Low income not found in regex\n",
      "WARNING:country_converter.country_converter:Low income not found in regex\n",
      "WARNING:country_converter.country_converter:Low income not found in regex\n",
      "WARNING:country_converter.country_converter:Low income not found in regex\n",
      "WARNING:country_converter.country_converter:Low income not found in regex\n",
      "WARNING:country_converter.country_converter:Low income not found in regex\n",
      "WARNING:country_converter.country_converter:Low income not found in regex\n",
      "WARNING:country_converter.country_converter:Lower middle income not found in regex\n",
      "WARNING:country_converter.country_converter:Lower middle income not found in regex\n",
      "WARNING:country_converter.country_converter:Lower middle income not found in regex\n",
      "WARNING:country_converter.country_converter:Lower middle income not found in regex\n",
      "WARNING:country_converter.country_converter:Lower middle income not found in regex\n",
      "WARNING:country_converter.country_converter:Lower middle income not found in regex\n",
      "WARNING:country_converter.country_converter:Lower middle income not found in regex\n",
      "WARNING:country_converter.country_converter:Lower middle income not found in regex\n",
      "WARNING:country_converter.country_converter:Lower middle income not found in regex\n",
      "WARNING:country_converter.country_converter:Middle East & North Africa not found in regex\n",
      "WARNING:country_converter.country_converter:Middle East & North Africa not found in regex\n",
      "WARNING:country_converter.country_converter:Middle East & North Africa not found in regex\n",
      "WARNING:country_converter.country_converter:Middle East & North Africa not found in regex\n",
      "WARNING:country_converter.country_converter:Middle East & North Africa not found in regex\n",
      "WARNING:country_converter.country_converter:Middle East & North Africa not found in regex\n",
      "WARNING:country_converter.country_converter:Middle East & North Africa not found in regex\n",
      "WARNING:country_converter.country_converter:Middle East & North Africa not found in regex\n",
      "WARNING:country_converter.country_converter:Middle East & North Africa not found in regex\n",
      "WARNING:country_converter.country_converter:Middle East & North Africa ( not found in regex\n",
      "WARNING:country_converter.country_converter:Middle East & North Africa ( not found in regex\n",
      "WARNING:country_converter.country_converter:Middle East & North Africa ( not found in regex\n",
      "WARNING:country_converter.country_converter:Middle East & North Africa ( not found in regex\n",
      "WARNING:country_converter.country_converter:Middle East & North Africa ( not found in regex\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:country_converter.country_converter:Middle East & North Africa ( not found in regex\n",
      "WARNING:country_converter.country_converter:Middle East & North Africa ( not found in regex\n",
      "WARNING:country_converter.country_converter:Middle East & North Africa ( not found in regex\n",
      "WARNING:country_converter.country_converter:Middle East & North Africa ( not found in regex\n",
      "WARNING:country_converter.country_converter:Middle East & North Africa (IDA & IBRD countries) not found in regex\n",
      "WARNING:country_converter.country_converter:Middle East & North Africa (IDA & IBRD countries) not found in regex\n",
      "WARNING:country_converter.country_converter:Middle East & North Africa (IDA & IBRD countries) not found in regex\n",
      "WARNING:country_converter.country_converter:Middle East & North Africa (IDA & IBRD countries) not found in regex\n",
      "WARNING:country_converter.country_converter:Middle East & North Africa (IDA & IBRD countries) not found in regex\n",
      "WARNING:country_converter.country_converter:Middle East & North Africa (IDA & IBRD countries) not found in regex\n",
      "WARNING:country_converter.country_converter:Middle East & North Africa (IDA & IBRD countries) not found in regex\n",
      "WARNING:country_converter.country_converter:Middle East & North Africa (IDA & IBRD countries) not found in regex\n",
      "WARNING:country_converter.country_converter:Middle East & North Africa (IDA & IBRD countries) not found in regex\n",
      "WARNING:country_converter.country_converter:Middle income not found in regex\n",
      "WARNING:country_converter.country_converter:Middle income not found in regex\n",
      "WARNING:country_converter.country_converter:Middle income not found in regex\n",
      "WARNING:country_converter.country_converter:Middle income not found in regex\n",
      "WARNING:country_converter.country_converter:Middle income not found in regex\n",
      "WARNING:country_converter.country_converter:Middle income not found in regex\n",
      "WARNING:country_converter.country_converter:Middle income not found in regex\n",
      "WARNING:country_converter.country_converter:Middle income not found in regex\n",
      "WARNING:country_converter.country_converter:Middle income not found in regex\n",
      "WARNING:country_converter.country_converter:North America not found in regex\n",
      "WARNING:country_converter.country_converter:North America not found in regex\n",
      "WARNING:country_converter.country_converter:North America not found in regex\n",
      "WARNING:country_converter.country_converter:North America not found in regex\n",
      "WARNING:country_converter.country_converter:North America not found in regex\n",
      "WARNING:country_converter.country_converter:North America not found in regex\n",
      "WARNING:country_converter.country_converter:North America not found in regex\n",
      "WARNING:country_converter.country_converter:North America not found in regex\n",
      "WARNING:country_converter.country_converter:North America not found in regex\n",
      "WARNING:country_converter.country_converter:Not classified not found in regex\n",
      "WARNING:country_converter.country_converter:Not classified not found in regex\n",
      "WARNING:country_converter.country_converter:Not classified not found in regex\n",
      "WARNING:country_converter.country_converter:Not classified not found in regex\n",
      "WARNING:country_converter.country_converter:Not classified not found in regex\n",
      "WARNING:country_converter.country_converter:Not classified not found in regex\n",
      "WARNING:country_converter.country_converter:Not classified not found in regex\n",
      "WARNING:country_converter.country_converter:Not classified not found in regex\n",
      "WARNING:country_converter.country_converter:Not classified not found in regex\n",
      "WARNING:country_converter.country_converter:OECD members not found in regex\n",
      "WARNING:country_converter.country_converter:OECD members not found in regex\n",
      "WARNING:country_converter.country_converter:OECD members not found in regex\n",
      "WARNING:country_converter.country_converter:OECD members not found in regex\n",
      "WARNING:country_converter.country_converter:OECD members not found in regex\n",
      "WARNING:country_converter.country_converter:OECD members not found in regex\n",
      "WARNING:country_converter.country_converter:OECD members not found in regex\n",
      "WARNING:country_converter.country_converter:OECD members not found in regex\n",
      "WARNING:country_converter.country_converter:OECD members not found in regex\n",
      "WARNING:country_converter.country_converter:Other small states not found in regex\n",
      "WARNING:country_converter.country_converter:Other small states not found in regex\n",
      "WARNING:country_converter.country_converter:Other small states not found in regex\n",
      "WARNING:country_converter.country_converter:Other small states not found in regex\n",
      "WARNING:country_converter.country_converter:Other small states not found in regex\n",
      "WARNING:country_converter.country_converter:Other small states not found in regex\n",
      "WARNING:country_converter.country_converter:Other small states not found in regex\n",
      "WARNING:country_converter.country_converter:Other small states not found in regex\n",
      "WARNING:country_converter.country_converter:Other small states not found in regex\n",
      "WARNING:country_converter.country_converter:Pacific island small states not found in regex\n",
      "WARNING:country_converter.country_converter:Pacific island small states not found in regex\n",
      "WARNING:country_converter.country_converter:Pacific island small states not found in regex\n",
      "WARNING:country_converter.country_converter:Pacific island small states not found in regex\n",
      "WARNING:country_converter.country_converter:Pacific island small states not found in regex\n",
      "WARNING:country_converter.country_converter:Pacific island small states not found in regex\n",
      "WARNING:country_converter.country_converter:Pacific island small states not found in regex\n",
      "WARNING:country_converter.country_converter:Pacific island small states not found in regex\n",
      "WARNING:country_converter.country_converter:Pacific island small states not found in regex\n",
      "WARNING:country_converter.country_converter:Post-demographic dividend not found in regex\n",
      "WARNING:country_converter.country_converter:Post-demographic dividend not found in regex\n",
      "WARNING:country_converter.country_converter:Post-demographic dividend not found in regex\n",
      "WARNING:country_converter.country_converter:Post-demographic dividend not found in regex\n",
      "WARNING:country_converter.country_converter:Post-demographic dividend not found in regex\n",
      "WARNING:country_converter.country_converter:Post-demographic dividend not found in regex\n",
      "WARNING:country_converter.country_converter:Post-demographic dividend not found in regex\n",
      "WARNING:country_converter.country_converter:Post-demographic dividend not found in regex\n",
      "WARNING:country_converter.country_converter:Post-demographic dividend not found in regex\n",
      "WARNING:country_converter.country_converter:Pre-demographic dividend not found in regex\n",
      "WARNING:country_converter.country_converter:Pre-demographic dividend not found in regex\n",
      "WARNING:country_converter.country_converter:Pre-demographic dividend not found in regex\n",
      "WARNING:country_converter.country_converter:Pre-demographic dividend not found in regex\n",
      "WARNING:country_converter.country_converter:Pre-demographic dividend not found in regex\n",
      "WARNING:country_converter.country_converter:Pre-demographic dividend not found in regex\n",
      "WARNING:country_converter.country_converter:Pre-demographic dividend not found in regex\n",
      "WARNING:country_converter.country_converter:Pre-demographic dividend not found in regex\n",
      "WARNING:country_converter.country_converter:Pre-demographic dividend not found in regex\n",
      "WARNING:country_converter.country_converter:Small states not found in regex\n",
      "WARNING:country_converter.country_converter:Small states not found in regex\n",
      "WARNING:country_converter.country_converter:Small states not found in regex\n",
      "WARNING:country_converter.country_converter:Small states not found in regex\n",
      "WARNING:country_converter.country_converter:Small states not found in regex\n",
      "WARNING:country_converter.country_converter:Small states not found in regex\n",
      "WARNING:country_converter.country_converter:Small states not found in regex\n",
      "WARNING:country_converter.country_converter:Small states not found in regex\n",
      "WARNING:country_converter.country_converter:Small states not found in regex\n",
      "WARNING:country_converter.country_converter:South Asia not found in regex\n",
      "WARNING:country_converter.country_converter:South Asia not found in regex\n",
      "WARNING:country_converter.country_converter:South Asia not found in regex\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:country_converter.country_converter:South Asia not found in regex\n",
      "WARNING:country_converter.country_converter:South Asia not found in regex\n",
      "WARNING:country_converter.country_converter:South Asia not found in regex\n",
      "WARNING:country_converter.country_converter:South Asia not found in regex\n",
      "WARNING:country_converter.country_converter:South Asia not found in regex\n",
      "WARNING:country_converter.country_converter:South Asia not found in regex\n",
      "WARNING:country_converter.country_converter:South Asia (IDA & IBRD) not found in regex\n",
      "WARNING:country_converter.country_converter:South Asia (IDA & IBRD) not found in regex\n",
      "WARNING:country_converter.country_converter:South Asia (IDA & IBRD) not found in regex\n",
      "WARNING:country_converter.country_converter:South Asia (IDA & IBRD) not found in regex\n",
      "WARNING:country_converter.country_converter:South Asia (IDA & IBRD) not found in regex\n",
      "WARNING:country_converter.country_converter:South Asia (IDA & IBRD) not found in regex\n",
      "WARNING:country_converter.country_converter:South Asia (IDA & IBRD) not found in regex\n",
      "WARNING:country_converter.country_converter:South Asia (IDA & IBRD) not found in regex\n",
      "WARNING:country_converter.country_converter:South Asia (IDA & IBRD) not found in regex\n",
      "WARNING:country_converter.country_converter:Sub-Saharan Africa not found in regex\n",
      "WARNING:country_converter.country_converter:Sub-Saharan Africa not found in regex\n",
      "WARNING:country_converter.country_converter:Sub-Saharan Africa not found in regex\n",
      "WARNING:country_converter.country_converter:Sub-Saharan Africa not found in regex\n",
      "WARNING:country_converter.country_converter:Sub-Saharan Africa not found in regex\n",
      "WARNING:country_converter.country_converter:Sub-Saharan Africa not found in regex\n",
      "WARNING:country_converter.country_converter:Sub-Saharan Africa not found in regex\n",
      "WARNING:country_converter.country_converter:Sub-Saharan Africa not found in regex\n",
      "WARNING:country_converter.country_converter:Sub-Saharan Africa not found in regex\n",
      "WARNING:country_converter.country_converter:Sub-Saharan Africa ( not found in regex\n",
      "WARNING:country_converter.country_converter:Sub-Saharan Africa ( not found in regex\n",
      "WARNING:country_converter.country_converter:Sub-Saharan Africa ( not found in regex\n",
      "WARNING:country_converter.country_converter:Sub-Saharan Africa ( not found in regex\n",
      "WARNING:country_converter.country_converter:Sub-Saharan Africa ( not found in regex\n",
      "WARNING:country_converter.country_converter:Sub-Saharan Africa ( not found in regex\n",
      "WARNING:country_converter.country_converter:Sub-Saharan Africa ( not found in regex\n",
      "WARNING:country_converter.country_converter:Sub-Saharan Africa ( not found in regex\n",
      "WARNING:country_converter.country_converter:Sub-Saharan Africa ( not found in regex\n",
      "WARNING:country_converter.country_converter:Sub-Saharan Africa (IDA & IBRD countries) not found in regex\n",
      "WARNING:country_converter.country_converter:Sub-Saharan Africa (IDA & IBRD countries) not found in regex\n",
      "WARNING:country_converter.country_converter:Sub-Saharan Africa (IDA & IBRD countries) not found in regex\n",
      "WARNING:country_converter.country_converter:Sub-Saharan Africa (IDA & IBRD countries) not found in regex\n",
      "WARNING:country_converter.country_converter:Sub-Saharan Africa (IDA & IBRD countries) not found in regex\n",
      "WARNING:country_converter.country_converter:Sub-Saharan Africa (IDA & IBRD countries) not found in regex\n",
      "WARNING:country_converter.country_converter:Sub-Saharan Africa (IDA & IBRD countries) not found in regex\n",
      "WARNING:country_converter.country_converter:Sub-Saharan Africa (IDA & IBRD countries) not found in regex\n",
      "WARNING:country_converter.country_converter:Sub-Saharan Africa (IDA & IBRD countries) not found in regex\n",
      "WARNING:country_converter.country_converter:Upper middle income not found in regex\n",
      "WARNING:country_converter.country_converter:Upper middle income not found in regex\n",
      "WARNING:country_converter.country_converter:Upper middle income not found in regex\n",
      "WARNING:country_converter.country_converter:Upper middle income not found in regex\n",
      "WARNING:country_converter.country_converter:Upper middle income not found in regex\n",
      "WARNING:country_converter.country_converter:Upper middle income not found in regex\n",
      "WARNING:country_converter.country_converter:Upper middle income not found in regex\n",
      "WARNING:country_converter.country_converter:Upper middle income not found in regex\n",
      "WARNING:country_converter.country_converter:Upper middle income not found in regex\n",
      "WARNING:country_converter.country_converter:World not found in regex\n",
      "WARNING:country_converter.country_converter:World not found in regex\n",
      "WARNING:country_converter.country_converter:World not found in regex\n",
      "WARNING:country_converter.country_converter:World not found in regex\n",
      "WARNING:country_converter.country_converter:World not found in regex\n",
      "WARNING:country_converter.country_converter:World not found in regex\n",
      "WARNING:country_converter.country_converter:World not found in regex\n",
      "WARNING:country_converter.country_converter:World not found in regex\n",
      "WARNING:country_converter.country_converter:World not found in regex\n",
      "WARNING:country_converter.country_converter:Channel Islands not found in regex\n",
      "WARNING:country_converter.country_converter:Channel Islands not found in regex\n",
      "WARNING:country_converter.country_converter:Channel Islands not found in regex\n",
      "WARNING:country_converter.country_converter:Channel Islands not found in regex\n",
      "WARNING:country_converter.country_converter:Channel Islands not found in regex\n",
      "WARNING:country_converter.country_converter:Channel Islands not found in regex\n",
      "WARNING:country_converter.country_converter:Channel Islands not found in regex\n",
      "WARNING:country_converter.country_converter:Channel Islands not found in regex\n",
      "WARNING:country_converter.country_converter:Channel Islands not found in regex\n"
     ]
    }
   ],
   "source": [
    "#Function call to standardize country names, so that merging can be done properly\n",
    "wb_final = func_cnames(df_wb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "b7c79e6c",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "#Renaming column names\n",
    "wb_final = wb_final.rename(columns={0: 'Country', 1: \"Arable_Land\", 2 : \"Cereal_Yield\", 3 : \"SalariedEmp\",  4: \"Year\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "aea4253d",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "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>Arable_Land</th>\n",
       "      <th>Cereal_Yield</th>\n",
       "      <th>SalariedEmp</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Africa Eastern and Southern</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>not found</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>27.844263</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>not found</td>\n",
       "      <td>0.184952</td>\n",
       "      <td>1622.882922</td>\n",
       "      <td>27.755236</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>not found</td>\n",
       "      <td>0.189571</td>\n",
       "      <td>1728.818670</td>\n",
       "      <td>27.416973</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>not found</td>\n",
       "      <td>0.195264</td>\n",
       "      <td>1498.714700</td>\n",
       "      <td>27.138512</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Country  Arable_Land  Cereal_Yield  SalariedEmp  Year\n",
       "0  Africa Eastern and Southern          NaN           NaN          NaN  2020\n",
       "1                    not found          NaN           NaN    27.844263  2019\n",
       "2                    not found     0.184952   1622.882922    27.755236  2018\n",
       "3                    not found     0.189571   1728.818670    27.416973  2017\n",
       "4                    not found     0.195264   1498.714700    27.138512  2016"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check result\n",
    "wb_final.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "465a441e",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Country           0\n",
       "Arable_Land     614\n",
       "Cereal_Yield    795\n",
       "SalariedEmp     514\n",
       "Year              0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check for missing data\n",
    "wb_final.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8212af6f",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "We have obtained the required world bank data. Now, we will merge this with our target dataframe, and see how many missing \n",
    "values we have after the merge."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "3a592609",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "#Merge with food insecurity dataframe\n",
    "merge1 = fi_final.merge(wb_final,how=\"left\",on=[\"Country\", \"Year\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "26c85587",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "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>Year</th>\n",
       "      <th>GFSI_Index</th>\n",
       "      <th>Arable_Land</th>\n",
       "      <th>Cereal_Yield</th>\n",
       "      <th>SalariedEmp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>2012</td>\n",
       "      <td>53.2</td>\n",
       "      <td>0.200795</td>\n",
       "      <td>1677.6</td>\n",
       "      <td>68.029999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Angola</td>\n",
       "      <td>2012</td>\n",
       "      <td>40.1</td>\n",
       "      <td>0.187192</td>\n",
       "      <td>552.0</td>\n",
       "      <td>27.889999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>2012</td>\n",
       "      <td>61.0</td>\n",
       "      <td>0.952573</td>\n",
       "      <td>4146.2</td>\n",
       "      <td>77.010002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Australia</td>\n",
       "      <td>2012</td>\n",
       "      <td>70.9</td>\n",
       "      <td>1.391666</td>\n",
       "      <td>2231.6</td>\n",
       "      <td>82.830002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Austria</td>\n",
       "      <td>2012</td>\n",
       "      <td>79.6</td>\n",
       "      <td>0.160748</td>\n",
       "      <td>6002.9</td>\n",
       "      <td>86.930000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1125</th>\n",
       "      <td>Uzbekistan</td>\n",
       "      <td>2021</td>\n",
       "      <td>53.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1126</th>\n",
       "      <td>Venezuela</td>\n",
       "      <td>2021</td>\n",
       "      <td>39.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1127</th>\n",
       "      <td>Vietnam</td>\n",
       "      <td>2021</td>\n",
       "      <td>61.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1128</th>\n",
       "      <td>Yemen</td>\n",
       "      <td>2021</td>\n",
       "      <td>35.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1129</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>2021</td>\n",
       "      <td>38.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1130 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Country  Year GFSI_Index  Arable_Land  Cereal_Yield  SalariedEmp\n",
       "0        Algeria  2012       53.2     0.200795        1677.6    68.029999\n",
       "1         Angola  2012       40.1     0.187192         552.0    27.889999\n",
       "2      Argentina  2012       61.0     0.952573        4146.2    77.010002\n",
       "3      Australia  2012       70.9     1.391666        2231.6    82.830002\n",
       "4        Austria  2012       79.6     0.160748        6002.9    86.930000\n",
       "...          ...   ...        ...          ...           ...          ...\n",
       "1125  Uzbekistan  2021       53.8          NaN           NaN          NaN\n",
       "1126   Venezuela  2021       39.4          NaN           NaN          NaN\n",
       "1127     Vietnam  2021       61.1          NaN           NaN          NaN\n",
       "1128       Yemen  2021       35.7          NaN           NaN          NaN\n",
       "1129      Zambia  2021       38.0          NaN           NaN          NaN\n",
       "\n",
       "[1130 rows x 6 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Checking result\n",
    "display(merge1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "603de60c",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Country           0\n",
       "Year              0\n",
       "GFSI_Index        0\n",
       "Arable_Land     339\n",
       "Cereal_Yield    353\n",
       "SalariedEmp     226\n",
       "dtype: int64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check for missing data\n",
    "merge1.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0efbff6",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "We obseve that the number of NA's has reduced, but they are still a lot in number"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a5adaab",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "##### Obtaining Logistics Performance Indicator data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "b963f0cf",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "#Function for web-scraping the link for LPI\n",
    "\n",
    "def func_lpi(url=None):\n",
    "    \"\"\"\n",
    "    func_lpi will take in the url(as a string) as a parameter, parse using beautiful soup, and then use read_html to read \n",
    "    the required tables.\n",
    "    ------------------------------------------------------------------------------------------------------------------------\n",
    "    Arguments: \n",
    "    url -> The url to be scraped, it is a string; its default value is None\n",
    "    ------------------------------------------------------------------------------------------------------------------------\n",
    "    Return:\n",
    "    A dataframe; which is basically the table scraped from the url\n",
    "    \"\"\"\n",
    "    \n",
    "    page_temp = requests.get(url)\n",
    "    if(page_temp.status_code==200):#Checking if the web-page is obtained properly\n",
    "        soup_temp = BeautifulSoup(page_temp.content, 'html.parser')\n",
    "        t1 = soup_temp.findAll('table', {'class':\"sticky-enabled\"}) #This detail was found by inspecting the web-page\n",
    "        df1 = pd.read_html(str(t1))\n",
    "        df_l1 = pd.DataFrame(df1[0])\n",
    "        return(df_l1)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "225a9e25",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "#This are the urls for the LPI scores \n",
    "\n",
    "url_lpi2018 = \"https://lpi.worldbank.org/international/global/2018.%202019\"\n",
    "url_lpi2016 = \"https://lpi.worldbank.org/international/global/2016\"\n",
    "url_lpi2014 = \"https://lpi.worldbank.org/international/global/2014\"\n",
    "url_lpi2012 = \"https://lpi.worldbank.org/international/global/2012\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "772d7826",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "#We are going to call function for each url to obtain the final result\n",
    "\n",
    "lpi2018 = func_lpi(url_lpi2018)\n",
    "lpi2016 = func_lpi(url_lpi2016)\n",
    "lpi2014 = func_lpi(url_lpi2014)\n",
    "lpi2012 = func_lpi(url_lpi2012)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "33b9fa05",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
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       "<div>\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</th>\n",
       "      <th>Year</th>\n",
       "      <th>LPI Rank</th>\n",
       "      <th>LPI Score</th>\n",
       "      <th>Customs</th>\n",
       "      <th>Customs.1</th>\n",
       "      <th>Infrastructure</th>\n",
       "      <th>Infrastructure.1</th>\n",
       "      <th>International shipments</th>\n",
       "      <th>International shipments.1</th>\n",
       "      <th>Logistics competence</th>\n",
       "      <th>Logistics competence.1</th>\n",
       "      <th>Tracking &amp; tracing</th>\n",
       "      <th>Tracking &amp; tracing.1</th>\n",
       "      <th>Timeliness</th>\n",
       "      <th>Timeliness.1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Germany</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>4.23</td>\n",
       "      <td>2</td>\n",
       "      <td>4.12</td>\n",
       "      <td>1</td>\n",
       "      <td>4.44</td>\n",
       "      <td>8</td>\n",
       "      <td>3.86</td>\n",
       "      <td>1</td>\n",
       "      <td>4.28</td>\n",
       "      <td>3</td>\n",
       "      <td>4.27</td>\n",
       "      <td>2</td>\n",
       "      <td>4.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Luxembourg</td>\n",
       "      <td>2016</td>\n",
       "      <td>2</td>\n",
       "      <td>4.22</td>\n",
       "      <td>9</td>\n",
       "      <td>3.90</td>\n",
       "      <td>4</td>\n",
       "      <td>4.24</td>\n",
       "      <td>1</td>\n",
       "      <td>4.24</td>\n",
       "      <td>10</td>\n",
       "      <td>4.01</td>\n",
       "      <td>8</td>\n",
       "      <td>4.12</td>\n",
       "      <td>1</td>\n",
       "      <td>4.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Sweden</td>\n",
       "      <td>2016</td>\n",
       "      <td>3</td>\n",
       "      <td>4.20</td>\n",
       "      <td>8</td>\n",
       "      <td>3.92</td>\n",
       "      <td>3</td>\n",
       "      <td>4.27</td>\n",
       "      <td>4</td>\n",
       "      <td>4.00</td>\n",
       "      <td>2</td>\n",
       "      <td>4.25</td>\n",
       "      <td>1</td>\n",
       "      <td>4.38</td>\n",
       "      <td>3</td>\n",
       "      <td>4.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Netherlands</td>\n",
       "      <td>2016</td>\n",
       "      <td>4</td>\n",
       "      <td>4.19</td>\n",
       "      <td>3</td>\n",
       "      <td>4.12</td>\n",
       "      <td>2</td>\n",
       "      <td>4.29</td>\n",
       "      <td>6</td>\n",
       "      <td>3.94</td>\n",
       "      <td>3</td>\n",
       "      <td>4.22</td>\n",
       "      <td>6</td>\n",
       "      <td>4.17</td>\n",
       "      <td>5</td>\n",
       "      <td>4.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Singapore</td>\n",
       "      <td>2016</td>\n",
       "      <td>5</td>\n",
       "      <td>4.14</td>\n",
       "      <td>1</td>\n",
       "      <td>4.18</td>\n",
       "      <td>6</td>\n",
       "      <td>4.20</td>\n",
       "      <td>5</td>\n",
       "      <td>3.96</td>\n",
       "      <td>5</td>\n",
       "      <td>4.09</td>\n",
       "      <td>10</td>\n",
       "      <td>4.05</td>\n",
       "      <td>6</td>\n",
       "      <td>4.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>Equatorial Guinea</td>\n",
       "      <td>2016</td>\n",
       "      <td>156</td>\n",
       "      <td>1.88</td>\n",
       "      <td>154</td>\n",
       "      <td>1.88</td>\n",
       "      <td>158</td>\n",
       "      <td>1.50</td>\n",
       "      <td>156</td>\n",
       "      <td>1.89</td>\n",
       "      <td>157</td>\n",
       "      <td>1.75</td>\n",
       "      <td>152</td>\n",
       "      <td>1.89</td>\n",
       "      <td>153</td>\n",
       "      <td>2.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>Mauritania</td>\n",
       "      <td>2016</td>\n",
       "      <td>157</td>\n",
       "      <td>1.87</td>\n",
       "      <td>131</td>\n",
       "      <td>2.14</td>\n",
       "      <td>157</td>\n",
       "      <td>1.54</td>\n",
       "      <td>154</td>\n",
       "      <td>2.00</td>\n",
       "      <td>158</td>\n",
       "      <td>1.74</td>\n",
       "      <td>159</td>\n",
       "      <td>1.54</td>\n",
       "      <td>157</td>\n",
       "      <td>2.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>Somalia</td>\n",
       "      <td>2016</td>\n",
       "      <td>158</td>\n",
       "      <td>1.75</td>\n",
       "      <td>159</td>\n",
       "      <td>1.29</td>\n",
       "      <td>156</td>\n",
       "      <td>1.57</td>\n",
       "      <td>157</td>\n",
       "      <td>1.86</td>\n",
       "      <td>156</td>\n",
       "      <td>1.85</td>\n",
       "      <td>160</td>\n",
       "      <td>1.51</td>\n",
       "      <td>152</td>\n",
       "      <td>2.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>Haiti</td>\n",
       "      <td>2016</td>\n",
       "      <td>159</td>\n",
       "      <td>1.72</td>\n",
       "      <td>158</td>\n",
       "      <td>1.70</td>\n",
       "      <td>159</td>\n",
       "      <td>1.47</td>\n",
       "      <td>159</td>\n",
       "      <td>1.81</td>\n",
       "      <td>159</td>\n",
       "      <td>1.68</td>\n",
       "      <td>158</td>\n",
       "      <td>1.56</td>\n",
       "      <td>160</td>\n",
       "      <td>2.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>Syrian Arab Republic</td>\n",
       "      <td>2016</td>\n",
       "      <td>160</td>\n",
       "      <td>1.60</td>\n",
       "      <td>160</td>\n",
       "      <td>1.11</td>\n",
       "      <td>160</td>\n",
       "      <td>1.24</td>\n",
       "      <td>160</td>\n",
       "      <td>1.36</td>\n",
       "      <td>160</td>\n",
       "      <td>1.39</td>\n",
       "      <td>138</td>\n",
       "      <td>2.10</td>\n",
       "      <td>147</td>\n",
       "      <td>2.40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>160 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Country  Year  LPI Rank  LPI Score  Customs  Customs.1  \\\n",
       "0                 Germany  2016         1       4.23        2       4.12   \n",
       "1              Luxembourg  2016         2       4.22        9       3.90   \n",
       "2                  Sweden  2016         3       4.20        8       3.92   \n",
       "3             Netherlands  2016         4       4.19        3       4.12   \n",
       "4               Singapore  2016         5       4.14        1       4.18   \n",
       "..                    ...   ...       ...        ...      ...        ...   \n",
       "155     Equatorial Guinea  2016       156       1.88      154       1.88   \n",
       "156            Mauritania  2016       157       1.87      131       2.14   \n",
       "157               Somalia  2016       158       1.75      159       1.29   \n",
       "158                 Haiti  2016       159       1.72      158       1.70   \n",
       "159  Syrian Arab Republic  2016       160       1.60      160       1.11   \n",
       "\n",
       "     Infrastructure  Infrastructure.1  International shipments  \\\n",
       "0                 1              4.44                        8   \n",
       "1                 4              4.24                        1   \n",
       "2                 3              4.27                        4   \n",
       "3                 2              4.29                        6   \n",
       "4                 6              4.20                        5   \n",
       "..              ...               ...                      ...   \n",
       "155             158              1.50                      156   \n",
       "156             157              1.54                      154   \n",
       "157             156              1.57                      157   \n",
       "158             159              1.47                      159   \n",
       "159             160              1.24                      160   \n",
       "\n",
       "     International shipments.1  Logistics competence  Logistics competence.1  \\\n",
       "0                         3.86                     1                    4.28   \n",
       "1                         4.24                    10                    4.01   \n",
       "2                         4.00                     2                    4.25   \n",
       "3                         3.94                     3                    4.22   \n",
       "4                         3.96                     5                    4.09   \n",
       "..                         ...                   ...                     ...   \n",
       "155                       1.89                   157                    1.75   \n",
       "156                       2.00                   158                    1.74   \n",
       "157                       1.86                   156                    1.85   \n",
       "158                       1.81                   159                    1.68   \n",
       "159                       1.36                   160                    1.39   \n",
       "\n",
       "     Tracking & tracing  Tracking & tracing.1  Timeliness  Timeliness.1  \n",
       "0                     3                  4.27           2          4.45  \n",
       "1                     8                  4.12           1          4.80  \n",
       "2                     1                  4.38           3          4.45  \n",
       "3                     6                  4.17           5          4.41  \n",
       "4                    10                  4.05           6          4.40  \n",
       "..                  ...                   ...         ...           ...  \n",
       "155                 152                  1.89         153          2.32  \n",
       "156                 159                  1.54         157          2.14  \n",
       "157                 160                  1.51         152          2.35  \n",
       "158                 158                  1.56         160          2.02  \n",
       "159                 138                  2.10         147          2.40  \n",
       "\n",
       "[160 rows x 16 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Checking any one to see the results\n",
    "display(lpi2016)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ebc1ae56",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "We have now scraped the required LPI data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "8191b6da",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Year</th>\n",
       "      <th>LPI Rank</th>\n",
       "      <th>LPI Score</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Germany</td>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "      <td>3</td>\n",
       "      <td>4.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Sweden</td>\n",
       "      <td>2018</td>\n",
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       "      <td>2</td>\n",
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       "      <td>2</td>\n",
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       "      <td>10</td>\n",
       "      <td>3.98</td>\n",
       "      <td>17</td>\n",
       "      <td>3.88</td>\n",
       "      <td>7</td>\n",
       "      <td>4.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Belgium</td>\n",
       "      <td>2018</td>\n",
       "      <td>3</td>\n",
       "      <td>4.04</td>\n",
       "      <td>14</td>\n",
       "      <td>3.66</td>\n",
       "      <td>14</td>\n",
       "      <td>3.98</td>\n",
       "      <td>1</td>\n",
       "      <td>3.99</td>\n",
       "      <td>2</td>\n",
       "      <td>4.13</td>\n",
       "      <td>9</td>\n",
       "      <td>4.05</td>\n",
       "      <td>1</td>\n",
       "      <td>4.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Austria</td>\n",
       "      <td>2018</td>\n",
       "      <td>4</td>\n",
       "      <td>4.03</td>\n",
       "      <td>12</td>\n",
       "      <td>3.71</td>\n",
       "      <td>5</td>\n",
       "      <td>4.18</td>\n",
       "      <td>3</td>\n",
       "      <td>3.88</td>\n",
       "      <td>6</td>\n",
       "      <td>4.08</td>\n",
       "      <td>7</td>\n",
       "      <td>4.09</td>\n",
       "      <td>12</td>\n",
       "      <td>4.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Japan</td>\n",
       "      <td>2018</td>\n",
       "      <td>5</td>\n",
       "      <td>4.03</td>\n",
       "      <td>3</td>\n",
       "      <td>3.99</td>\n",
       "      <td>2</td>\n",
       "      <td>4.25</td>\n",
       "      <td>14</td>\n",
       "      <td>3.59</td>\n",
       "      <td>4</td>\n",
       "      <td>4.09</td>\n",
       "      <td>10</td>\n",
       "      <td>4.05</td>\n",
       "      <td>10</td>\n",
       "      <td>4.25</td>\n",
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       "    <tr>\n",
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       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>Equatorial Guinea</td>\n",
       "      <td>2016</td>\n",
       "      <td>156</td>\n",
       "      <td>1.88</td>\n",
       "      <td>154</td>\n",
       "      <td>1.88</td>\n",
       "      <td>158</td>\n",
       "      <td>1.50</td>\n",
       "      <td>156</td>\n",
       "      <td>1.89</td>\n",
       "      <td>157</td>\n",
       "      <td>1.75</td>\n",
       "      <td>152</td>\n",
       "      <td>1.89</td>\n",
       "      <td>153</td>\n",
       "      <td>2.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>Mauritania</td>\n",
       "      <td>2016</td>\n",
       "      <td>157</td>\n",
       "      <td>1.87</td>\n",
       "      <td>131</td>\n",
       "      <td>2.14</td>\n",
       "      <td>157</td>\n",
       "      <td>1.54</td>\n",
       "      <td>154</td>\n",
       "      <td>2.00</td>\n",
       "      <td>158</td>\n",
       "      <td>1.74</td>\n",
       "      <td>159</td>\n",
       "      <td>1.54</td>\n",
       "      <td>157</td>\n",
       "      <td>2.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>Somalia</td>\n",
       "      <td>2016</td>\n",
       "      <td>158</td>\n",
       "      <td>1.75</td>\n",
       "      <td>159</td>\n",
       "      <td>1.29</td>\n",
       "      <td>156</td>\n",
       "      <td>1.57</td>\n",
       "      <td>157</td>\n",
       "      <td>1.86</td>\n",
       "      <td>156</td>\n",
       "      <td>1.85</td>\n",
       "      <td>160</td>\n",
       "      <td>1.51</td>\n",
       "      <td>152</td>\n",
       "      <td>2.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>Haiti</td>\n",
       "      <td>2016</td>\n",
       "      <td>159</td>\n",
       "      <td>1.72</td>\n",
       "      <td>158</td>\n",
       "      <td>1.70</td>\n",
       "      <td>159</td>\n",
       "      <td>1.47</td>\n",
       "      <td>159</td>\n",
       "      <td>1.81</td>\n",
       "      <td>159</td>\n",
       "      <td>1.68</td>\n",
       "      <td>158</td>\n",
       "      <td>1.56</td>\n",
       "      <td>160</td>\n",
       "      <td>2.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>Syrian Arab Republic</td>\n",
       "      <td>2016</td>\n",
       "      <td>160</td>\n",
       "      <td>1.60</td>\n",
       "      <td>160</td>\n",
       "      <td>1.11</td>\n",
       "      <td>160</td>\n",
       "      <td>1.24</td>\n",
       "      <td>160</td>\n",
       "      <td>1.36</td>\n",
       "      <td>160</td>\n",
       "      <td>1.39</td>\n",
       "      <td>138</td>\n",
       "      <td>2.10</td>\n",
       "      <td>147</td>\n",
       "      <td>2.40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>320 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Country  Year  LPI Rank  LPI Score  Customs  Customs.1  \\\n",
       "0                 Germany  2018         1       4.20        1       4.09   \n",
       "1                  Sweden  2018         2       4.05        2       4.05   \n",
       "2                 Belgium  2018         3       4.04       14       3.66   \n",
       "3                 Austria  2018         4       4.03       12       3.71   \n",
       "4                   Japan  2018         5       4.03        3       3.99   \n",
       "..                    ...   ...       ...        ...      ...        ...   \n",
       "155     Equatorial Guinea  2016       156       1.88      154       1.88   \n",
       "156            Mauritania  2016       157       1.87      131       2.14   \n",
       "157               Somalia  2016       158       1.75      159       1.29   \n",
       "158                 Haiti  2016       159       1.72      158       1.70   \n",
       "159  Syrian Arab Republic  2016       160       1.60      160       1.11   \n",
       "\n",
       "     Infrastructure  Infrastructure.1  International shipments  \\\n",
       "0                 1              4.37                        4   \n",
       "1                 3              4.24                        2   \n",
       "2                14              3.98                        1   \n",
       "3                 5              4.18                        3   \n",
       "4                 2              4.25                       14   \n",
       "..              ...               ...                      ...   \n",
       "155             158              1.50                      156   \n",
       "156             157              1.54                      154   \n",
       "157             156              1.57                      157   \n",
       "158             159              1.47                      159   \n",
       "159             160              1.24                      160   \n",
       "\n",
       "     International shipments.1  Logistics competence  Logistics competence.1  \\\n",
       "0                         3.86                     1                    4.31   \n",
       "1                         3.92                    10                    3.98   \n",
       "2                         3.99                     2                    4.13   \n",
       "3                         3.88                     6                    4.08   \n",
       "4                         3.59                     4                    4.09   \n",
       "..                         ...                   ...                     ...   \n",
       "155                       1.89                   157                    1.75   \n",
       "156                       2.00                   158                    1.74   \n",
       "157                       1.86                   156                    1.85   \n",
       "158                       1.81                   159                    1.68   \n",
       "159                       1.36                   160                    1.39   \n",
       "\n",
       "     Tracking & tracing  Tracking & tracing.1  Timeliness  Timeliness.1  \n",
       "0                     2                  4.24           3          4.39  \n",
       "1                    17                  3.88           7          4.28  \n",
       "2                     9                  4.05           1          4.41  \n",
       "3                     7                  4.09          12          4.25  \n",
       "4                    10                  4.05          10          4.25  \n",
       "..                  ...                   ...         ...           ...  \n",
       "155                 152                  1.89         153          2.32  \n",
       "156                 159                  1.54         157          2.14  \n",
       "157                 160                  1.51         152          2.35  \n",
       "158                 158                  1.56         160          2.02  \n",
       "159                 138                  2.10         147          2.40  \n",
       "\n",
       "[320 rows x 16 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Join two tables together\n",
    "a = pd.concat([lpi2018,lpi2016],sort=False)\n",
    "display(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "5e526820",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Customs</th>\n",
       "      <th>Customs.1</th>\n",
       "      <th>Infrastructure</th>\n",
       "      <th>Infrastructure.1</th>\n",
       "      <th>International shipments</th>\n",
       "      <th>International shipments.1</th>\n",
       "      <th>LPI Rank</th>\n",
       "      <th>LPI Score</th>\n",
       "      <th>Logistics competence</th>\n",
       "      <th>Logistics competence.1</th>\n",
       "      <th>Timeliness</th>\n",
       "      <th>Timeliness.1</th>\n",
       "      <th>Tracking &amp; tracing</th>\n",
       "      <th>Tracking &amp; tracing.1</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Germany</td>\n",
       "      <td>2</td>\n",
       "      <td>4.10</td>\n",
       "      <td>1</td>\n",
       "      <td>4.32</td>\n",
       "      <td>4</td>\n",
       "      <td>3.74</td>\n",
       "      <td>1</td>\n",
       "      <td>4.12</td>\n",
       "      <td>3</td>\n",
       "      <td>4.12</td>\n",
       "      <td>4</td>\n",
       "      <td>4.36</td>\n",
       "      <td>1</td>\n",
       "      <td>4.17</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Netherlands</td>\n",
       "      <td>4</td>\n",
       "      <td>3.96</td>\n",
       "      <td>3</td>\n",
       "      <td>4.23</td>\n",
       "      <td>11</td>\n",
       "      <td>3.64</td>\n",
       "      <td>2</td>\n",
       "      <td>4.05</td>\n",
       "      <td>2</td>\n",
       "      <td>4.13</td>\n",
       "      <td>6</td>\n",
       "      <td>4.34</td>\n",
       "      <td>6</td>\n",
       "      <td>4.07</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Belgium</td>\n",
       "      <td>11</td>\n",
       "      <td>3.80</td>\n",
       "      <td>8</td>\n",
       "      <td>4.10</td>\n",
       "      <td>2</td>\n",
       "      <td>3.80</td>\n",
       "      <td>3</td>\n",
       "      <td>4.04</td>\n",
       "      <td>4</td>\n",
       "      <td>4.11</td>\n",
       "      <td>2</td>\n",
       "      <td>4.39</td>\n",
       "      <td>4</td>\n",
       "      <td>4.11</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>5</td>\n",
       "      <td>3.94</td>\n",
       "      <td>6</td>\n",
       "      <td>4.16</td>\n",
       "      <td>12</td>\n",
       "      <td>3.63</td>\n",
       "      <td>4</td>\n",
       "      <td>4.01</td>\n",
       "      <td>5</td>\n",
       "      <td>4.03</td>\n",
       "      <td>7</td>\n",
       "      <td>4.33</td>\n",
       "      <td>5</td>\n",
       "      <td>4.08</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Singapore</td>\n",
       "      <td>3</td>\n",
       "      <td>4.01</td>\n",
       "      <td>2</td>\n",
       "      <td>4.28</td>\n",
       "      <td>6</td>\n",
       "      <td>3.70</td>\n",
       "      <td>5</td>\n",
       "      <td>4.00</td>\n",
       "      <td>8</td>\n",
       "      <td>3.97</td>\n",
       "      <td>9</td>\n",
       "      <td>4.25</td>\n",
       "      <td>11</td>\n",
       "      <td>3.90</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>Nepal</td>\n",
       "      <td>125</td>\n",
       "      <td>2.20</td>\n",
       "      <td>149</td>\n",
       "      <td>1.87</td>\n",
       "      <td>151</td>\n",
       "      <td>1.86</td>\n",
       "      <td>151</td>\n",
       "      <td>2.04</td>\n",
       "      <td>146</td>\n",
       "      <td>2.12</td>\n",
       "      <td>153</td>\n",
       "      <td>2.21</td>\n",
       "      <td>149</td>\n",
       "      <td>1.95</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>Chad</td>\n",
       "      <td>148</td>\n",
       "      <td>1.86</td>\n",
       "      <td>142</td>\n",
       "      <td>2.00</td>\n",
       "      <td>146</td>\n",
       "      <td>2.00</td>\n",
       "      <td>152</td>\n",
       "      <td>2.03</td>\n",
       "      <td>150</td>\n",
       "      <td>2.00</td>\n",
       "      <td>133</td>\n",
       "      <td>2.71</td>\n",
       "      <td>155</td>\n",
       "      <td>1.57</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>Haiti</td>\n",
       "      <td>150</td>\n",
       "      <td>1.78</td>\n",
       "      <td>151</td>\n",
       "      <td>1.78</td>\n",
       "      <td>148</td>\n",
       "      <td>1.94</td>\n",
       "      <td>153</td>\n",
       "      <td>2.03</td>\n",
       "      <td>154</td>\n",
       "      <td>1.74</td>\n",
       "      <td>129</td>\n",
       "      <td>2.74</td>\n",
       "      <td>139</td>\n",
       "      <td>2.15</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>Djibouti</td>\n",
       "      <td>154</td>\n",
       "      <td>1.72</td>\n",
       "      <td>154</td>\n",
       "      <td>1.51</td>\n",
       "      <td>154</td>\n",
       "      <td>1.77</td>\n",
       "      <td>154</td>\n",
       "      <td>1.80</td>\n",
       "      <td>153</td>\n",
       "      <td>1.84</td>\n",
       "      <td>154</td>\n",
       "      <td>2.19</td>\n",
       "      <td>153</td>\n",
       "      <td>1.73</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>Burundi</td>\n",
       "      <td>155</td>\n",
       "      <td>1.67</td>\n",
       "      <td>153</td>\n",
       "      <td>1.68</td>\n",
       "      <td>155</td>\n",
       "      <td>1.57</td>\n",
       "      <td>155</td>\n",
       "      <td>1.61</td>\n",
       "      <td>155</td>\n",
       "      <td>1.43</td>\n",
       "      <td>155</td>\n",
       "      <td>1.67</td>\n",
       "      <td>154</td>\n",
       "      <td>1.67</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>315 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Country  Customs  Customs.1  Infrastructure  Infrastructure.1  \\\n",
       "0           Germany        2       4.10               1              4.32   \n",
       "1       Netherlands        4       3.96               3              4.23   \n",
       "2           Belgium       11       3.80               8              4.10   \n",
       "3    United Kingdom        5       3.94               6              4.16   \n",
       "4         Singapore        3       4.01               2              4.28   \n",
       "..              ...      ...        ...             ...               ...   \n",
       "150           Nepal      125       2.20             149              1.87   \n",
       "151            Chad      148       1.86             142              2.00   \n",
       "152           Haiti      150       1.78             151              1.78   \n",
       "153        Djibouti      154       1.72             154              1.51   \n",
       "154         Burundi      155       1.67             153              1.68   \n",
       "\n",
       "     International shipments  International shipments.1  LPI Rank  LPI Score  \\\n",
       "0                          4                       3.74         1       4.12   \n",
       "1                         11                       3.64         2       4.05   \n",
       "2                          2                       3.80         3       4.04   \n",
       "3                         12                       3.63         4       4.01   \n",
       "4                          6                       3.70         5       4.00   \n",
       "..                       ...                        ...       ...        ...   \n",
       "150                      151                       1.86       151       2.04   \n",
       "151                      146                       2.00       152       2.03   \n",
       "152                      148                       1.94       153       2.03   \n",
       "153                      154                       1.77       154       1.80   \n",
       "154                      155                       1.57       155       1.61   \n",
       "\n",
       "     Logistics competence  Logistics competence.1  Timeliness  Timeliness.1  \\\n",
       "0                       3                    4.12           4          4.36   \n",
       "1                       2                    4.13           6          4.34   \n",
       "2                       4                    4.11           2          4.39   \n",
       "3                       5                    4.03           7          4.33   \n",
       "4                       8                    3.97           9          4.25   \n",
       "..                    ...                     ...         ...           ...   \n",
       "150                   146                    2.12         153          2.21   \n",
       "151                   150                    2.00         133          2.71   \n",
       "152                   154                    1.74         129          2.74   \n",
       "153                   153                    1.84         154          2.19   \n",
       "154                   155                    1.43         155          1.67   \n",
       "\n",
       "     Tracking & tracing  Tracking & tracing.1  Year  \n",
       "0                     1                  4.17  2014  \n",
       "1                     6                  4.07  2014  \n",
       "2                     4                  4.11  2014  \n",
       "3                     5                  4.08  2014  \n",
       "4                    11                  3.90  2014  \n",
       "..                  ...                   ...   ...  \n",
       "150                 149                  1.95  2012  \n",
       "151                 155                  1.57  2012  \n",
       "152                 139                  2.15  2012  \n",
       "153                 153                  1.73  2012  \n",
       "154                 154                  1.67  2012  \n",
       "\n",
       "[315 rows x 16 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Join the other two tables together\n",
    "b = pd.concat([lpi2014,lpi2012],sort=True)\n",
    "display(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "e56737ab",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Year</th>\n",
       "      <th>LPI Rank</th>\n",
       "      <th>LPI Score</th>\n",
       "      <th>Customs</th>\n",
       "      <th>Customs.1</th>\n",
       "      <th>Infrastructure</th>\n",
       "      <th>Infrastructure.1</th>\n",
       "      <th>International shipments</th>\n",
       "      <th>International shipments.1</th>\n",
       "      <th>Logistics competence</th>\n",
       "      <th>Logistics competence.1</th>\n",
       "      <th>Tracking &amp; tracing</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Germany</td>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>4.31</td>\n",
       "      <td>2</td>\n",
       "      <td>4.24</td>\n",
       "      <td>3</td>\n",
       "      <td>4.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Sweden</td>\n",
       "      <td>2018</td>\n",
       "      <td>2</td>\n",
       "      <td>4.05</td>\n",
       "      <td>2</td>\n",
       "      <td>4.05</td>\n",
       "      <td>3</td>\n",
       "      <td>4.24</td>\n",
       "      <td>2</td>\n",
       "      <td>3.92</td>\n",
       "      <td>10</td>\n",
       "      <td>3.98</td>\n",
       "      <td>17</td>\n",
       "      <td>3.88</td>\n",
       "      <td>7</td>\n",
       "      <td>4.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Belgium</td>\n",
       "      <td>2018</td>\n",
       "      <td>3</td>\n",
       "      <td>4.04</td>\n",
       "      <td>14</td>\n",
       "      <td>3.66</td>\n",
       "      <td>14</td>\n",
       "      <td>3.98</td>\n",
       "      <td>1</td>\n",
       "      <td>3.99</td>\n",
       "      <td>2</td>\n",
       "      <td>4.13</td>\n",
       "      <td>9</td>\n",
       "      <td>4.05</td>\n",
       "      <td>1</td>\n",
       "      <td>4.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Austria</td>\n",
       "      <td>2018</td>\n",
       "      <td>4</td>\n",
       "      <td>4.03</td>\n",
       "      <td>12</td>\n",
       "      <td>3.71</td>\n",
       "      <td>5</td>\n",
       "      <td>4.18</td>\n",
       "      <td>3</td>\n",
       "      <td>3.88</td>\n",
       "      <td>6</td>\n",
       "      <td>4.08</td>\n",
       "      <td>7</td>\n",
       "      <td>4.09</td>\n",
       "      <td>12</td>\n",
       "      <td>4.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Japan</td>\n",
       "      <td>2018</td>\n",
       "      <td>5</td>\n",
       "      <td>4.03</td>\n",
       "      <td>3</td>\n",
       "      <td>3.99</td>\n",
       "      <td>2</td>\n",
       "      <td>4.25</td>\n",
       "      <td>14</td>\n",
       "      <td>3.59</td>\n",
       "      <td>4</td>\n",
       "      <td>4.09</td>\n",
       "      <td>10</td>\n",
       "      <td>4.05</td>\n",
       "      <td>10</td>\n",
       "      <td>4.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>630</th>\n",
       "      <td>Nepal</td>\n",
       "      <td>2012</td>\n",
       "      <td>151</td>\n",
       "      <td>2.04</td>\n",
       "      <td>125</td>\n",
       "      <td>2.20</td>\n",
       "      <td>149</td>\n",
       "      <td>1.87</td>\n",
       "      <td>151</td>\n",
       "      <td>1.86</td>\n",
       "      <td>146</td>\n",
       "      <td>2.12</td>\n",
       "      <td>149</td>\n",
       "      <td>1.95</td>\n",
       "      <td>153</td>\n",
       "      <td>2.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>631</th>\n",
       "      <td>Chad</td>\n",
       "      <td>2012</td>\n",
       "      <td>152</td>\n",
       "      <td>2.03</td>\n",
       "      <td>148</td>\n",
       "      <td>1.86</td>\n",
       "      <td>142</td>\n",
       "      <td>2.00</td>\n",
       "      <td>146</td>\n",
       "      <td>2.00</td>\n",
       "      <td>150</td>\n",
       "      <td>2.00</td>\n",
       "      <td>155</td>\n",
       "      <td>1.57</td>\n",
       "      <td>133</td>\n",
       "      <td>2.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>632</th>\n",
       "      <td>Haiti</td>\n",
       "      <td>2012</td>\n",
       "      <td>153</td>\n",
       "      <td>2.03</td>\n",
       "      <td>150</td>\n",
       "      <td>1.78</td>\n",
       "      <td>151</td>\n",
       "      <td>1.78</td>\n",
       "      <td>148</td>\n",
       "      <td>1.94</td>\n",
       "      <td>154</td>\n",
       "      <td>1.74</td>\n",
       "      <td>139</td>\n",
       "      <td>2.15</td>\n",
       "      <td>129</td>\n",
       "      <td>2.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>633</th>\n",
       "      <td>Djibouti</td>\n",
       "      <td>2012</td>\n",
       "      <td>154</td>\n",
       "      <td>1.80</td>\n",
       "      <td>154</td>\n",
       "      <td>1.72</td>\n",
       "      <td>154</td>\n",
       "      <td>1.51</td>\n",
       "      <td>154</td>\n",
       "      <td>1.77</td>\n",
       "      <td>153</td>\n",
       "      <td>1.84</td>\n",
       "      <td>153</td>\n",
       "      <td>1.73</td>\n",
       "      <td>154</td>\n",
       "      <td>2.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>634</th>\n",
       "      <td>Burundi</td>\n",
       "      <td>2012</td>\n",
       "      <td>155</td>\n",
       "      <td>1.61</td>\n",
       "      <td>155</td>\n",
       "      <td>1.67</td>\n",
       "      <td>153</td>\n",
       "      <td>1.68</td>\n",
       "      <td>155</td>\n",
       "      <td>1.57</td>\n",
       "      <td>155</td>\n",
       "      <td>1.43</td>\n",
       "      <td>154</td>\n",
       "      <td>1.67</td>\n",
       "      <td>155</td>\n",
       "      <td>1.67</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>635 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Country  Year  LPI Rank  LPI Score  Customs  Customs.1  Infrastructure  \\\n",
       "0     Germany  2018         1       4.20        1       4.09               1   \n",
       "1      Sweden  2018         2       4.05        2       4.05               3   \n",
       "2     Belgium  2018         3       4.04       14       3.66              14   \n",
       "3     Austria  2018         4       4.03       12       3.71               5   \n",
       "4       Japan  2018         5       4.03        3       3.99               2   \n",
       "..        ...   ...       ...        ...      ...        ...             ...   \n",
       "630     Nepal  2012       151       2.04      125       2.20             149   \n",
       "631      Chad  2012       152       2.03      148       1.86             142   \n",
       "632     Haiti  2012       153       2.03      150       1.78             151   \n",
       "633  Djibouti  2012       154       1.80      154       1.72             154   \n",
       "634   Burundi  2012       155       1.61      155       1.67             153   \n",
       "\n",
       "     Infrastructure.1  International shipments  International shipments.1  \\\n",
       "0                4.37                        4                       3.86   \n",
       "1                4.24                        2                       3.92   \n",
       "2                3.98                        1                       3.99   \n",
       "3                4.18                        3                       3.88   \n",
       "4                4.25                       14                       3.59   \n",
       "..                ...                      ...                        ...   \n",
       "630              1.87                      151                       1.86   \n",
       "631              2.00                      146                       2.00   \n",
       "632              1.78                      148                       1.94   \n",
       "633              1.51                      154                       1.77   \n",
       "634              1.68                      155                       1.57   \n",
       "\n",
       "     Logistics competence  Logistics competence.1  Tracking & tracing  \\\n",
       "0                       1                    4.31                   2   \n",
       "1                      10                    3.98                  17   \n",
       "2                       2                    4.13                   9   \n",
       "3                       6                    4.08                   7   \n",
       "4                       4                    4.09                  10   \n",
       "..                    ...                     ...                 ...   \n",
       "630                   146                    2.12                 149   \n",
       "631                   150                    2.00                 155   \n",
       "632                   154                    1.74                 139   \n",
       "633                   153                    1.84                 153   \n",
       "634                   155                    1.43                 154   \n",
       "\n",
       "     Tracking & tracing.1  Timeliness  Timeliness.1  \n",
       "0                    4.24           3          4.39  \n",
       "1                    3.88           7          4.28  \n",
       "2                    4.05           1          4.41  \n",
       "3                    4.09          12          4.25  \n",
       "4                    4.05          10          4.25  \n",
       "..                    ...         ...           ...  \n",
       "630                  1.95         153          2.21  \n",
       "631                  1.57         133          2.71  \n",
       "632                  2.15         129          2.74  \n",
       "633                  1.73         154          2.19  \n",
       "634                  1.67         155          1.67  \n",
       "\n",
       "[635 rows x 16 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Finally, join everything together\n",
    "lpi_temp1 = pd.concat([a,b], ignore_index = True)\n",
    "display(lpi_temp1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "40c4e734",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\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>Country</th>\n",
       "      <th>Year</th>\n",
       "      <th>LPI Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Germany</td>\n",
       "      <td>2018</td>\n",
       "      <td>4.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Sweden</td>\n",
       "      <td>2018</td>\n",
       "      <td>4.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Belgium</td>\n",
       "      <td>2018</td>\n",
       "      <td>4.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Austria</td>\n",
       "      <td>2018</td>\n",
       "      <td>4.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Japan</td>\n",
       "      <td>2018</td>\n",
       "      <td>4.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>630</th>\n",
       "      <td>Nepal</td>\n",
       "      <td>2012</td>\n",
       "      <td>2.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>631</th>\n",
       "      <td>Chad</td>\n",
       "      <td>2012</td>\n",
       "      <td>2.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>632</th>\n",
       "      <td>Haiti</td>\n",
       "      <td>2012</td>\n",
       "      <td>2.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>633</th>\n",
       "      <td>Djibouti</td>\n",
       "      <td>2012</td>\n",
       "      <td>1.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>634</th>\n",
       "      <td>Burundi</td>\n",
       "      <td>2012</td>\n",
       "      <td>1.61</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>635 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Country  Year  LPI Score\n",
       "0     Germany  2018       4.20\n",
       "1      Sweden  2018       4.05\n",
       "2     Belgium  2018       4.04\n",
       "3     Austria  2018       4.03\n",
       "4       Japan  2018       4.03\n",
       "..        ...   ...        ...\n",
       "630     Nepal  2012       2.04\n",
       "631      Chad  2012       2.03\n",
       "632     Haiti  2012       2.03\n",
       "633  Djibouti  2012       1.80\n",
       "634   Burundi  2012       1.61\n",
       "\n",
       "[635 rows x 3 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Removing the rows that are not required \n",
    "lpi_temp1 = lpi_temp1.drop(lpi_temp1.iloc[:, [2, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]], axis=1)\n",
    "display(lpi_temp1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "4dd3b4b1",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Year</th>\n",
       "      <th>LPI Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Germany</td>\n",
       "      <td>2018</td>\n",
       "      <td>4.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Sweden</td>\n",
       "      <td>2018</td>\n",
       "      <td>4.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Belgium</td>\n",
       "      <td>2018</td>\n",
       "      <td>4.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Austria</td>\n",
       "      <td>2018</td>\n",
       "      <td>4.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Japan</td>\n",
       "      <td>2018</td>\n",
       "      <td>4.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1265</th>\n",
       "      <td>Nepal</td>\n",
       "      <td>2012</td>\n",
       "      <td>2.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1266</th>\n",
       "      <td>Chad</td>\n",
       "      <td>2012</td>\n",
       "      <td>2.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1267</th>\n",
       "      <td>Haiti</td>\n",
       "      <td>2012</td>\n",
       "      <td>2.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1268</th>\n",
       "      <td>Djibouti</td>\n",
       "      <td>2012</td>\n",
       "      <td>1.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1269</th>\n",
       "      <td>Burundi</td>\n",
       "      <td>2012</td>\n",
       "      <td>1.61</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1270 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Country  Year  LPI Score\n",
       "0      Germany  2018       4.20\n",
       "1       Sweden  2018       4.05\n",
       "2      Belgium  2018       4.04\n",
       "3      Austria  2018       4.03\n",
       "4        Japan  2018       4.03\n",
       "...        ...   ...        ...\n",
       "1265     Nepal  2012       2.04\n",
       "1266      Chad  2012       2.03\n",
       "1267     Haiti  2012       2.03\n",
       "1268  Djibouti  2012       1.80\n",
       "1269   Burundi  2012       1.61\n",
       "\n",
       "[1270 rows x 3 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Since LPI scores are measured every two years, we are going to create rows for the odd years as well\n",
    "#The effect of this has been changed later, to ensure that the unit of analysis is the same for all variables. \n",
    "lpi_repeated = pd.concat([lpi_temp1]*2, ignore_index=True)\n",
    "display(lpi_repeated)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "d05a82f7",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Country       object\n",
       "Year           int64\n",
       "LPI Score    float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check data types\n",
    "lpi_repeated.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "4b152998",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>635</th>\n",
       "      <td>Germany</td>\n",
       "      <td>2018</td>\n",
       "      <td>4.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>636</th>\n",
       "      <td>Sweden</td>\n",
       "      <td>2018</td>\n",
       "      <td>4.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>637</th>\n",
       "      <td>Belgium</td>\n",
       "      <td>2018</td>\n",
       "      <td>4.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>638</th>\n",
       "      <td>Austria</td>\n",
       "      <td>2018</td>\n",
       "      <td>4.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>639</th>\n",
       "      <td>Japan</td>\n",
       "      <td>2018</td>\n",
       "      <td>4.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1265</th>\n",
       "      <td>Nepal</td>\n",
       "      <td>2012</td>\n",
       "      <td>2.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1266</th>\n",
       "      <td>Chad</td>\n",
       "      <td>2012</td>\n",
       "      <td>2.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1267</th>\n",
       "      <td>Haiti</td>\n",
       "      <td>2012</td>\n",
       "      <td>2.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1268</th>\n",
       "      <td>Djibouti</td>\n",
       "      <td>2012</td>\n",
       "      <td>1.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1269</th>\n",
       "      <td>Burundi</td>\n",
       "      <td>2012</td>\n",
       "      <td>1.61</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>635 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Country  Year  LPI Score\n",
       "635    Germany  2018       4.20\n",
       "636     Sweden  2018       4.05\n",
       "637    Belgium  2018       4.04\n",
       "638    Austria  2018       4.03\n",
       "639      Japan  2018       4.03\n",
       "...        ...   ...        ...\n",
       "1265     Nepal  2012       2.04\n",
       "1266      Chad  2012       2.03\n",
       "1267     Haiti  2012       2.03\n",
       "1268  Djibouti  2012       1.80\n",
       "1269   Burundi  2012       1.61\n",
       "\n",
       "[635 rows x 3 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Creating subset of data\n",
    "lpi_temp2 = lpi_repeated.loc[635:]\n",
    "display(lpi_temp2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "45125c7e",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Year</th>\n",
       "      <th>LPI Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>4.20</td>\n",
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       "    <tr>\n",
       "      <th>636</th>\n",
       "      <td>Sweden</td>\n",
       "      <td>2017</td>\n",
       "      <td>4.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>637</th>\n",
       "      <td>Belgium</td>\n",
       "      <td>2017</td>\n",
       "      <td>4.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>638</th>\n",
       "      <td>Austria</td>\n",
       "      <td>2017</td>\n",
       "      <td>4.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>639</th>\n",
       "      <td>Japan</td>\n",
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       "      <th>1265</th>\n",
       "      <td>Nepal</td>\n",
       "      <td>2011</td>\n",
       "      <td>2.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1266</th>\n",
       "      <td>Chad</td>\n",
       "      <td>2011</td>\n",
       "      <td>2.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1267</th>\n",
       "      <td>Haiti</td>\n",
       "      <td>2011</td>\n",
       "      <td>2.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1268</th>\n",
       "      <td>Djibouti</td>\n",
       "      <td>2011</td>\n",
       "      <td>1.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1269</th>\n",
       "      <td>Burundi</td>\n",
       "      <td>2011</td>\n",
       "      <td>1.61</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>635 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Country  Year  LPI Score\n",
       "635    Germany  2017       4.20\n",
       "636     Sweden  2017       4.05\n",
       "637    Belgium  2017       4.04\n",
       "638    Austria  2017       4.03\n",
       "639      Japan  2017       4.03\n",
       "...        ...   ...        ...\n",
       "1265     Nepal  2011       2.04\n",
       "1266      Chad  2011       2.03\n",
       "1267     Haiti  2011       2.03\n",
       "1268  Djibouti  2011       1.80\n",
       "1269   Burundi  2011       1.61\n",
       "\n",
       "[635 rows x 3 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Decrementing year value by 1, so we get data for the other years also\n",
    "lpi_temp2['Year'] = lpi_temp2['Year']-1\n",
    "display(lpi_temp2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "f88a6f4c",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>Country</th>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Belgium</td>\n",
       "      <td>2018</td>\n",
       "      <td>4.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Austria</td>\n",
       "      <td>2018</td>\n",
       "      <td>4.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Japan</td>\n",
       "      <td>2018</td>\n",
       "      <td>4.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>630</th>\n",
       "      <td>Nepal</td>\n",
       "      <td>2012</td>\n",
       "      <td>2.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>631</th>\n",
       "      <td>Chad</td>\n",
       "      <td>2012</td>\n",
       "      <td>2.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>632</th>\n",
       "      <td>Haiti</td>\n",
       "      <td>2012</td>\n",
       "      <td>2.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>633</th>\n",
       "      <td>Djibouti</td>\n",
       "      <td>2012</td>\n",
       "      <td>1.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>634</th>\n",
       "      <td>Burundi</td>\n",
       "      <td>2012</td>\n",
       "      <td>1.61</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>635 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Country  Year  LPI Score\n",
       "0     Germany  2018       4.20\n",
       "1      Sweden  2018       4.05\n",
       "2     Belgium  2018       4.04\n",
       "3     Austria  2018       4.03\n",
       "4       Japan  2018       4.03\n",
       "..        ...   ...        ...\n",
       "630     Nepal  2012       2.04\n",
       "631      Chad  2012       2.03\n",
       "632     Haiti  2012       2.03\n",
       "633  Djibouti  2012       1.80\n",
       "634   Burundi  2012       1.61\n",
       "\n",
       "[635 rows x 3 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#This subset is the first half of the data\n",
    "lpi_new = lpi_repeated.drop(lpi_repeated.index[635:])\n",
    "display(lpi_new)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "b46f3866",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>Country</th>\n",
       "      <th>Year</th>\n",
       "      <th>LPI Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>4.05</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Belgium</td>\n",
       "      <td>2018</td>\n",
       "      <td>4.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Austria</td>\n",
       "      <td>2018</td>\n",
       "      <td>4.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Japan</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>2011</td>\n",
       "      <td>2.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1266</th>\n",
       "      <td>Chad</td>\n",
       "      <td>2011</td>\n",
       "      <td>2.03</td>\n",
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       "    <tr>\n",
       "      <th>1267</th>\n",
       "      <td>Haiti</td>\n",
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       "      <td>2.03</td>\n",
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       "    <tr>\n",
       "      <th>1268</th>\n",
       "      <td>Djibouti</td>\n",
       "      <td>2011</td>\n",
       "      <td>1.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1269</th>\n",
       "      <td>Burundi</td>\n",
       "      <td>2011</td>\n",
       "      <td>1.61</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1270 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Country  Year  LPI Score\n",
       "0      Germany  2018       4.20\n",
       "1       Sweden  2018       4.05\n",
       "2      Belgium  2018       4.04\n",
       "3      Austria  2018       4.03\n",
       "4        Japan  2018       4.03\n",
       "...        ...   ...        ...\n",
       "1265     Nepal  2011       2.04\n",
       "1266      Chad  2011       2.03\n",
       "1267     Haiti  2011       2.03\n",
       "1268  Djibouti  2011       1.80\n",
       "1269   Burundi  2011       1.61\n",
       "\n",
       "[1270 rows x 3 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Now, we are combining both our subsets together\n",
    "lpi_temp3 = pd.concat([lpi_new,lpi_temp2],sort=False)\n",
    "display(lpi_temp3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "667b2b06",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:country_converter.country_converter:C.A.R. not found in regex\n",
      "WARNING:country_converter.country_converter:C.A.R. not found in regex\n",
      "WARNING:country_converter.country_converter:C.A.R. not found in regex\n",
      "WARNING:country_converter.country_converter:C.A.R. not found in regex\n",
      "WARNING:country_converter.country_converter:C.A.R. not found in regex\n",
      "WARNING:country_converter.country_converter:C.A.R. not found in regex\n"
     ]
    }
   ],
   "source": [
    "#Function call to standardize country names, so that merging can be done properly\n",
    "lpi_final = func_cnames(lpi_temp3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "3437c804",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "#Renaming column names\n",
    "lpi_final = lpi_final.rename(columns={0: 'Country', 1: \"Year\", 2 : \"LPI_Score\"}) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "aea963d1",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Year</th>\n",
       "      <th>LPI_Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>Germany</td>\n",
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       "    <tr>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Belgium</td>\n",
       "      <td>2018</td>\n",
       "      <td>4.04</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Austria</td>\n",
       "      <td>2018</td>\n",
       "      <td>4.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Japan</td>\n",
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       "    <tr>\n",
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       "      <td>2011</td>\n",
       "      <td>2.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1266</th>\n",
       "      <td>Chad</td>\n",
       "      <td>2011</td>\n",
       "      <td>2.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1267</th>\n",
       "      <td>Haiti</td>\n",
       "      <td>2011</td>\n",
       "      <td>2.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1268</th>\n",
       "      <td>Djibouti</td>\n",
       "      <td>2011</td>\n",
       "      <td>1.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1269</th>\n",
       "      <td>Burundi</td>\n",
       "      <td>2011</td>\n",
       "      <td>1.61</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1270 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Country  Year  LPI_Score\n",
       "0      Germany  2018       4.20\n",
       "1       Sweden  2018       4.05\n",
       "2      Belgium  2018       4.04\n",
       "3      Austria  2018       4.03\n",
       "4        Japan  2018       4.03\n",
       "...        ...   ...        ...\n",
       "1265     Nepal  2011       2.04\n",
       "1266      Chad  2011       2.03\n",
       "1267     Haiti  2011       2.03\n",
       "1268  Djibouti  2011       1.80\n",
       "1269   Burundi  2011       1.61\n",
       "\n",
       "[1270 rows x 3 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Check result\n",
    "display(lpi_final)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "21e123ba",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Country       object\n",
       "Year           int64\n",
       "LPI_Score    float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking data types\n",
    "lpi_final.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93e979e6",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "Merging the final LPI data with our previously merged dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "8e4ee77c",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "merge2 = merge1.merge(lpi_final,how=\"left\",on=[\"Country\", \"Year\"])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "55388368",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "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>Year</th>\n",
       "      <th>GFSI_Index</th>\n",
       "      <th>Arable_Land</th>\n",
       "      <th>Cereal_Yield</th>\n",
       "      <th>SalariedEmp</th>\n",
       "      <th>LPI_Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>2012</td>\n",
       "      <td>53.2</td>\n",
       "      <td>0.200795</td>\n",
       "      <td>1677.6</td>\n",
       "      <td>68.029999</td>\n",
       "      <td>2.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Angola</td>\n",
       "      <td>2012</td>\n",
       "      <td>40.1</td>\n",
       "      <td>0.187192</td>\n",
       "      <td>552.0</td>\n",
       "      <td>27.889999</td>\n",
       "      <td>2.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>2012</td>\n",
       "      <td>61.0</td>\n",
       "      <td>0.952573</td>\n",
       "      <td>4146.2</td>\n",
       "      <td>77.010002</td>\n",
       "      <td>3.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Australia</td>\n",
       "      <td>2012</td>\n",
       "      <td>70.9</td>\n",
       "      <td>1.391666</td>\n",
       "      <td>2231.6</td>\n",
       "      <td>82.830002</td>\n",
       "      <td>3.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Austria</td>\n",
       "      <td>2012</td>\n",
       "      <td>79.6</td>\n",
       "      <td>0.160748</td>\n",
       "      <td>6002.9</td>\n",
       "      <td>86.930000</td>\n",
       "      <td>3.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1125</th>\n",
       "      <td>Uzbekistan</td>\n",
       "      <td>2021</td>\n",
       "      <td>53.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1126</th>\n",
       "      <td>Venezuela</td>\n",
       "      <td>2021</td>\n",
       "      <td>39.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1127</th>\n",
       "      <td>Vietnam</td>\n",
       "      <td>2021</td>\n",
       "      <td>61.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1128</th>\n",
       "      <td>Yemen</td>\n",
       "      <td>2021</td>\n",
       "      <td>35.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1129</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>2021</td>\n",
       "      <td>38.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1130 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Country  Year GFSI_Index  Arable_Land  Cereal_Yield  SalariedEmp  \\\n",
       "0        Algeria  2012       53.2     0.200795        1677.6    68.029999   \n",
       "1         Angola  2012       40.1     0.187192         552.0    27.889999   \n",
       "2      Argentina  2012       61.0     0.952573        4146.2    77.010002   \n",
       "3      Australia  2012       70.9     1.391666        2231.6    82.830002   \n",
       "4        Austria  2012       79.6     0.160748        6002.9    86.930000   \n",
       "...          ...   ...        ...          ...           ...          ...   \n",
       "1125  Uzbekistan  2021       53.8          NaN           NaN          NaN   \n",
       "1126   Venezuela  2021       39.4          NaN           NaN          NaN   \n",
       "1127     Vietnam  2021       61.1          NaN           NaN          NaN   \n",
       "1128       Yemen  2021       35.7          NaN           NaN          NaN   \n",
       "1129      Zambia  2021       38.0          NaN           NaN          NaN   \n",
       "\n",
       "      LPI_Score  \n",
       "0          2.41  \n",
       "1          2.28  \n",
       "2          3.05  \n",
       "3          3.73  \n",
       "4          3.89  \n",
       "...         ...  \n",
       "1125        NaN  \n",
       "1126        NaN  \n",
       "1127        NaN  \n",
       "1128        NaN  \n",
       "1129        NaN  \n",
       "\n",
       "[1130 rows x 7 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Check the result\n",
    "display(merge2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "46a98bd0",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Country           0\n",
       "Year              0\n",
       "GFSI_Index        0\n",
       "Arable_Land     339\n",
       "Cereal_Yield    353\n",
       "SalariedEmp     226\n",
       "LPI_Score       372\n",
       "dtype: int64"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check for missing data\n",
    "merge2.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3337b944",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "##### Obtaining data on Poverty"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "bf89bb19",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Reading the csv file\n",
    "poverty_new = pd.read_csv(\"share-of-population-in-extreme-poverty.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "5aa32c1e",
   "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>Entity</th>\n",
       "      <th>Code</th>\n",
       "      <th>Year</th>\n",
       "      <th>$1.90 per day - share of population below poverty line</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Albania</td>\n",
       "      <td>ALB</td>\n",
       "      <td>1981</td>\n",
       "      <td>0.464528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>ALB</td>\n",
       "      <td>1982</td>\n",
       "      <td>0.407918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Albania</td>\n",
       "      <td>ALB</td>\n",
       "      <td>1983</td>\n",
       "      <td>0.464528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Albania</td>\n",
       "      <td>ALB</td>\n",
       "      <td>1984</td>\n",
       "      <td>0.534846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Albania</td>\n",
       "      <td>ALB</td>\n",
       "      <td>1985</td>\n",
       "      <td>0.534846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6948</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>2015</td>\n",
       "      <td>28.671930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6949</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>2016</td>\n",
       "      <td>32.366640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6950</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>2017</td>\n",
       "      <td>33.862690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6951</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>2018</td>\n",
       "      <td>33.216590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6952</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>2019</td>\n",
       "      <td>39.529950</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6953 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Entity Code  Year  \\\n",
       "0      Albania  ALB  1981   \n",
       "1      Albania  ALB  1982   \n",
       "2      Albania  ALB  1983   \n",
       "3      Albania  ALB  1984   \n",
       "4      Albania  ALB  1985   \n",
       "...        ...  ...   ...   \n",
       "6948  Zimbabwe  ZWE  2015   \n",
       "6949  Zimbabwe  ZWE  2016   \n",
       "6950  Zimbabwe  ZWE  2017   \n",
       "6951  Zimbabwe  ZWE  2018   \n",
       "6952  Zimbabwe  ZWE  2019   \n",
       "\n",
       "      $1.90 per day - share of population below poverty line  \n",
       "0                                              0.464528       \n",
       "1                                              0.407918       \n",
       "2                                              0.464528       \n",
       "3                                              0.534846       \n",
       "4                                              0.534846       \n",
       "...                                                 ...       \n",
       "6948                                          28.671930       \n",
       "6949                                          32.366640       \n",
       "6950                                          33.862690       \n",
       "6951                                          33.216590       \n",
       "6952                                          39.529950       \n",
       "\n",
       "[6953 rows x 4 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Check the results\n",
    "display(poverty_new)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "c1de40ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Remove rows before 2012\n",
    "poverty_new = poverty_new[poverty_new.Year>2011]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "e34d3e4e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Remove Code column \n",
    "poverty_new = poverty_new.drop(['Code'], axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "613dea41",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Change column names\n",
    "poverty_new = poverty_new.rename(columns={'Entity': 'Country', \"$1.90 per day - share of population below poverty line\" : \"Poverty Share\"})  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "48d69972",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Changing data types so that appropriate edits can be made\n",
    "poverty_new[\"Year\"] = poverty_new[\"Year\"].astype(str).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "7db4be33",
   "metadata": {},
   "outputs": [],
   "source": [
    "#We are making these changes as some of these do not get standardized properly using country convertor\n",
    "poverty_new = poverty_new[poverty_new[\"Country\"]!=\"China (Rural)\"]\n",
    "poverty_new = poverty_new[poverty_new[\"Country\"]!=\"China (Urban)\"]\n",
    "poverty_new = poverty_new[poverty_new[\"Country\"]!=\"India (Rural)\"]\n",
    "poverty_new = poverty_new[poverty_new[\"Country\"]!=\"India (Urban)\"]\n",
    "poverty_new = poverty_new[poverty_new[\"Country\"]!=\"Indonesia (Rural)\"]\n",
    "poverty_new = poverty_new[poverty_new[\"Country\"]!=\"Indonesia (Urban)\"]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "80a4ae12",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:country_converter.country_converter:East Asia and Pacific not found in regex\n",
      "WARNING:country_converter.country_converter:East Asia and Pacific not found in regex\n",
      "WARNING:country_converter.country_converter:East Asia and Pacific not found in regex\n",
      "WARNING:country_converter.country_converter:East Asia and Pacific not found in regex\n",
      "WARNING:country_converter.country_converter:East Asia and Pacific not found in regex\n",
      "WARNING:country_converter.country_converter:East Asia and Pacific not found in regex\n",
      "WARNING:country_converter.country_converter:East Asia and Pacific not found in regex\n",
      "WARNING:country_converter.country_converter:East Asia and Pacific not found in regex\n",
      "WARNING:country_converter.country_converter:Europe and Central Asia not found in regex\n",
      "WARNING:country_converter.country_converter:Europe and Central Asia not found in regex\n",
      "WARNING:country_converter.country_converter:Europe and Central Asia not found in regex\n",
      "WARNING:country_converter.country_converter:Europe and Central Asia not found in regex\n",
      "WARNING:country_converter.country_converter:Europe and Central Asia not found in regex\n",
      "WARNING:country_converter.country_converter:Europe and Central Asia not found in regex\n",
      "WARNING:country_converter.country_converter:Europe and Central Asia not found in regex\n",
      "WARNING:country_converter.country_converter:Europe and Central Asia not found in regex\n",
      "WARNING:country_converter.country_converter:Latin America and the Caribbean not found in regex\n",
      "WARNING:country_converter.country_converter:Latin America and the Caribbean not found in regex\n",
      "WARNING:country_converter.country_converter:Latin America and the Caribbean not found in regex\n",
      "WARNING:country_converter.country_converter:Latin America and the Caribbean not found in regex\n",
      "WARNING:country_converter.country_converter:Latin America and the Caribbean not found in regex\n",
      "WARNING:country_converter.country_converter:Latin America and the Caribbean not found in regex\n",
      "WARNING:country_converter.country_converter:Latin America and the Caribbean not found in regex\n",
      "WARNING:country_converter.country_converter:Latin America and the Caribbean not found in regex\n",
      "WARNING:country_converter.country_converter:Middle East and North Africa not found in regex\n",
      "WARNING:country_converter.country_converter:Middle East and North Africa not found in regex\n",
      "WARNING:country_converter.country_converter:Middle East and North Africa not found in regex\n",
      "WARNING:country_converter.country_converter:Middle East and North Africa not found in regex\n",
      "WARNING:country_converter.country_converter:Middle East and North Africa not found in regex\n",
      "WARNING:country_converter.country_converter:Middle East and North Africa not found in regex\n",
      "WARNING:country_converter.country_converter:Middle East and North Africa not found in regex\n",
      "WARNING:country_converter.country_converter:Other high Income (World Bank) not found in regex\n",
      "WARNING:country_converter.country_converter:Other high Income (World Bank) not found in regex\n",
      "WARNING:country_converter.country_converter:Other high Income (World Bank) not found in regex\n",
      "WARNING:country_converter.country_converter:Other high Income (World Bank) not found in regex\n",
      "WARNING:country_converter.country_converter:Other high Income (World Bank) not found in regex\n",
      "WARNING:country_converter.country_converter:Other high Income (World Bank) not found in regex\n",
      "WARNING:country_converter.country_converter:Other high Income (World Bank) not found in regex\n",
      "WARNING:country_converter.country_converter:Other high Income (World Bank) not found in regex\n",
      "WARNING:country_converter.country_converter:South Asia not found in regex\n",
      "WARNING:country_converter.country_converter:South Asia not found in regex\n",
      "WARNING:country_converter.country_converter:South Asia not found in regex\n",
      "WARNING:country_converter.country_converter:Sub-Saharan Africa not found in regex\n",
      "WARNING:country_converter.country_converter:Sub-Saharan Africa not found in regex\n",
      "WARNING:country_converter.country_converter:Sub-Saharan Africa not found in regex\n",
      "WARNING:country_converter.country_converter:Sub-Saharan Africa not found in regex\n",
      "WARNING:country_converter.country_converter:Sub-Saharan Africa not found in regex\n",
      "WARNING:country_converter.country_converter:Sub-Saharan Africa not found in regex\n",
      "WARNING:country_converter.country_converter:Sub-Saharan Africa not found in regex\n",
      "WARNING:country_converter.country_converter:Timor not found in regex\n",
      "WARNING:country_converter.country_converter:Timor not found in regex\n",
      "WARNING:country_converter.country_converter:Timor not found in regex\n",
      "WARNING:country_converter.country_converter:Timor not found in regex\n",
      "WARNING:country_converter.country_converter:Timor not found in regex\n",
      "WARNING:country_converter.country_converter:Timor not found in regex\n",
      "WARNING:country_converter.country_converter:Timor not found in regex\n",
      "WARNING:country_converter.country_converter:Timor not found in regex\n",
      "WARNING:country_converter.country_converter:World not found in regex\n",
      "WARNING:country_converter.country_converter:World not found in regex\n",
      "WARNING:country_converter.country_converter:World not found in regex\n",
      "WARNING:country_converter.country_converter:World not found in regex\n",
      "WARNING:country_converter.country_converter:World not found in regex\n",
      "WARNING:country_converter.country_converter:World not found in regex\n"
     ]
    }
   ],
   "source": [
    "#Call the country convertor function \n",
    "pnew_final = func_cnames(poverty_new)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "940a590c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Rename colummns\n",
    "pnew_final = pnew_final.rename(columns={0: 'Country', 1: \"Year\", 2 : \"Poverty_Share\"}) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "072a5571",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Changing data types so that appropriate edits can be made\n",
    "pnew_final[\"Year\"] = pnew_final[\"Year\"].astype(str).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "8ea41957",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Merge with our previous dataframe\n",
    "merge_pnew = merge2.merge(pnew_final,how=\"left\",on=[\"Country\", \"Year\"])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "4fb40802",
   "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>Year</th>\n",
       "      <th>GFSI_Index</th>\n",
       "      <th>Arable_Land</th>\n",
       "      <th>Cereal_Yield</th>\n",
       "      <th>SalariedEmp</th>\n",
       "      <th>LPI_Score</th>\n",
       "      <th>Poverty_Share</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>2012</td>\n",
       "      <td>53.2</td>\n",
       "      <td>0.200795</td>\n",
       "      <td>1677.6</td>\n",
       "      <td>68.029999</td>\n",
       "      <td>2.41</td>\n",
       "      <td>0.415515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Angola</td>\n",
       "      <td>2012</td>\n",
       "      <td>40.1</td>\n",
       "      <td>0.187192</td>\n",
       "      <td>552.0</td>\n",
       "      <td>27.889999</td>\n",
       "      <td>2.28</td>\n",
       "      <td>36.781340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>2012</td>\n",
       "      <td>61.0</td>\n",
       "      <td>0.952573</td>\n",
       "      <td>4146.2</td>\n",
       "      <td>77.010002</td>\n",
       "      <td>3.05</td>\n",
       "      <td>1.155554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Australia</td>\n",
       "      <td>2012</td>\n",
       "      <td>70.9</td>\n",
       "      <td>1.391666</td>\n",
       "      <td>2231.6</td>\n",
       "      <td>82.830002</td>\n",
       "      <td>3.73</td>\n",
       "      <td>0.423826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Austria</td>\n",
       "      <td>2012</td>\n",
       "      <td>79.6</td>\n",
       "      <td>0.160748</td>\n",
       "      <td>6002.9</td>\n",
       "      <td>86.930000</td>\n",
       "      <td>3.89</td>\n",
       "      <td>0.616917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1125</th>\n",
       "      <td>Uzbekistan</td>\n",
       "      <td>2021</td>\n",
       "      <td>53.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1126</th>\n",
       "      <td>Venezuela</td>\n",
       "      <td>2021</td>\n",
       "      <td>39.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1127</th>\n",
       "      <td>Vietnam</td>\n",
       "      <td>2021</td>\n",
       "      <td>61.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1128</th>\n",
       "      <td>Yemen</td>\n",
       "      <td>2021</td>\n",
       "      <td>35.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1129</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>2021</td>\n",
       "      <td>38.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1130 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Country  Year GFSI_Index  Arable_Land  Cereal_Yield  SalariedEmp  \\\n",
       "0        Algeria  2012       53.2     0.200795        1677.6    68.029999   \n",
       "1         Angola  2012       40.1     0.187192         552.0    27.889999   \n",
       "2      Argentina  2012       61.0     0.952573        4146.2    77.010002   \n",
       "3      Australia  2012       70.9     1.391666        2231.6    82.830002   \n",
       "4        Austria  2012       79.6     0.160748        6002.9    86.930000   \n",
       "...          ...   ...        ...          ...           ...          ...   \n",
       "1125  Uzbekistan  2021       53.8          NaN           NaN          NaN   \n",
       "1126   Venezuela  2021       39.4          NaN           NaN          NaN   \n",
       "1127     Vietnam  2021       61.1          NaN           NaN          NaN   \n",
       "1128       Yemen  2021       35.7          NaN           NaN          NaN   \n",
       "1129      Zambia  2021       38.0          NaN           NaN          NaN   \n",
       "\n",
       "      LPI_Score  Poverty_Share  \n",
       "0          2.41       0.415515  \n",
       "1          2.28      36.781340  \n",
       "2          3.05       1.155554  \n",
       "3          3.73       0.423826  \n",
       "4          3.89       0.616917  \n",
       "...         ...            ...  \n",
       "1125        NaN            NaN  \n",
       "1126        NaN            NaN  \n",
       "1127        NaN            NaN  \n",
       "1128        NaN            NaN  \n",
       "1129        NaN            NaN  \n",
       "\n",
       "[1130 rows x 8 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Check result\n",
    "display(merge_pnew)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "10a30c1e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking if there are any duplicates in the dataframe, there are none so that's good \n",
    "merge_pnew.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "4b0ff192",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Country            0\n",
       "Year               0\n",
       "GFSI_Index         0\n",
       "Arable_Land      339\n",
       "Cereal_Yield     353\n",
       "SalariedEmp      226\n",
       "LPI_Score        372\n",
       "Poverty_Share    297\n",
       "dtype: int64"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check missingness\n",
    "merge_pnew.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "edf241a8",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "##### Scraping the climate change performance index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "d5c4999d",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "#This the link for climate change index - this is one of the predictor variables of the model\n",
    "url_ci=\"https://en.wikipedia.org/wiki/Climate_Change_Performance_Index\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "6b075dcc",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "200"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "page_ci = requests.get(url_ci)   #Download the web-page\n",
    "page_ci.status_code              #Check status of download"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "3eed9f53",
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4\n"
     ]
    }
   ],
   "source": [
    "soup_ci = BeautifulSoup(page_ci.content, 'html.parser')  #Parsing\n",
    "\n",
    "#Obtaining tables\n",
    "t3 = soup_ci.findAll('table', {'class':\"wikitable\"})\n",
    "print(len(t3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "b45f53f9",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "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>Rank</th>\n",
       "      <th>Country</th>\n",
       "      <th>Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Sweden</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Morocco</td>\n",
       "      <td>70.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>Lithuania</td>\n",
       "      <td>70.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>Latvia</td>\n",
       "      <td>68.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>65.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>Switzerland</td>\n",
       "      <td>65.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>Malta</td>\n",
       "      <td>65.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>India</td>\n",
       "      <td>62.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>Norway</td>\n",
       "      <td>62.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>Finland</td>\n",
       "      <td>62.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>Croatia</td>\n",
       "      <td>62.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>Denmark</td>\n",
       "      <td>61.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>European Union (28)</td>\n",
       "      <td>60.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>Portugal</td>\n",
       "      <td>60.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>Ukraine</td>\n",
       "      <td>60.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>Luxembourg</td>\n",
       "      <td>59.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>Romania</td>\n",
       "      <td>59.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>21</td>\n",
       "      <td>France</td>\n",
       "      <td>59.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>22</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>59.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>23</td>\n",
       "      <td>Italy</td>\n",
       "      <td>58.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>24</td>\n",
       "      <td>Egypt</td>\n",
       "      <td>57.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>25</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>56.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>26</td>\n",
       "      <td>Slovak Republic</td>\n",
       "      <td>56.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>27</td>\n",
       "      <td>Germany</td>\n",
       "      <td>55.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>28</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>54.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>29</td>\n",
       "      <td>Belarus</td>\n",
       "      <td>53.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>30</td>\n",
       "      <td>Greece</td>\n",
       "      <td>50.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>31</td>\n",
       "      <td>Belgium</td>\n",
       "      <td>50.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>32</td>\n",
       "      <td>Czech Republic</td>\n",
       "      <td>49.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>33</td>\n",
       "      <td>China</td>\n",
       "      <td>49.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>34</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>49.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>35</td>\n",
       "      <td>Spain</td>\n",
       "      <td>48.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>36</td>\n",
       "      <td>Austria</td>\n",
       "      <td>48.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>37</td>\n",
       "      <td>Thailand</td>\n",
       "      <td>48.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>38</td>\n",
       "      <td>Indonesia</td>\n",
       "      <td>48.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>39</td>\n",
       "      <td>South Africa</td>\n",
       "      <td>48.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>40</td>\n",
       "      <td>Bulgaria</td>\n",
       "      <td>48.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>41</td>\n",
       "      <td>Poland</td>\n",
       "      <td>47.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>42</td>\n",
       "      <td>Hungary</td>\n",
       "      <td>46.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>43</td>\n",
       "      <td>Slovenia</td>\n",
       "      <td>44.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>44</td>\n",
       "      <td>New Zealand</td>\n",
       "      <td>44.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>45</td>\n",
       "      <td>Estonia</td>\n",
       "      <td>44.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>46</td>\n",
       "      <td>Cyprus</td>\n",
       "      <td>44.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>47</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>42.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>48</td>\n",
       "      <td>Ireland</td>\n",
       "      <td>40.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>49</td>\n",
       "      <td>Japan</td>\n",
       "      <td>40.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>50</td>\n",
       "      <td>Turkey</td>\n",
       "      <td>40.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>51</td>\n",
       "      <td>Malaysia</td>\n",
       "      <td>38.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>52</td>\n",
       "      <td>Russia</td>\n",
       "      <td>37.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>53</td>\n",
       "      <td>Kazakhstan</td>\n",
       "      <td>36.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>54</td>\n",
       "      <td>Canada</td>\n",
       "      <td>34.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>55</td>\n",
       "      <td>Australia</td>\n",
       "      <td>31.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>56</td>\n",
       "      <td>Chinese Taipei</td>\n",
       "      <td>28.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>57</td>\n",
       "      <td>Korea</td>\n",
       "      <td>28.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>58</td>\n",
       "      <td>Iran</td>\n",
       "      <td>23.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>59</td>\n",
       "      <td>United States</td>\n",
       "      <td>18.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>60</td>\n",
       "      <td>Saudi Arabia</td>\n",
       "      <td>8.82</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Rank              Country  Score\n",
       "0      1                    -      -\n",
       "1      2                    -      -\n",
       "2      3                    -      -\n",
       "3      4               Sweden     76\n",
       "4      5              Morocco  70.48\n",
       "5      6            Lithuania  70.47\n",
       "6      7               Latvia  68.31\n",
       "7      8       United Kingdom  65.92\n",
       "8      9          Switzerland  65.42\n",
       "9     10                Malta  65.06\n",
       "10    11                India  62.93\n",
       "11    12               Norway  62.80\n",
       "12    13              Finland  62.61\n",
       "13    14              Croatia  62.39\n",
       "14    15              Denmark  61.96\n",
       "15    16  European Union (28)  60.65\n",
       "16    17             Portugal  60.54\n",
       "17    18              Ukraine  60.09\n",
       "18    19           Luxembourg  59.92\n",
       "19    20              Romania  59.42\n",
       "20    21               France  59.30\n",
       "21    22               Brazil  59.29\n",
       "22    23                Italy  58.69\n",
       "23    24                Egypt  57.49\n",
       "24    25               Mexico  56.82\n",
       "25    26      Slovak Republic  56.61\n",
       "26    27              Germany  55.18\n",
       "27    28          Netherlands  54.11\n",
       "28    29              Belarus  53.31\n",
       "29    30               Greece  50.86\n",
       "30    31              Belgium  50.63\n",
       "31    32       Czech Republic  49.73\n",
       "32    33                China  49.60\n",
       "33    34            Argentina  49.01\n",
       "34    35                Spain  48.97\n",
       "35    36              Austria  48.78\n",
       "36    37             Thailand  48.71\n",
       "37    38            Indonesia  48.68\n",
       "38    39         South Africa  48.25\n",
       "39    40             Bulgaria  48.11\n",
       "40    41               Poland  47.59\n",
       "41    42              Hungary  46.79\n",
       "42    43             Slovenia  44.90\n",
       "43    44          New Zealand  44.61\n",
       "44    45              Estonia  44.37\n",
       "45    46               Cyprus  44.34\n",
       "46    47              Algeria  42.10\n",
       "47    48              Ireland  40.84\n",
       "48    49                Japan  40.63\n",
       "49    50               Turkey  40.22\n",
       "50    51             Malaysia  38.08\n",
       "51    52               Russia  37.59\n",
       "52    53           Kazakhstan  36.47\n",
       "53    54               Canada  34.26\n",
       "54    55            Australia  31.27\n",
       "55    56       Chinese Taipei  28.80\n",
       "56    57                Korea  28.53\n",
       "57    58                 Iran  23.94\n",
       "58    59        United States  18.82\n",
       "59    60         Saudi Arabia   8.82"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#The required table is the last one - so its index is 3 \n",
    "temp3 = t3[3]\n",
    "\n",
    "#Now, we are reading in the table, and converting it to dataframe\n",
    "df_temp3 = pd.read_html(str(temp3))\n",
    "df_ci = pd.DataFrame(df_temp3[0])\n",
    "display(df_ci)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "0c553ce7",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "#Removing Rank Column \n",
    "df_ci = df_ci.drop(['Rank'], axis=1)\n",
    "\n",
    "#To remove the first three rows\n",
    "df_ci = df_ci[df_ci.Country != \"-\"]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "0adc5e30",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "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>Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sweden</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Morocco</td>\n",
       "      <td>70.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Lithuania</td>\n",
       "      <td>70.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Latvia</td>\n",
       "      <td>68.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>65.92</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Country  Score\n",
       "3          Sweden     76\n",
       "4         Morocco  70.48\n",
       "5       Lithuania  70.47\n",
       "6          Latvia  68.31\n",
       "7  United Kingdom  65.92"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check result\n",
    "df_ci.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "7e155654",
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:country_converter.country_converter:European Union (28) not found in regex\n"
     ]
    }
   ],
   "source": [
    "#Function call to standardize country names, so that merging can be done properly\n",
    "ci_final = func_cnames(df_ci)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "563379ef",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "#Renaming column names\n",
    "ci_final = ci_final.rename(columns={0: 'Country', 1: \"CCPI\"}) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "4ea5f297",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "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>CCPI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Sweden</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Morocco</td>\n",
       "      <td>70.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Lithuania</td>\n",
       "      <td>70.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Latvia</td>\n",
       "      <td>68.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>65.92</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Country   CCPI\n",
       "0          Sweden     76\n",
       "1         Morocco  70.48\n",
       "2       Lithuania  70.47\n",
       "3          Latvia  68.31\n",
       "4  United Kingdom  65.92"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#To check result\n",
    "ci_final.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "898f1f22",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "Merging this dataframe with our previously merged dataframe. The Climate Change Performance Index (CCPI) column will then be changed into 0/1, where 0 corresponds to 'No record of CCPI' and 1 corresponds to CCPI recorded. Countries that allow for report of CCPI index have higher probability of working towards climate change concerns, and this factor is included for our analysis of food insecurity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "1951e7b3",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "#Merging the dataframes\n",
    "merge4 = merge_pnew.merge(ci_final,how=\"left\",on=\"Country\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "69fc44fc",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "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>Year</th>\n",
       "      <th>GFSI_Index</th>\n",
       "      <th>Arable_Land</th>\n",
       "      <th>Cereal_Yield</th>\n",
       "      <th>SalariedEmp</th>\n",
       "      <th>LPI_Score</th>\n",
       "      <th>Poverty_Share</th>\n",
       "      <th>CCPI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>2012</td>\n",
       "      <td>53.2</td>\n",
       "      <td>0.200795</td>\n",
       "      <td>1677.6</td>\n",
       "      <td>68.029999</td>\n",
       "      <td>2.41</td>\n",
       "      <td>0.415515</td>\n",
       "      <td>42.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Angola</td>\n",
       "      <td>2012</td>\n",
       "      <td>40.1</td>\n",
       "      <td>0.187192</td>\n",
       "      <td>552.0</td>\n",
       "      <td>27.889999</td>\n",
       "      <td>2.28</td>\n",
       "      <td>36.781340</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>2012</td>\n",
       "      <td>61.0</td>\n",
       "      <td>0.952573</td>\n",
       "      <td>4146.2</td>\n",
       "      <td>77.010002</td>\n",
       "      <td>3.05</td>\n",
       "      <td>1.155554</td>\n",
       "      <td>49.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Australia</td>\n",
       "      <td>2012</td>\n",
       "      <td>70.9</td>\n",
       "      <td>1.391666</td>\n",
       "      <td>2231.6</td>\n",
       "      <td>82.830002</td>\n",
       "      <td>3.73</td>\n",
       "      <td>0.423826</td>\n",
       "      <td>31.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Austria</td>\n",
       "      <td>2012</td>\n",
       "      <td>79.6</td>\n",
       "      <td>0.160748</td>\n",
       "      <td>6002.9</td>\n",
       "      <td>86.930000</td>\n",
       "      <td>3.89</td>\n",
       "      <td>0.616917</td>\n",
       "      <td>48.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1125</th>\n",
       "      <td>Uzbekistan</td>\n",
       "      <td>2021</td>\n",
       "      <td>53.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1126</th>\n",
       "      <td>Venezuela</td>\n",
       "      <td>2021</td>\n",
       "      <td>39.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1127</th>\n",
       "      <td>Vietnam</td>\n",
       "      <td>2021</td>\n",
       "      <td>61.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1128</th>\n",
       "      <td>Yemen</td>\n",
       "      <td>2021</td>\n",
       "      <td>35.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1129</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>2021</td>\n",
       "      <td>38.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1130 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Country  Year GFSI_Index  Arable_Land  Cereal_Yield  SalariedEmp  \\\n",
       "0        Algeria  2012       53.2     0.200795        1677.6    68.029999   \n",
       "1         Angola  2012       40.1     0.187192         552.0    27.889999   \n",
       "2      Argentina  2012       61.0     0.952573        4146.2    77.010002   \n",
       "3      Australia  2012       70.9     1.391666        2231.6    82.830002   \n",
       "4        Austria  2012       79.6     0.160748        6002.9    86.930000   \n",
       "...          ...   ...        ...          ...           ...          ...   \n",
       "1125  Uzbekistan  2021       53.8          NaN           NaN          NaN   \n",
       "1126   Venezuela  2021       39.4          NaN           NaN          NaN   \n",
       "1127     Vietnam  2021       61.1          NaN           NaN          NaN   \n",
       "1128       Yemen  2021       35.7          NaN           NaN          NaN   \n",
       "1129      Zambia  2021       38.0          NaN           NaN          NaN   \n",
       "\n",
       "      LPI_Score  Poverty_Share   CCPI  \n",
       "0          2.41       0.415515  42.10  \n",
       "1          2.28      36.781340    NaN  \n",
       "2          3.05       1.155554  49.01  \n",
       "3          3.73       0.423826  31.27  \n",
       "4          3.89       0.616917  48.78  \n",
       "...         ...            ...    ...  \n",
       "1125        NaN            NaN    NaN  \n",
       "1126        NaN            NaN    NaN  \n",
       "1127        NaN            NaN    NaN  \n",
       "1128        NaN            NaN    NaN  \n",
       "1129        NaN            NaN    NaN  \n",
       "\n",
       "[1130 rows x 9 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Check the result\n",
    "display(merge4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "95b1c40c",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
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       "      <th>Poverty_Share</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>2012</td>\n",
       "      <td>53.2</td>\n",
       "      <td>0.200795</td>\n",
       "      <td>1677.6</td>\n",
       "      <td>68.029999</td>\n",
       "      <td>2.41</td>\n",
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       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Angola</td>\n",
       "      <td>2012</td>\n",
       "      <td>40.1</td>\n",
       "      <td>0.187192</td>\n",
       "      <td>552.0</td>\n",
       "      <td>27.889999</td>\n",
       "      <td>2.28</td>\n",
       "      <td>36.781340</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>2012</td>\n",
       "      <td>61.0</td>\n",
       "      <td>0.952573</td>\n",
       "      <td>4146.2</td>\n",
       "      <td>77.010002</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <td>2012</td>\n",
       "      <td>70.9</td>\n",
       "      <td>1.391666</td>\n",
       "      <td>2231.6</td>\n",
       "      <td>82.830002</td>\n",
       "      <td>3.73</td>\n",
       "      <td>0.423826</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Austria</td>\n",
       "      <td>2012</td>\n",
       "      <td>79.6</td>\n",
       "      <td>0.160748</td>\n",
       "      <td>6002.9</td>\n",
       "      <td>86.930000</td>\n",
       "      <td>3.89</td>\n",
       "      <td>0.616917</td>\n",
       "      <td>1</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Country  Year GFSI_Index  Arable_Land  Cereal_Yield  SalariedEmp  \\\n",
       "0    Algeria  2012       53.2     0.200795        1677.6    68.029999   \n",
       "1     Angola  2012       40.1     0.187192         552.0    27.889999   \n",
       "2  Argentina  2012       61.0     0.952573        4146.2    77.010002   \n",
       "3  Australia  2012       70.9     1.391666        2231.6    82.830002   \n",
       "4    Austria  2012       79.6     0.160748        6002.9    86.930000   \n",
       "\n",
       "   LPI_Score  Poverty_Share  CCPI  \n",
       "0       2.41       0.415515     1  \n",
       "1       2.28      36.781340     0  \n",
       "2       3.05       1.155554     1  \n",
       "3       3.73       0.423826     1  \n",
       "4       3.89       0.616917     1  "
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Modify Column to 0 and 1\n",
    "merge4['CCPI'] = merge4['CCPI'].apply(lambda x: 0 if  pd.isna(x) else 1)\n",
    "\n",
    "#Check result\n",
    "merge4.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "712e8a40",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Country            0\n",
       "Year               0\n",
       "GFSI_Index         0\n",
       "Arable_Land      339\n",
       "Cereal_Yield     353\n",
       "SalariedEmp      226\n",
       "LPI_Score        372\n",
       "Poverty_Share    297\n",
       "CCPI               0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check missingness\n",
    "merge4.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "0a2abe89",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>Algeria</td>\n",
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       "      <td>1677.6</td>\n",
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       "    <tr>\n",
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       "      <td>Angola</td>\n",
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       "      <td>4146.2</td>\n",
       "      <td>77.010002</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Australia</td>\n",
       "      <td>2012</td>\n",
       "      <td>70.9</td>\n",
       "      <td>1.391666</td>\n",
       "      <td>2231.6</td>\n",
       "      <td>82.830002</td>\n",
       "      <td>3.73</td>\n",
       "      <td>0.423826</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Austria</td>\n",
       "      <td>2012</td>\n",
       "      <td>79.6</td>\n",
       "      <td>0.160748</td>\n",
       "      <td>6002.9</td>\n",
       "      <td>86.930000</td>\n",
       "      <td>3.89</td>\n",
       "      <td>0.616917</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>1125</th>\n",
       "      <td>Uzbekistan</td>\n",
       "      <td>2021</td>\n",
       "      <td>53.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1126</th>\n",
       "      <td>Venezuela</td>\n",
       "      <td>2021</td>\n",
       "      <td>39.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1127</th>\n",
       "      <td>Vietnam</td>\n",
       "      <td>2021</td>\n",
       "      <td>61.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1128</th>\n",
       "      <td>Yemen</td>\n",
       "      <td>2021</td>\n",
       "      <td>35.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1129</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>2021</td>\n",
       "      <td>38.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1130 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Country  Year GFSI_Index  Arable_Land  Cereal_Yield  SalariedEmp  \\\n",
       "0        Algeria  2012       53.2     0.200795        1677.6    68.029999   \n",
       "1         Angola  2012       40.1     0.187192         552.0    27.889999   \n",
       "2      Argentina  2012       61.0     0.952573        4146.2    77.010002   \n",
       "3      Australia  2012       70.9     1.391666        2231.6    82.830002   \n",
       "4        Austria  2012       79.6     0.160748        6002.9    86.930000   \n",
       "...          ...   ...        ...          ...           ...          ...   \n",
       "1125  Uzbekistan  2021       53.8          NaN           NaN          NaN   \n",
       "1126   Venezuela  2021       39.4          NaN           NaN          NaN   \n",
       "1127     Vietnam  2021       61.1          NaN           NaN          NaN   \n",
       "1128       Yemen  2021       35.7          NaN           NaN          NaN   \n",
       "1129      Zambia  2021       38.0          NaN           NaN          NaN   \n",
       "\n",
       "      LPI_Score  Poverty_Share  CCPI  \n",
       "0          2.41       0.415515     1  \n",
       "1          2.28      36.781340     0  \n",
       "2          3.05       1.155554     1  \n",
       "3          3.73       0.423826     1  \n",
       "4          3.89       0.616917     1  \n",
       "...         ...            ...   ...  \n",
       "1125        NaN            NaN     0  \n",
       "1126        NaN            NaN     0  \n",
       "1127        NaN            NaN     0  \n",
       "1128        NaN            NaN     0  \n",
       "1129        NaN            NaN     0  \n",
       "\n",
       "[1130 rows x 9 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Check result\n",
    "display(merge4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "9e92d4cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "#By analysing the data, we see that some predictor variables do not have data after 2018, so we will limit data till 2018 \n",
    "merge4 = merge4[merge4.Year<2019]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "d075f958",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>Angola</td>\n",
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       "      <th>2</th>\n",
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       "      <td>4146.2</td>\n",
       "      <td>77.010002</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Australia</td>\n",
       "      <td>2012</td>\n",
       "      <td>70.9</td>\n",
       "      <td>1.391666</td>\n",
       "      <td>2231.6</td>\n",
       "      <td>82.830002</td>\n",
       "      <td>3.73</td>\n",
       "      <td>0.423826</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Austria</td>\n",
       "      <td>2012</td>\n",
       "      <td>79.6</td>\n",
       "      <td>0.160748</td>\n",
       "      <td>6002.9</td>\n",
       "      <td>86.930000</td>\n",
       "      <td>3.89</td>\n",
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       "    <tr>\n",
       "      <th>786</th>\n",
       "      <td>Uzbekistan</td>\n",
       "      <td>2018</td>\n",
       "      <td>55.0</td>\n",
       "      <td>0.123331</td>\n",
       "      <td>4055.9</td>\n",
       "      <td>64.809998</td>\n",
       "      <td>2.58</td>\n",
       "      <td>11.729680</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>787</th>\n",
       "      <td>Venezuela</td>\n",
       "      <td>2018</td>\n",
       "      <td>45.8</td>\n",
       "      <td>0.090006</td>\n",
       "      <td>3395.0</td>\n",
       "      <td>58.820000</td>\n",
       "      <td>2.23</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>788</th>\n",
       "      <td>Vietnam</td>\n",
       "      <td>2018</td>\n",
       "      <td>62.9</td>\n",
       "      <td>0.073141</td>\n",
       "      <td>5685.4</td>\n",
       "      <td>43.939999</td>\n",
       "      <td>3.27</td>\n",
       "      <td>1.801464</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>789</th>\n",
       "      <td>Yemen</td>\n",
       "      <td>2018</td>\n",
       "      <td>34.7</td>\n",
       "      <td>0.038518</td>\n",
       "      <td>759.2</td>\n",
       "      <td>45.299999</td>\n",
       "      <td>2.27</td>\n",
       "      <td>54.537280</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>790</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>2018</td>\n",
       "      <td>41.9</td>\n",
       "      <td>0.218999</td>\n",
       "      <td>2157.8</td>\n",
       "      <td>24.860001</td>\n",
       "      <td>2.53</td>\n",
       "      <td>58.195540</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>791 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Country  Year GFSI_Index  Arable_Land  Cereal_Yield  SalariedEmp  \\\n",
       "0       Algeria  2012       53.2     0.200795        1677.6    68.029999   \n",
       "1        Angola  2012       40.1     0.187192         552.0    27.889999   \n",
       "2     Argentina  2012       61.0     0.952573        4146.2    77.010002   \n",
       "3     Australia  2012       70.9     1.391666        2231.6    82.830002   \n",
       "4       Austria  2012       79.6     0.160748        6002.9    86.930000   \n",
       "..          ...   ...        ...          ...           ...          ...   \n",
       "786  Uzbekistan  2018       55.0     0.123331        4055.9    64.809998   \n",
       "787   Venezuela  2018       45.8     0.090006        3395.0    58.820000   \n",
       "788     Vietnam  2018       62.9     0.073141        5685.4    43.939999   \n",
       "789       Yemen  2018       34.7     0.038518         759.2    45.299999   \n",
       "790      Zambia  2018       41.9     0.218999        2157.8    24.860001   \n",
       "\n",
       "     LPI_Score  Poverty_Share  CCPI  \n",
       "0         2.41       0.415515     1  \n",
       "1         2.28      36.781340     0  \n",
       "2         3.05       1.155554     1  \n",
       "3         3.73       0.423826     1  \n",
       "4         3.89       0.616917     1  \n",
       "..         ...            ...   ...  \n",
       "786       2.58      11.729680     0  \n",
       "787       2.23            NaN     0  \n",
       "788       3.27       1.801464     0  \n",
       "789       2.27      54.537280     0  \n",
       "790       2.53      58.195540     0  \n",
       "\n",
       "[791 rows x 9 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Check result\n",
    "display(merge4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "3c901020",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Country           0\n",
       "Year              0\n",
       "GFSI_Index        0\n",
       "Arable_Land       0\n",
       "Cereal_Yield     14\n",
       "SalariedEmp       0\n",
       "LPI_Score        33\n",
       "Poverty_Share    61\n",
       "CCPI              0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check missingness\n",
    "merge4.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76b53464",
   "metadata": {},
   "source": [
    "##### Net Food Trade"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "5285183e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Reading in the excel files\n",
    "ft_2012 = pd.read_excel('WITS-Partner-2012.xlsx', sheet_name='Partner')\n",
    "ft_2013 = pd.read_excel('WITS-Partner-2013.xlsx', sheet_name='Partner')\n",
    "ft_2014 = pd.read_excel('WITS-Partner-2014.xlsx', sheet_name='Partner')\n",
    "ft_2015 = pd.read_excel('WITS-Partner-2015.xlsx', sheet_name='Partner')\n",
    "ft_2016 = pd.read_excel('WITS-Partner-2016.xlsx', sheet_name='Partner')\n",
    "ft_2017 = pd.read_excel('WITS-Partner-2017.xlsx', sheet_name='Partner')\n",
    "ft_2018 = pd.read_excel('WITS-Partner-2018.xlsx', sheet_name='Partner')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "229ceae5",
   "metadata": {},
   "outputs": [],
   "source": [
    "#To ensure that all floats are not in standard form\n",
    "pd.set_option('display.float_format', lambda x: '%.3f' % x)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "b7bc54b1",
   "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",
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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>Reporter Name</th>\n",
       "      <th>Partner Name</th>\n",
       "      <th>Year</th>\n",
       "      <th>Trade Flow</th>\n",
       "      <th>Product Group</th>\n",
       "      <th>Export (US$ Thousand)</th>\n",
       "      <th>Import (US$ Thousand)</th>\n",
       "      <th>Export Product Share (%)</th>\n",
       "      <th>Import Product Share (%)</th>\n",
       "      <th>Revealed comparative advantage</th>\n",
       "      <th>...</th>\n",
       "      <th>MFN Total Tariff Lines</th>\n",
       "      <th>MFN Dutiable Tariff Lines Share (%)</th>\n",
       "      <th>MFN Duty Free Tariff Lines Share (%)</th>\n",
       "      <th>MFN Specific Tariff Lines Share (%)</th>\n",
       "      <th>MFN AVE Tariff Lines Share (%)</th>\n",
       "      <th>MFN MaxRate (%)</th>\n",
       "      <th>MFN MinRate (%)</th>\n",
       "      <th>MFN SpecificDuty Imports (US$ Thousand)</th>\n",
       "      <th>MFN Dutiable Imports (US$ Thousand)</th>\n",
       "      <th>MFN Duty Free Imports (US$ Thousand)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>World</td>\n",
       "      <td>Aruba</td>\n",
       "      <td>2016</td>\n",
       "      <td>EXPIMP</td>\n",
       "      <td>Food Products</td>\n",
       "      <td>188496.310</td>\n",
       "      <td>5987.150</td>\n",
       "      <td>10.300</td>\n",
       "      <td>3.380</td>\n",
       "      <td>1.000</td>\n",
       "      <td>...</td>\n",
       "      <td>60469.000</td>\n",
       "      <td>50.060</td>\n",
       "      <td>10.170</td>\n",
       "      <td>218.800</td>\n",
       "      <td>1115.140</td>\n",
       "      <td>3000.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>15864.680</td>\n",
       "      <td>15990.970</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>World</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2016</td>\n",
       "      <td>EXPIMP</td>\n",
       "      <td>Food Products</td>\n",
       "      <td>760218.990</td>\n",
       "      <td>1914.660</td>\n",
       "      <td>8.220</td>\n",
       "      <td>0.180</td>\n",
       "      <td>1.000</td>\n",
       "      <td>...</td>\n",
       "      <td>36560.000</td>\n",
       "      <td>62.110</td>\n",
       "      <td>9.460</td>\n",
       "      <td>232.620</td>\n",
       "      <td>875.480</td>\n",
       "      <td>3000.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1600.530</td>\n",
       "      <td>1944.650</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>World</td>\n",
       "      <td>Angola</td>\n",
       "      <td>2016</td>\n",
       "      <td>EXPIMP</td>\n",
       "      <td>Food Products</td>\n",
       "      <td>1070282.490</td>\n",
       "      <td>15574.570</td>\n",
       "      <td>6.930</td>\n",
       "      <td>0.060</td>\n",
       "      <td>1.000</td>\n",
       "      <td>...</td>\n",
       "      <td>57229.000</td>\n",
       "      <td>54.440</td>\n",
       "      <td>9.040</td>\n",
       "      <td>134.240</td>\n",
       "      <td>784.530</td>\n",
       "      <td>3000.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1216.790</td>\n",
       "      <td>13481.420</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>World</td>\n",
       "      <td>Anguila</td>\n",
       "      <td>2016</td>\n",
       "      <td>EXPIMP</td>\n",
       "      <td>Food Products</td>\n",
       "      <td>8994.930</td>\n",
       "      <td>2754.240</td>\n",
       "      <td>12.480</td>\n",
       "      <td>21.750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>14555.000</td>\n",
       "      <td>50.680</td>\n",
       "      <td>14.750</td>\n",
       "      <td>818.750</td>\n",
       "      <td>1427.230</td>\n",
       "      <td>3000.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>3876.620</td>\n",
       "      <td>5209.830</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>World</td>\n",
       "      <td>Albania</td>\n",
       "      <td>2016</td>\n",
       "      <td>EXPIMP</td>\n",
       "      <td>Food Products</td>\n",
       "      <td>575401.730</td>\n",
       "      <td>64813.290</td>\n",
       "      <td>7.740</td>\n",
       "      <td>3.170</td>\n",
       "      <td>1.000</td>\n",
       "      <td>...</td>\n",
       "      <td>70404.000</td>\n",
       "      <td>56.700</td>\n",
       "      <td>9.360</td>\n",
       "      <td>104.030</td>\n",
       "      <td>493.840</td>\n",
       "      <td>3000.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>40530.120</td>\n",
       "      <td>98835.910</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>239</th>\n",
       "      <td>World</td>\n",
       "      <td>Latin America &amp; Caribbean</td>\n",
       "      <td>2016</td>\n",
       "      <td>EXPIMP</td>\n",
       "      <td>Food Products</td>\n",
       "      <td>34837788.520</td>\n",
       "      <td>71659809.740</td>\n",
       "      <td>3.460</td>\n",
       "      <td>7.610</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>106830.000</td>\n",
       "      <td>59.350</td>\n",
       "      <td>10.530</td>\n",
       "      <td>2.210</td>\n",
       "      <td>12.460</td>\n",
       "      <td>3000.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>52515963.740</td>\n",
       "      <td>86336180.960</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>240</th>\n",
       "      <td>World</td>\n",
       "      <td>Middle East &amp; North Africa</td>\n",
       "      <td>2016</td>\n",
       "      <td>EXPIMP</td>\n",
       "      <td>Food Products</td>\n",
       "      <td>47919169.020</td>\n",
       "      <td>9846670.490</td>\n",
       "      <td>4.370</td>\n",
       "      <td>1.330</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>106995.000</td>\n",
       "      <td>59.380</td>\n",
       "      <td>10.540</td>\n",
       "      <td>3.330</td>\n",
       "      <td>18.800</td>\n",
       "      <td>3000.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>6464209.680</td>\n",
       "      <td>9658857.100</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>241</th>\n",
       "      <td>World</td>\n",
       "      <td>North America</td>\n",
       "      <td>2016</td>\n",
       "      <td>EXPIMP</td>\n",
       "      <td>Food Products</td>\n",
       "      <td>95784568.780</td>\n",
       "      <td>57632682.610</td>\n",
       "      <td>3.330</td>\n",
       "      <td>3.270</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>106829.000</td>\n",
       "      <td>59.350</td>\n",
       "      <td>10.530</td>\n",
       "      <td>5.710</td>\n",
       "      <td>32.200</td>\n",
       "      <td>3000.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>45852915.410</td>\n",
       "      <td>62071728.690</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242</th>\n",
       "      <td>World</td>\n",
       "      <td>South Asia</td>\n",
       "      <td>2016</td>\n",
       "      <td>EXPIMP</td>\n",
       "      <td>Food Products</td>\n",
       "      <td>7445571.870</td>\n",
       "      <td>6660846.770</td>\n",
       "      <td>1.580</td>\n",
       "      <td>2.100</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>106251.000</td>\n",
       "      <td>59.250</td>\n",
       "      <td>10.510</td>\n",
       "      <td>7.240</td>\n",
       "      <td>40.790</td>\n",
       "      <td>3000.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>4744673.840</td>\n",
       "      <td>6570593.470</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>243</th>\n",
       "      <td>World</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>2016</td>\n",
       "      <td>EXPIMP</td>\n",
       "      <td>Food Products</td>\n",
       "      <td>21223203.820</td>\n",
       "      <td>19989376.740</td>\n",
       "      <td>5.660</td>\n",
       "      <td>7.100</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>106801.000</td>\n",
       "      <td>59.390</td>\n",
       "      <td>10.470</td>\n",
       "      <td>3.940</td>\n",
       "      <td>22.230</td>\n",
       "      <td>3000.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>12582978.700</td>\n",
       "      <td>30078330.980</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>244 rows × 36 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Reporter Name                Partner Name  Year Trade Flow  Product Group  \\\n",
       "0           World                       Aruba  2016     EXPIMP  Food Products   \n",
       "1           World                 Afghanistan  2016     EXPIMP  Food Products   \n",
       "2           World                      Angola  2016     EXPIMP  Food Products   \n",
       "3           World                     Anguila  2016     EXPIMP  Food Products   \n",
       "4           World                     Albania  2016     EXPIMP  Food Products   \n",
       "..            ...                         ...   ...        ...            ...   \n",
       "239         World   Latin America & Caribbean  2016     EXPIMP  Food Products   \n",
       "240         World  Middle East & North Africa  2016     EXPIMP  Food Products   \n",
       "241         World               North America  2016     EXPIMP  Food Products   \n",
       "242         World                  South Asia  2016     EXPIMP  Food Products   \n",
       "243         World          Sub-Saharan Africa  2016     EXPIMP  Food Products   \n",
       "\n",
       "     Export (US$ Thousand)  Import (US$ Thousand)  Export Product Share (%)  \\\n",
       "0               188496.310               5987.150                    10.300   \n",
       "1               760218.990               1914.660                     8.220   \n",
       "2              1070282.490              15574.570                     6.930   \n",
       "3                 8994.930               2754.240                    12.480   \n",
       "4               575401.730              64813.290                     7.740   \n",
       "..                     ...                    ...                       ...   \n",
       "239           34837788.520           71659809.740                     3.460   \n",
       "240           47919169.020            9846670.490                     4.370   \n",
       "241           95784568.780           57632682.610                     3.330   \n",
       "242            7445571.870            6660846.770                     1.580   \n",
       "243           21223203.820           19989376.740                     5.660   \n",
       "\n",
       "     Import Product Share (%)  Revealed comparative advantage  ...  \\\n",
       "0                       3.380                           1.000  ...   \n",
       "1                       0.180                           1.000  ...   \n",
       "2                       0.060                           1.000  ...   \n",
       "3                      21.750                             NaN  ...   \n",
       "4                       3.170                           1.000  ...   \n",
       "..                        ...                             ...  ...   \n",
       "239                     7.610                             NaN  ...   \n",
       "240                     1.330                             NaN  ...   \n",
       "241                     3.270                             NaN  ...   \n",
       "242                     2.100                             NaN  ...   \n",
       "243                     7.100                             NaN  ...   \n",
       "\n",
       "     MFN Total Tariff Lines  MFN Dutiable Tariff Lines Share (%)  \\\n",
       "0                 60469.000                               50.060   \n",
       "1                 36560.000                               62.110   \n",
       "2                 57229.000                               54.440   \n",
       "3                 14555.000                               50.680   \n",
       "4                 70404.000                               56.700   \n",
       "..                      ...                                  ...   \n",
       "239              106830.000                               59.350   \n",
       "240              106995.000                               59.380   \n",
       "241              106829.000                               59.350   \n",
       "242              106251.000                               59.250   \n",
       "243              106801.000                               59.390   \n",
       "\n",
       "     MFN Duty Free Tariff Lines Share (%)  \\\n",
       "0                                  10.170   \n",
       "1                                   9.460   \n",
       "2                                   9.040   \n",
       "3                                  14.750   \n",
       "4                                   9.360   \n",
       "..                                    ...   \n",
       "239                                10.530   \n",
       "240                                10.540   \n",
       "241                                10.530   \n",
       "242                                10.510   \n",
       "243                                10.470   \n",
       "\n",
       "     MFN Specific Tariff Lines Share (%)  MFN AVE Tariff Lines Share (%)  \\\n",
       "0                                218.800                        1115.140   \n",
       "1                                232.620                         875.480   \n",
       "2                                134.240                         784.530   \n",
       "3                                818.750                        1427.230   \n",
       "4                                104.030                         493.840   \n",
       "..                                   ...                             ...   \n",
       "239                                2.210                          12.460   \n",
       "240                                3.330                          18.800   \n",
       "241                                5.710                          32.200   \n",
       "242                                7.240                          40.790   \n",
       "243                                3.940                          22.230   \n",
       "\n",
       "     MFN MaxRate (%)  MFN MinRate (%)  \\\n",
       "0           3000.000            0.000   \n",
       "1           3000.000            0.000   \n",
       "2           3000.000            0.000   \n",
       "3           3000.000            0.000   \n",
       "4           3000.000            0.000   \n",
       "..               ...              ...   \n",
       "239         3000.000            0.000   \n",
       "240         3000.000            0.000   \n",
       "241         3000.000            0.000   \n",
       "242         3000.000            0.000   \n",
       "243         3000.000            0.000   \n",
       "\n",
       "     MFN SpecificDuty Imports (US$ Thousand)  \\\n",
       "0                                  15864.680   \n",
       "1                                   1600.530   \n",
       "2                                   1216.790   \n",
       "3                                   3876.620   \n",
       "4                                  40530.120   \n",
       "..                                       ...   \n",
       "239                             52515963.740   \n",
       "240                              6464209.680   \n",
       "241                             45852915.410   \n",
       "242                              4744673.840   \n",
       "243                             12582978.700   \n",
       "\n",
       "     MFN Dutiable Imports (US$ Thousand)  MFN Duty Free Imports (US$ Thousand)  \n",
       "0                              15990.970                                 0.000  \n",
       "1                               1944.650                                 0.000  \n",
       "2                              13481.420                                 0.000  \n",
       "3                               5209.830                                 0.000  \n",
       "4                              98835.910                                 0.000  \n",
       "..                                   ...                                   ...  \n",
       "239                         86336180.960                                 0.000  \n",
       "240                          9658857.100                                 0.000  \n",
       "241                         62071728.690                                 0.000  \n",
       "242                          6570593.470                                 0.000  \n",
       "243                         30078330.980                                 0.000  \n",
       "\n",
       "[244 rows x 36 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Checking a random result\n",
    "display(ft_2016)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "0d49af38",
   "metadata": {},
   "outputs": [],
   "source": [
    "def func_ft(df=None):\n",
    "    \"\"\"\n",
    "    The food trade related dataframes will be passed as arguments to func_ft, which will remove the unnecessary columns, \n",
    "    and call the func_cnames function to standardize country names so that merging can be performed easily.\n",
    "    ------------------------------------------------------------------------------------------------------------------------\n",
    "    Arguments : \n",
    "    df -> It is a dataframe, and default value is set to None\n",
    "    ------------------------------------------------------------------------------------------------------------------------\n",
    "    Return Value:\n",
    "    The modified dataframe df_temp\n",
    "    \"\"\"\n",
    "    #Taking only the required columns\n",
    "    df = df[['Partner Name','Year', 'Export (US$ Thousand)', 'Import (US$ Thousand)']]\n",
    "    \n",
    "    #Removing this particular row as it does not get standardized properly using country convertor. \n",
    "    df = df[df['Partner Name'] != \"United States Minor Outlying I\"]\n",
    "\n",
    "    #Calling the func_cnames function to standardize country names\n",
    "    df_temp = func_cnames(df)\n",
    "    \n",
    "    #Change the column  names\n",
    "    df_temp = df_temp.rename(columns={0: 'Country', 1 : 'Year', 2 : 'Export', 3: 'Import'}) \n",
    "    \n",
    "    #Change Year type so that it is easier to merge later\n",
    "    df_temp[\"Year\"] = df_temp[\"Year\"].astype(str).astype(int)\n",
    "    \n",
    "    #Create a new column -> Net Food Trade, as the difference between Export and Import\n",
    "    df_temp['Net_Food_Trade'] = df_temp['Export'] - df_temp['Import']\n",
    "    \n",
    "    #Drop Export and Import columns \n",
    "    df_temp = df_temp.drop(['Export', 'Import'], axis=1)\n",
    "\n",
    "    \n",
    "    #Return the dataframe\n",
    "    return(df_temp)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "6d979e2e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
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      "WARNING:country_converter.country_converter:Bunkers not found in regex\n",
      "WARNING:country_converter.country_converter:Free Zones not found in regex\n",
      "WARNING:country_converter.country_converter:Other Asia, nes not found in regex\n",
      "WARNING:country_converter.country_converter:Occ.Pal.Terr not found in regex\n",
      "WARNING:country_converter.country_converter:Serbia, FR(Serbia/Montenegro) not found in regex\n",
      "WARNING:country_converter.country_converter:Special Categories not found in regex\n",
      "WARNING:country_converter.country_converter:Unspecified not found in regex\n",
      "WARNING:country_converter.country_converter: World not found in regex\n",
      "WARNING:country_converter.country_converter:East Asia & Pacific not found in regex\n",
      "WARNING:country_converter.country_converter:Europe & Central Asia not found in regex\n",
      "WARNING:country_converter.country_converter:Latin America & Caribbean not found in regex\n",
      "WARNING:country_converter.country_converter:Middle East & North Africa not found in regex\n",
      "WARNING:country_converter.country_converter:North America not found in regex\n",
      "WARNING:country_converter.country_converter:South Asia not found in regex\n",
      "WARNING:country_converter.country_converter:Sub-Saharan Africa not found in regex\n",
      "WARNING:country_converter.country_converter:Bonaire not found in regex\n",
      "WARNING:country_converter.country_converter:Bunkers not found in regex\n",
      "WARNING:country_converter.country_converter:Free Zones not found in regex\n",
      "WARNING:country_converter.country_converter:Other Asia, nes not found in regex\n",
      "WARNING:country_converter.country_converter:Occ.Pal.Terr not found in regex\n",
      "WARNING:country_converter.country_converter:Serbia, FR(Serbia/Montenegro) not found in regex\n",
      "WARNING:country_converter.country_converter:Special Categories not found in regex\n",
      "WARNING:country_converter.country_converter:Unspecified not found in regex\n",
      "WARNING:country_converter.country_converter: World not found in regex\n",
      "WARNING:country_converter.country_converter:East Asia & Pacific not found in regex\n",
      "WARNING:country_converter.country_converter:Europe & Central Asia not found in regex\n",
      "WARNING:country_converter.country_converter:Latin America & Caribbean not found in regex\n",
      "WARNING:country_converter.country_converter:Middle East & North Africa not found in regex\n",
      "WARNING:country_converter.country_converter:North America not found in regex\n",
      "WARNING:country_converter.country_converter:South Asia not found in regex\n",
      "WARNING:country_converter.country_converter:Sub-Saharan Africa not found in regex\n",
      "WARNING:country_converter.country_converter:Bonaire not found in regex\n",
      "WARNING:country_converter.country_converter:Bunkers not found in regex\n",
      "WARNING:country_converter.country_converter:Free Zones not found in regex\n",
      "WARNING:country_converter.country_converter:Other Asia, nes not found in regex\n",
      "WARNING:country_converter.country_converter:Occ.Pal.Terr not found in regex\n",
      "WARNING:country_converter.country_converter:Serbia, FR(Serbia/Montenegro) not found in regex\n",
      "WARNING:country_converter.country_converter:Special Categories not found in regex\n",
      "WARNING:country_converter.country_converter:Unspecified not found in regex\n",
      "WARNING:country_converter.country_converter: World not found in regex\n",
      "WARNING:country_converter.country_converter:East Asia & Pacific not found in regex\n",
      "WARNING:country_converter.country_converter:Europe & Central Asia not found in regex\n",
      "WARNING:country_converter.country_converter:Latin America & Caribbean not found in regex\n",
      "WARNING:country_converter.country_converter:Middle East & North Africa not found in regex\n",
      "WARNING:country_converter.country_converter:North America not found in regex\n",
      "WARNING:country_converter.country_converter:South Asia not found in regex\n",
      "WARNING:country_converter.country_converter:Sub-Saharan Africa not found in regex\n",
      "WARNING:country_converter.country_converter:Bonaire not found in regex\n",
      "WARNING:country_converter.country_converter:Bunkers not found in regex\n",
      "WARNING:country_converter.country_converter:Free Zones not found in regex\n",
      "WARNING:country_converter.country_converter:Other Asia, nes not found in regex\n",
      "WARNING:country_converter.country_converter:Occ.Pal.Terr not found in regex\n",
      "WARNING:country_converter.country_converter:Serbia, FR(Serbia/Montenegro) not found in regex\n",
      "WARNING:country_converter.country_converter:Special Categories not found in regex\n",
      "WARNING:country_converter.country_converter:Unspecified not found in regex\n",
      "WARNING:country_converter.country_converter: World not found in regex\n",
      "WARNING:country_converter.country_converter:East Asia & Pacific not found in regex\n",
      "WARNING:country_converter.country_converter:Europe & Central Asia not found in regex\n",
      "WARNING:country_converter.country_converter:Latin America & Caribbean not found in regex\n",
      "WARNING:country_converter.country_converter:Middle East & North Africa not found in regex\n",
      "WARNING:country_converter.country_converter:North America not found in regex\n",
      "WARNING:country_converter.country_converter:South Asia not found in regex\n",
      "WARNING:country_converter.country_converter:Sub-Saharan Africa not found in regex\n",
      "WARNING:country_converter.country_converter:Bonaire not found in regex\n",
      "WARNING:country_converter.country_converter:Bunkers not found in regex\n",
      "WARNING:country_converter.country_converter:Free Zones not found in regex\n",
      "WARNING:country_converter.country_converter:Other Asia, nes not found in regex\n",
      "WARNING:country_converter.country_converter:Occ.Pal.Terr not found in regex\n",
      "WARNING:country_converter.country_converter:Serbia, FR(Serbia/Montenegro) not found in regex\n",
      "WARNING:country_converter.country_converter:Special Categories not found in regex\n",
      "WARNING:country_converter.country_converter:Unspecified not found in regex\n",
      "WARNING:country_converter.country_converter: World not found in regex\n",
      "WARNING:country_converter.country_converter:East Asia & Pacific not found in regex\n",
      "WARNING:country_converter.country_converter:Europe & Central Asia not found in regex\n",
      "WARNING:country_converter.country_converter:Latin America & Caribbean not found in regex\n",
      "WARNING:country_converter.country_converter:Middle East & North Africa not found in regex\n",
      "WARNING:country_converter.country_converter:North America not found in regex\n",
      "WARNING:country_converter.country_converter:South Asia not found in regex\n",
      "WARNING:country_converter.country_converter:Sub-Saharan Africa not found in regex\n",
      "WARNING:country_converter.country_converter:Br. Antr. Terr not found in regex\n",
      "WARNING:country_converter.country_converter:Bonaire not found in regex\n",
      "WARNING:country_converter.country_converter:Bunkers not found in regex\n",
      "WARNING:country_converter.country_converter:Free Zones not found in regex\n",
      "WARNING:country_converter.country_converter:Other Asia, nes not found in regex\n",
      "WARNING:country_converter.country_converter:Occ.Pal.Terr not found in regex\n",
      "WARNING:country_converter.country_converter:Serbia, FR(Serbia/Montenegro) not found in regex\n",
      "WARNING:country_converter.country_converter:Special Categories not found in regex\n",
      "WARNING:country_converter.country_converter:Unspecified not found in regex\n",
      "WARNING:country_converter.country_converter: World not found in regex\n",
      "WARNING:country_converter.country_converter:East Asia & Pacific not found in regex\n",
      "WARNING:country_converter.country_converter:Europe & Central Asia not found in regex\n",
      "WARNING:country_converter.country_converter:Latin America & Caribbean not found in regex\n",
      "WARNING:country_converter.country_converter:Middle East & North Africa not found in regex\n",
      "WARNING:country_converter.country_converter:North America not found in regex\n",
      "WARNING:country_converter.country_converter:South Asia not found in regex\n",
      "WARNING:country_converter.country_converter:Sub-Saharan Africa not found in regex\n",
      "WARNING:country_converter.country_converter:Netherlands Antilles not found in regex\n",
      "WARNING:country_converter.country_converter:Bonaire not found in regex\n",
      "WARNING:country_converter.country_converter:Bunkers not found in regex\n",
      "WARNING:country_converter.country_converter:Free Zones not found in regex\n",
      "WARNING:country_converter.country_converter:Other Asia, nes not found in regex\n",
      "WARNING:country_converter.country_converter:Occ.Pal.Terr not found in regex\n",
      "WARNING:country_converter.country_converter:Serbia, FR(Serbia/Montenegro) not found in regex\n",
      "WARNING:country_converter.country_converter:Special Categories not found in regex\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:country_converter.country_converter:Unspecified not found in regex\n",
      "WARNING:country_converter.country_converter: World not found in regex\n",
      "WARNING:country_converter.country_converter:East Asia & Pacific not found in regex\n",
      "WARNING:country_converter.country_converter:Europe & Central Asia not found in regex\n",
      "WARNING:country_converter.country_converter:Latin America & Caribbean not found in regex\n",
      "WARNING:country_converter.country_converter:Middle East & North Africa not found in regex\n",
      "WARNING:country_converter.country_converter:North America not found in regex\n",
      "WARNING:country_converter.country_converter:South Asia not found in regex\n",
      "WARNING:country_converter.country_converter:Sub-Saharan Africa not found in regex\n"
     ]
    }
   ],
   "source": [
    "#Function calls\n",
    "\n",
    "ft_2012_new = func_ft(ft_2012)\n",
    "ft_2013_new = func_ft(ft_2013)\n",
    "ft_2014_new = func_ft(ft_2014)\n",
    "ft_2015_new = func_ft(ft_2015)\n",
    "ft_2016_new = func_ft(ft_2016)\n",
    "ft_2017_new = func_ft(ft_2017)\n",
    "ft_2018_new = func_ft(ft_2018)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "91e1bb63",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Country            object\n",
       "Year                int32\n",
       "Net_Food_Trade    float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking the types for one of the dataframes\n",
    "ft_2018_new.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "545645d9",
   "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>Year</th>\n",
       "      <th>Net_Food_Trade</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Aruba</td>\n",
       "      <td>2018</td>\n",
       "      <td>188596.210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2018</td>\n",
       "      <td>1258157.070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Angola</td>\n",
       "      <td>2018</td>\n",
       "      <td>1053077.560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Anguilla</td>\n",
       "      <td>2018</td>\n",
       "      <td>3175.220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Albania</td>\n",
       "      <td>2018</td>\n",
       "      <td>626629.980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>239</th>\n",
       "      <td>not found</td>\n",
       "      <td>2018</td>\n",
       "      <td>-30047116.330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>240</th>\n",
       "      <td>not found</td>\n",
       "      <td>2018</td>\n",
       "      <td>40636740.670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>241</th>\n",
       "      <td>not found</td>\n",
       "      <td>2018</td>\n",
       "      <td>51479830.800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242</th>\n",
       "      <td>not found</td>\n",
       "      <td>2018</td>\n",
       "      <td>492436.220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>243</th>\n",
       "      <td>not found</td>\n",
       "      <td>2018</td>\n",
       "      <td>3225788.820</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>244 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Country  Year  Net_Food_Trade\n",
       "0          Aruba  2018      188596.210\n",
       "1    Afghanistan  2018     1258157.070\n",
       "2         Angola  2018     1053077.560\n",
       "3       Anguilla  2018        3175.220\n",
       "4        Albania  2018      626629.980\n",
       "..           ...   ...             ...\n",
       "239    not found  2018   -30047116.330\n",
       "240    not found  2018    40636740.670\n",
       "241    not found  2018    51479830.800\n",
       "242    not found  2018      492436.220\n",
       "243    not found  2018     3225788.820\n",
       "\n",
       "[244 rows x 3 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(ft_2018_new)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "cdc1b6c2",
   "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",
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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</th>\n",
       "      <th>Year</th>\n",
       "      <th>Net_Food_Trade</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Aruba</td>\n",
       "      <td>2012</td>\n",
       "      <td>241879.760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2012</td>\n",
       "      <td>600792.680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Angola</td>\n",
       "      <td>2012</td>\n",
       "      <td>2549709.980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Anguilla</td>\n",
       "      <td>2012</td>\n",
       "      <td>3451.230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Albania</td>\n",
       "      <td>2012</td>\n",
       "      <td>553119.160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>239</th>\n",
       "      <td>not found</td>\n",
       "      <td>2018</td>\n",
       "      <td>-30047116.330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>240</th>\n",
       "      <td>not found</td>\n",
       "      <td>2018</td>\n",
       "      <td>40636740.670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>241</th>\n",
       "      <td>not found</td>\n",
       "      <td>2018</td>\n",
       "      <td>51479830.800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242</th>\n",
       "      <td>not found</td>\n",
       "      <td>2018</td>\n",
       "      <td>492436.220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>243</th>\n",
       "      <td>not found</td>\n",
       "      <td>2018</td>\n",
       "      <td>3225788.820</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1700 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Country  Year  Net_Food_Trade\n",
       "0          Aruba  2012      241879.760\n",
       "1    Afghanistan  2012      600792.680\n",
       "2         Angola  2012     2549709.980\n",
       "3       Anguilla  2012        3451.230\n",
       "4        Albania  2012      553119.160\n",
       "..           ...   ...             ...\n",
       "239    not found  2018   -30047116.330\n",
       "240    not found  2018    40636740.670\n",
       "241    not found  2018    51479830.800\n",
       "242    not found  2018      492436.220\n",
       "243    not found  2018     3225788.820\n",
       "\n",
       "[1700 rows x 3 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#We have to concat the dataframes together\n",
    "ft_concat = pd.concat([ft_2012_new, ft_2013_new, ft_2014_new, ft_2015_new, ft_2016_new, ft_2017_new, ft_2018_new],sort=False)\n",
    "\n",
    "display(ft_concat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "8cad8e8d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Merging this with our previously merged dataframe\n",
    "df_final = merge4.merge(ft_concat, how=\"left\", on=[\"Country\", \"Year\"])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "bd76aa3b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Year</th>\n",
       "      <th>GFSI_Index</th>\n",
       "      <th>Arable_Land</th>\n",
       "      <th>Cereal_Yield</th>\n",
       "      <th>SalariedEmp</th>\n",
       "      <th>LPI_Score</th>\n",
       "      <th>Poverty_Share</th>\n",
       "      <th>CCPI</th>\n",
       "      <th>Net_Food_Trade</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>2012</td>\n",
       "      <td>53.2</td>\n",
       "      <td>0.201</td>\n",
       "      <td>1677.600</td>\n",
       "      <td>68.030</td>\n",
       "      <td>2.410</td>\n",
       "      <td>0.416</td>\n",
       "      <td>1</td>\n",
       "      <td>3415586.700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Angola</td>\n",
       "      <td>2012</td>\n",
       "      <td>40.1</td>\n",
       "      <td>0.187</td>\n",
       "      <td>552.000</td>\n",
       "      <td>27.890</td>\n",
       "      <td>2.280</td>\n",
       "      <td>36.781</td>\n",
       "      <td>0</td>\n",
       "      <td>2549709.980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>2012</td>\n",
       "      <td>61.0</td>\n",
       "      <td>0.953</td>\n",
       "      <td>4146.200</td>\n",
       "      <td>77.010</td>\n",
       "      <td>3.050</td>\n",
       "      <td>1.156</td>\n",
       "      <td>1</td>\n",
       "      <td>-14955506.580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Australia</td>\n",
       "      <td>2012</td>\n",
       "      <td>70.9</td>\n",
       "      <td>1.392</td>\n",
       "      <td>2231.600</td>\n",
       "      <td>82.830</td>\n",
       "      <td>3.730</td>\n",
       "      <td>0.424</td>\n",
       "      <td>1</td>\n",
       "      <td>2668858.520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Austria</td>\n",
       "      <td>2012</td>\n",
       "      <td>79.6</td>\n",
       "      <td>0.161</td>\n",
       "      <td>6002.900</td>\n",
       "      <td>86.930</td>\n",
       "      <td>3.890</td>\n",
       "      <td>0.617</td>\n",
       "      <td>1</td>\n",
       "      <td>-961422.040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>786</th>\n",
       "      <td>Uzbekistan</td>\n",
       "      <td>2018</td>\n",
       "      <td>55.0</td>\n",
       "      <td>0.123</td>\n",
       "      <td>4055.900</td>\n",
       "      <td>64.810</td>\n",
       "      <td>2.580</td>\n",
       "      <td>11.730</td>\n",
       "      <td>0</td>\n",
       "      <td>520246.860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>787</th>\n",
       "      <td>Venezuela</td>\n",
       "      <td>2018</td>\n",
       "      <td>45.8</td>\n",
       "      <td>0.090</td>\n",
       "      <td>3395.000</td>\n",
       "      <td>58.820</td>\n",
       "      <td>2.230</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>577658.250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>788</th>\n",
       "      <td>Vietnam</td>\n",
       "      <td>2018</td>\n",
       "      <td>62.9</td>\n",
       "      <td>0.073</td>\n",
       "      <td>5685.400</td>\n",
       "      <td>43.940</td>\n",
       "      <td>3.270</td>\n",
       "      <td>1.801</td>\n",
       "      <td>0</td>\n",
       "      <td>5246528.870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>789</th>\n",
       "      <td>Yemen</td>\n",
       "      <td>2018</td>\n",
       "      <td>34.7</td>\n",
       "      <td>0.039</td>\n",
       "      <td>759.200</td>\n",
       "      <td>45.300</td>\n",
       "      <td>2.270</td>\n",
       "      <td>54.537</td>\n",
       "      <td>0</td>\n",
       "      <td>1071576.390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>790</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>2018</td>\n",
       "      <td>41.9</td>\n",
       "      <td>0.219</td>\n",
       "      <td>2157.800</td>\n",
       "      <td>24.860</td>\n",
       "      <td>2.530</td>\n",
       "      <td>58.196</td>\n",
       "      <td>0</td>\n",
       "      <td>15306.140</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>791 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Country  Year GFSI_Index  Arable_Land  Cereal_Yield  SalariedEmp  \\\n",
       "0       Algeria  2012       53.2        0.201      1677.600       68.030   \n",
       "1        Angola  2012       40.1        0.187       552.000       27.890   \n",
       "2     Argentina  2012       61.0        0.953      4146.200       77.010   \n",
       "3     Australia  2012       70.9        1.392      2231.600       82.830   \n",
       "4       Austria  2012       79.6        0.161      6002.900       86.930   \n",
       "..          ...   ...        ...          ...           ...          ...   \n",
       "786  Uzbekistan  2018       55.0        0.123      4055.900       64.810   \n",
       "787   Venezuela  2018       45.8        0.090      3395.000       58.820   \n",
       "788     Vietnam  2018       62.9        0.073      5685.400       43.940   \n",
       "789       Yemen  2018       34.7        0.039       759.200       45.300   \n",
       "790      Zambia  2018       41.9        0.219      2157.800       24.860   \n",
       "\n",
       "     LPI_Score  Poverty_Share  CCPI  Net_Food_Trade  \n",
       "0        2.410          0.416     1     3415586.700  \n",
       "1        2.280         36.781     0     2549709.980  \n",
       "2        3.050          1.156     1   -14955506.580  \n",
       "3        3.730          0.424     1     2668858.520  \n",
       "4        3.890          0.617     1     -961422.040  \n",
       "..         ...            ...   ...             ...  \n",
       "786      2.580         11.730     0      520246.860  \n",
       "787      2.230            NaN     0      577658.250  \n",
       "788      3.270          1.801     0     5246528.870  \n",
       "789      2.270         54.537     0     1071576.390  \n",
       "790      2.530         58.196     0       15306.140  \n",
       "\n",
       "[791 rows x 10 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Check the result\n",
    "display(df_final)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "17bfe4a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Country            object\n",
       "Year                int32\n",
       "GFSI_Index         object\n",
       "Arable_Land       float64\n",
       "Cereal_Yield      float64\n",
       "SalariedEmp       float64\n",
       "LPI_Score         float64\n",
       "Poverty_Share     float64\n",
       "CCPI                int64\n",
       "Net_Food_Trade    float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "3a8f41a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "#converting GFSI Index to float type from object \n",
    "df_final[\"GFSI_Index\"] = df_final[\"GFSI_Index\"].astype(str).astype(float)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fffa1638",
   "metadata": {},
   "source": [
    "We had included LPI score for every odd year as well, but now we are going to change it. The unit of analysis will be \n",
    "Country-level data for every two years. For this, the data will be joined into the following year groups: \n",
    "- 2018-2017\n",
    "- 2016-2015\n",
    "- 2014 - 2013\n",
    "\n",
    "This is based on how the LPI scores are reported. For all the variables, we will take the mean of the years in the year sub-group. Taking the mean of the LPI score will give us the same result as we had replicated it earlier. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "eab93335",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Drop the 2012 year column. \n",
    "df_final = df_final[df_final['Year']>=2013]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "a9acf1f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>Year</th>\n",
       "      <th>GFSI_Index</th>\n",
       "      <th>Arable_Land</th>\n",
       "      <th>Cereal_Yield</th>\n",
       "      <th>SalariedEmp</th>\n",
       "      <th>LPI_Score</th>\n",
       "      <th>Poverty_Share</th>\n",
       "      <th>CCPI</th>\n",
       "      <th>Net_Food_Trade</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>2013</td>\n",
       "      <td>51.300</td>\n",
       "      <td>0.197</td>\n",
       "      <td>1813.100</td>\n",
       "      <td>68.970</td>\n",
       "      <td>2.650</td>\n",
       "      <td>0.381</td>\n",
       "      <td>1</td>\n",
       "      <td>3826813.540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>Angola</td>\n",
       "      <td>2013</td>\n",
       "      <td>40.900</td>\n",
       "      <td>0.188</td>\n",
       "      <td>814.400</td>\n",
       "      <td>26.990</td>\n",
       "      <td>2.540</td>\n",
       "      <td>37.187</td>\n",
       "      <td>0</td>\n",
       "      <td>2834125.800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>2013</td>\n",
       "      <td>61.200</td>\n",
       "      <td>0.941</td>\n",
       "      <td>4796.800</td>\n",
       "      <td>76.450</td>\n",
       "      <td>2.990</td>\n",
       "      <td>1.016</td>\n",
       "      <td>1</td>\n",
       "      <td>-15161248.320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>Australia</td>\n",
       "      <td>2013</td>\n",
       "      <td>74.400</td>\n",
       "      <td>1.350</td>\n",
       "      <td>1936.100</td>\n",
       "      <td>83.160</td>\n",
       "      <td>3.810</td>\n",
       "      <td>0.461</td>\n",
       "      <td>1</td>\n",
       "      <td>4205722.880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>Austria</td>\n",
       "      <td>2013</td>\n",
       "      <td>79.000</td>\n",
       "      <td>0.160</td>\n",
       "      <td>5846.800</td>\n",
       "      <td>86.800</td>\n",
       "      <td>3.650</td>\n",
       "      <td>0.338</td>\n",
       "      <td>1</td>\n",
       "      <td>-998156.580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>786</th>\n",
       "      <td>Uzbekistan</td>\n",
       "      <td>2018</td>\n",
       "      <td>55.000</td>\n",
       "      <td>0.123</td>\n",
       "      <td>4055.900</td>\n",
       "      <td>64.810</td>\n",
       "      <td>2.580</td>\n",
       "      <td>11.730</td>\n",
       "      <td>0</td>\n",
       "      <td>520246.860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>787</th>\n",
       "      <td>Venezuela</td>\n",
       "      <td>2018</td>\n",
       "      <td>45.800</td>\n",
       "      <td>0.090</td>\n",
       "      <td>3395.000</td>\n",
       "      <td>58.820</td>\n",
       "      <td>2.230</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>577658.250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>788</th>\n",
       "      <td>Vietnam</td>\n",
       "      <td>2018</td>\n",
       "      <td>62.900</td>\n",
       "      <td>0.073</td>\n",
       "      <td>5685.400</td>\n",
       "      <td>43.940</td>\n",
       "      <td>3.270</td>\n",
       "      <td>1.801</td>\n",
       "      <td>0</td>\n",
       "      <td>5246528.870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>789</th>\n",
       "      <td>Yemen</td>\n",
       "      <td>2018</td>\n",
       "      <td>34.700</td>\n",
       "      <td>0.039</td>\n",
       "      <td>759.200</td>\n",
       "      <td>45.300</td>\n",
       "      <td>2.270</td>\n",
       "      <td>54.537</td>\n",
       "      <td>0</td>\n",
       "      <td>1071576.390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>790</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>2018</td>\n",
       "      <td>41.900</td>\n",
       "      <td>0.219</td>\n",
       "      <td>2157.800</td>\n",
       "      <td>24.860</td>\n",
       "      <td>2.530</td>\n",
       "      <td>58.196</td>\n",
       "      <td>0</td>\n",
       "      <td>15306.140</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>678 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Country  Year  GFSI_Index  Arable_Land  Cereal_Yield  SalariedEmp  \\\n",
       "113     Algeria  2013      51.300        0.197      1813.100       68.970   \n",
       "114      Angola  2013      40.900        0.188       814.400       26.990   \n",
       "115   Argentina  2013      61.200        0.941      4796.800       76.450   \n",
       "116   Australia  2013      74.400        1.350      1936.100       83.160   \n",
       "117     Austria  2013      79.000        0.160      5846.800       86.800   \n",
       "..          ...   ...         ...          ...           ...          ...   \n",
       "786  Uzbekistan  2018      55.000        0.123      4055.900       64.810   \n",
       "787   Venezuela  2018      45.800        0.090      3395.000       58.820   \n",
       "788     Vietnam  2018      62.900        0.073      5685.400       43.940   \n",
       "789       Yemen  2018      34.700        0.039       759.200       45.300   \n",
       "790      Zambia  2018      41.900        0.219      2157.800       24.860   \n",
       "\n",
       "     LPI_Score  Poverty_Share  CCPI  Net_Food_Trade  \n",
       "113      2.650          0.381     1     3826813.540  \n",
       "114      2.540         37.187     0     2834125.800  \n",
       "115      2.990          1.016     1   -15161248.320  \n",
       "116      3.810          0.461     1     4205722.880  \n",
       "117      3.650          0.338     1     -998156.580  \n",
       "..         ...            ...   ...             ...  \n",
       "786      2.580         11.730     0      520246.860  \n",
       "787      2.230            NaN     0      577658.250  \n",
       "788      3.270          1.801     0     5246528.870  \n",
       "789      2.270         54.537     0     1071576.390  \n",
       "790      2.530         58.196     0       15306.140  \n",
       "\n",
       "[678 rows x 10 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(df_final)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "dcf0d395",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create subsets of the year-groups \n",
    "s1 = df_final[df_final['Year']<2015]\n",
    "s2 = df_final[df_final['Year']>2014]\n",
    "s2 = s2[s2['Year']<2017]\n",
    "s3 = df_final[df_final['Year']>2016]\n",
    "s3 = s3[s3['Year']<2019]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "97072aab",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Year</th>\n",
       "      <th>GFSI_Index</th>\n",
       "      <th>Arable_Land</th>\n",
       "      <th>Cereal_Yield</th>\n",
       "      <th>SalariedEmp</th>\n",
       "      <th>LPI_Score</th>\n",
       "      <th>Poverty_Share</th>\n",
       "      <th>CCPI</th>\n",
       "      <th>Net_Food_Trade</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>565</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>2017</td>\n",
       "      <td>63.500</td>\n",
       "      <td>0.181</td>\n",
       "      <td>990.300</td>\n",
       "      <td>68.270</td>\n",
       "      <td>2.450</td>\n",
       "      <td>0.329</td>\n",
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       "      <td>2852933.160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>566</th>\n",
       "      <td>Angola</td>\n",
       "      <td>2017</td>\n",
       "      <td>38.100</td>\n",
       "      <td>0.164</td>\n",
       "      <td>754.700</td>\n",
       "      <td>22.600</td>\n",
       "      <td>2.050</td>\n",
       "      <td>45.069</td>\n",
       "      <td>0</td>\n",
       "      <td>1230907.530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>567</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>2017</td>\n",
       "      <td>65.600</td>\n",
       "      <td>0.890</td>\n",
       "      <td>5404.900</td>\n",
       "      <td>74.660</td>\n",
       "      <td>2.890</td>\n",
       "      <td>0.928</td>\n",
       "      <td>1</td>\n",
       "      <td>-12911456.170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>568</th>\n",
       "      <td>Australia</td>\n",
       "      <td>2017</td>\n",
       "      <td>76.100</td>\n",
       "      <td>1.250</td>\n",
       "      <td>2674.300</td>\n",
       "      <td>83.100</td>\n",
       "      <td>3.750</td>\n",
       "      <td>0.498</td>\n",
       "      <td>1</td>\n",
       "      <td>3974008.060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>569</th>\n",
       "      <td>Austria</td>\n",
       "      <td>2017</td>\n",
       "      <td>79.200</td>\n",
       "      <td>0.151</td>\n",
       "      <td>6260.600</td>\n",
       "      <td>87.620</td>\n",
       "      <td>4.030</td>\n",
       "      <td>0.318</td>\n",
       "      <td>1</td>\n",
       "      <td>-1964219.510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>786</th>\n",
       "      <td>Uzbekistan</td>\n",
       "      <td>2018</td>\n",
       "      <td>55.000</td>\n",
       "      <td>0.123</td>\n",
       "      <td>4055.900</td>\n",
       "      <td>64.810</td>\n",
       "      <td>2.580</td>\n",
       "      <td>11.730</td>\n",
       "      <td>0</td>\n",
       "      <td>520246.860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>787</th>\n",
       "      <td>Venezuela</td>\n",
       "      <td>2018</td>\n",
       "      <td>45.800</td>\n",
       "      <td>0.090</td>\n",
       "      <td>3395.000</td>\n",
       "      <td>58.820</td>\n",
       "      <td>2.230</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>577658.250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>788</th>\n",
       "      <td>Vietnam</td>\n",
       "      <td>2018</td>\n",
       "      <td>62.900</td>\n",
       "      <td>0.073</td>\n",
       "      <td>5685.400</td>\n",
       "      <td>43.940</td>\n",
       "      <td>3.270</td>\n",
       "      <td>1.801</td>\n",
       "      <td>0</td>\n",
       "      <td>5246528.870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>789</th>\n",
       "      <td>Yemen</td>\n",
       "      <td>2018</td>\n",
       "      <td>34.700</td>\n",
       "      <td>0.039</td>\n",
       "      <td>759.200</td>\n",
       "      <td>45.300</td>\n",
       "      <td>2.270</td>\n",
       "      <td>54.537</td>\n",
       "      <td>0</td>\n",
       "      <td>1071576.390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>790</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>2018</td>\n",
       "      <td>41.900</td>\n",
       "      <td>0.219</td>\n",
       "      <td>2157.800</td>\n",
       "      <td>24.860</td>\n",
       "      <td>2.530</td>\n",
       "      <td>58.196</td>\n",
       "      <td>0</td>\n",
       "      <td>15306.140</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>226 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Country  Year  GFSI_Index  Arable_Land  Cereal_Yield  SalariedEmp  \\\n",
       "565     Algeria  2017      63.500        0.181       990.300       68.270   \n",
       "566      Angola  2017      38.100        0.164       754.700       22.600   \n",
       "567   Argentina  2017      65.600        0.890      5404.900       74.660   \n",
       "568   Australia  2017      76.100        1.250      2674.300       83.100   \n",
       "569     Austria  2017      79.200        0.151      6260.600       87.620   \n",
       "..          ...   ...         ...          ...           ...          ...   \n",
       "786  Uzbekistan  2018      55.000        0.123      4055.900       64.810   \n",
       "787   Venezuela  2018      45.800        0.090      3395.000       58.820   \n",
       "788     Vietnam  2018      62.900        0.073      5685.400       43.940   \n",
       "789       Yemen  2018      34.700        0.039       759.200       45.300   \n",
       "790      Zambia  2018      41.900        0.219      2157.800       24.860   \n",
       "\n",
       "     LPI_Score  Poverty_Share  CCPI  Net_Food_Trade  \n",
       "565      2.450          0.329     1     2852933.160  \n",
       "566      2.050         45.069     0     1230907.530  \n",
       "567      2.890          0.928     1   -12911456.170  \n",
       "568      3.750          0.498     1     3974008.060  \n",
       "569      4.030          0.318     1    -1964219.510  \n",
       "..         ...            ...   ...             ...  \n",
       "786      2.580         11.730     0      520246.860  \n",
       "787      2.230            NaN     0      577658.250  \n",
       "788      3.270          1.801     0     5246528.870  \n",
       "789      2.270         54.537     0     1071576.390  \n",
       "790      2.530         58.196     0       15306.140  \n",
       "\n",
       "[226 rows x 10 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Checking the results\n",
    "display(s3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "dd705b89",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Obtaining the mean for each column for each subset\n",
    "t1 = s1.groupby(\"Country\").mean()\n",
    "t2 = s2.groupby(\"Country\").mean()\n",
    "t3 = s3.groupby(\"Country\").mean()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "4b5ca6a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Change year value for each subset\n",
    "t1[\"Year\"] = 2014\n",
    "t2[\"Year\"] = 2016\n",
    "t3[\"Year\"] = 2018\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "5067d8a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Reset the index for each subset\n",
    "t1.reset_index(inplace=True)\n",
    "t2.reset_index(inplace=True)\n",
    "t3.reset_index(inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "e601975d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Now we are going to concat each subset to create our final complete dataset\n",
    "df_complete = pd.concat([t1, t2, t3],sort=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "817fb026",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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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>Year</th>\n",
       "      <th>GFSI_Index</th>\n",
       "      <th>Arable_Land</th>\n",
       "      <th>Cereal_Yield</th>\n",
       "      <th>SalariedEmp</th>\n",
       "      <th>LPI_Score</th>\n",
       "      <th>Poverty_Share</th>\n",
       "      <th>CCPI</th>\n",
       "      <th>Net_Food_Trade</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>2014</td>\n",
       "      <td>54.350</td>\n",
       "      <td>0.194</td>\n",
       "      <td>1591.150</td>\n",
       "      <td>68.890</td>\n",
       "      <td>2.650</td>\n",
       "      <td>0.368</td>\n",
       "      <td>1</td>\n",
       "      <td>3830183.250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Angola</td>\n",
       "      <td>2014</td>\n",
       "      <td>39.700</td>\n",
       "      <td>0.185</td>\n",
       "      <td>851.300</td>\n",
       "      <td>26.255</td>\n",
       "      <td>2.540</td>\n",
       "      <td>37.474</td>\n",
       "      <td>0</td>\n",
       "      <td>2832262.290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>2014</td>\n",
       "      <td>61.350</td>\n",
       "      <td>0.930</td>\n",
       "      <td>4816.800</td>\n",
       "      <td>76.400</td>\n",
       "      <td>2.990</td>\n",
       "      <td>0.972</td>\n",
       "      <td>1</td>\n",
       "      <td>-15318650.680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Australia</td>\n",
       "      <td>2014</td>\n",
       "      <td>75.450</td>\n",
       "      <td>1.356</td>\n",
       "      <td>2037.050</td>\n",
       "      <td>83.010</td>\n",
       "      <td>3.810</td>\n",
       "      <td>0.480</td>\n",
       "      <td>1</td>\n",
       "      <td>4584525.535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Austria</td>\n",
       "      <td>2014</td>\n",
       "      <td>79.000</td>\n",
       "      <td>0.159</td>\n",
       "      <td>6446.750</td>\n",
       "      <td>86.755</td>\n",
       "      <td>3.650</td>\n",
       "      <td>0.281</td>\n",
       "      <td>1</td>\n",
       "      <td>-962040.265</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Country  Year  GFSI_Index  Arable_Land  Cereal_Yield  SalariedEmp  \\\n",
       "0    Algeria  2014      54.350        0.194      1591.150       68.890   \n",
       "1     Angola  2014      39.700        0.185       851.300       26.255   \n",
       "2  Argentina  2014      61.350        0.930      4816.800       76.400   \n",
       "3  Australia  2014      75.450        1.356      2037.050       83.010   \n",
       "4    Austria  2014      79.000        0.159      6446.750       86.755   \n",
       "\n",
       "   LPI_Score  Poverty_Share  CCPI  Net_Food_Trade  \n",
       "0      2.650          0.368     1     3830183.250  \n",
       "1      2.540         37.474     0     2832262.290  \n",
       "2      2.990          0.972     1   -15318650.680  \n",
       "3      3.810          0.480     1     4584525.535  \n",
       "4      3.650          0.281     1     -962040.265  "
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check the result\n",
    "df_complete.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "5181be84",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Country            0\n",
       "Year               0\n",
       "GFSI_Index         0\n",
       "Arable_Land        0\n",
       "Cereal_Yield       6\n",
       "SalariedEmp        0\n",
       "LPI_Score         13\n",
       "Poverty_Share     26\n",
       "CCPI               0\n",
       "Net_Food_Trade     3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking for missing values\n",
    "df_complete.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "14fc8185",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Drop the missing values from the dataframe\n",
    "df_complete=df_complete.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "e63bfc06",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Country            object\n",
       "Year                int64\n",
       "GFSI_Index        float64\n",
       "Arable_Land       float64\n",
       "Cereal_Yield      float64\n",
       "SalariedEmp       float64\n",
       "LPI_Score         float64\n",
       "Poverty_Share     float64\n",
       "CCPI                int64\n",
       "Net_Food_Trade    float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking types\n",
    "df_complete.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "c8d562c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "#We are going to change year to dummies\n",
    "\n",
    "#First we convert year to category type\n",
    "df_complete[\"Year\"] = df_complete[\"Year\"].astype('category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "136ed696",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create the dummies\n",
    "year_dummies = pd.get_dummies(df_complete.Year)\n",
    "\n",
    "#Drop 2014 as the baseline\n",
    "year_dummies = year_dummies.drop([2014],axis=1) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "afc2c541",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Now  these changes will be reflected in the main dataframe\n",
    "df_complete = pd.concat([df_complete.drop(['Year'],axis=1),year_dummies],axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3eefb953",
   "metadata": {},
   "source": [
    "#### This is the final dataframe of the project, on which machine learning will be used. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "3abb088f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>GFSI_Index</th>\n",
       "      <th>Arable_Land</th>\n",
       "      <th>Cereal_Yield</th>\n",
       "      <th>SalariedEmp</th>\n",
       "      <th>LPI_Score</th>\n",
       "      <th>Poverty_Share</th>\n",
       "      <th>CCPI</th>\n",
       "      <th>Net_Food_Trade</th>\n",
       "      <th>2016</th>\n",
       "      <th>2018</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>54.350</td>\n",
       "      <td>0.194</td>\n",
       "      <td>1591.150</td>\n",
       "      <td>68.890</td>\n",
       "      <td>2.650</td>\n",
       "      <td>0.368</td>\n",
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       "      <td>3830183.250</td>\n",
       "      <td>0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Angola</td>\n",
       "      <td>39.700</td>\n",
       "      <td>0.185</td>\n",
       "      <td>851.300</td>\n",
       "      <td>26.255</td>\n",
       "      <td>2.540</td>\n",
       "      <td>37.474</td>\n",
       "      <td>0</td>\n",
       "      <td>2832262.290</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>61.350</td>\n",
       "      <td>0.930</td>\n",
       "      <td>4816.800</td>\n",
       "      <td>76.400</td>\n",
       "      <td>2.990</td>\n",
       "      <td>0.972</td>\n",
       "      <td>1</td>\n",
       "      <td>-15318650.680</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Australia</td>\n",
       "      <td>75.450</td>\n",
       "      <td>1.356</td>\n",
       "      <td>2037.050</td>\n",
       "      <td>83.010</td>\n",
       "      <td>3.810</td>\n",
       "      <td>0.480</td>\n",
       "      <td>1</td>\n",
       "      <td>4584525.535</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Austria</td>\n",
       "      <td>79.000</td>\n",
       "      <td>0.159</td>\n",
       "      <td>6446.750</td>\n",
       "      <td>86.755</td>\n",
       "      <td>3.650</td>\n",
       "      <td>0.281</td>\n",
       "      <td>1</td>\n",
       "      <td>-962040.265</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>Uruguay</td>\n",
       "      <td>70.800</td>\n",
       "      <td>0.578</td>\n",
       "      <td>4812.550</td>\n",
       "      <td>71.890</td>\n",
       "      <td>2.690</td>\n",
       "      <td>0.055</td>\n",
       "      <td>0</td>\n",
       "      <td>364256.245</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>Uzbekistan</td>\n",
       "      <td>57.600</td>\n",
       "      <td>0.125</td>\n",
       "      <td>4150.700</td>\n",
       "      <td>64.300</td>\n",
       "      <td>2.580</td>\n",
       "      <td>12.213</td>\n",
       "      <td>0</td>\n",
       "      <td>394478.715</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>Vietnam</td>\n",
       "      <td>61.250</td>\n",
       "      <td>0.074</td>\n",
       "      <td>5559.900</td>\n",
       "      <td>43.395</td>\n",
       "      <td>3.270</td>\n",
       "      <td>1.799</td>\n",
       "      <td>0</td>\n",
       "      <td>4893159.095</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>Yemen</td>\n",
       "      <td>35.350</td>\n",
       "      <td>0.039</td>\n",
       "      <td>729.100</td>\n",
       "      <td>45.675</td>\n",
       "      <td>2.270</td>\n",
       "      <td>53.982</td>\n",
       "      <td>0</td>\n",
       "      <td>1102238.555</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>39.950</td>\n",
       "      <td>0.222</td>\n",
       "      <td>2318.600</td>\n",
       "      <td>25.035</td>\n",
       "      <td>2.530</td>\n",
       "      <td>58.298</td>\n",
       "      <td>0</td>\n",
       "      <td>-30698.645</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>297 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Country  GFSI_Index  Arable_Land  Cereal_Yield  SalariedEmp  \\\n",
       "0       Algeria      54.350        0.194      1591.150       68.890   \n",
       "1        Angola      39.700        0.185       851.300       26.255   \n",
       "2     Argentina      61.350        0.930      4816.800       76.400   \n",
       "3     Australia      75.450        1.356      2037.050       83.010   \n",
       "4       Austria      79.000        0.159      6446.750       86.755   \n",
       "..          ...         ...          ...           ...          ...   \n",
       "107     Uruguay      70.800        0.578      4812.550       71.890   \n",
       "108  Uzbekistan      57.600        0.125      4150.700       64.300   \n",
       "110     Vietnam      61.250        0.074      5559.900       43.395   \n",
       "111       Yemen      35.350        0.039       729.100       45.675   \n",
       "112      Zambia      39.950        0.222      2318.600       25.035   \n",
       "\n",
       "     LPI_Score  Poverty_Share  CCPI  Net_Food_Trade  2016  2018  \n",
       "0        2.650          0.368     1     3830183.250     0     0  \n",
       "1        2.540         37.474     0     2832262.290     0     0  \n",
       "2        2.990          0.972     1   -15318650.680     0     0  \n",
       "3        3.810          0.480     1     4584525.535     0     0  \n",
       "4        3.650          0.281     1     -962040.265     0     0  \n",
       "..         ...            ...   ...             ...   ...   ...  \n",
       "107      2.690          0.055     0      364256.245     0     1  \n",
       "108      2.580         12.213     0      394478.715     0     1  \n",
       "110      3.270          1.799     0     4893159.095     0     1  \n",
       "111      2.270         53.982     0     1102238.555     0     1  \n",
       "112      2.530         58.298     0      -30698.645     0     1  \n",
       "\n",
       "[297 rows x 11 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(df_complete)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "id": "54db8ab1",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Convert to csv\n",
    "df_complete.to_csv(r'C:\\Users\\sonal\\DataScience1-Project-\\food_security.csv',index=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "2732bf49",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Arable_Land       float64\n",
       "Cereal_Yield      float64\n",
       "SalariedEmp       float64\n",
       "LPI_Score         float64\n",
       "Poverty_Share     float64\n",
       "CCPI                int64\n",
       "Net_Food_Trade    float64\n",
       "2016                uint8\n",
       "2018                uint8\n",
       "dtype: object"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#New Dataframes X, y and y1 to store specific columns of the dataframe\n",
    "y = df_complete[['GFSI_Index']]\n",
    "y1 = df_complete[['GFSI_Index', 'Country']]\n",
    "X = df_complete.drop(columns=['GFSI_Index', 'Country'])\n",
    "\n",
    "X.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96bb67fb",
   "metadata": {},
   "source": [
    "### Split into training and testing, and exploring the training data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "a83acf8a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Split into training and testing\n",
    "train_X, test_X, train_y, test_y = train_test_split(X,y,test_size = .25,random_state=123)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "id": "fbda9605",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plot the continuous Variables \n",
    "temp1 = train_X.select_dtypes(include=[\"float64\"]).melt()\n",
    "\n",
    "v1 =(\n",
    "    ggplot(temp1,aes(x=\"value\")) +\n",
    "    geom_histogram(bins=30, color =\"black\", fill=\"lightblue\") +\n",
    "    facet_wrap(\"variable\",scales='free') +\n",
    "    theme_bw()+\n",
    "    ggtitle(\"Distributions of Continous Predictors\")+\n",
    "    theme(figure_size=(10,6),\n",
    "          subplots_adjust={'wspace':0.25, 'hspace':0.25})\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "id": "11602b47",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "print(v1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "id": "87363936",
   "metadata": {},
   "outputs": [],
   "source": [
    "ggsave(filename=\"Continous.png\",\n",
    "       plot=v1,\n",
    "       device='png',\n",
    "       dpi=100)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "787b43c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "D = train_X[['Arable_Land', 'Cereal_Yield', 'SalariedEmp', 'LPI_Score', 'Poverty_Share', 'Net_Food_Trade']]\n",
    "D['GFSI_Index'] = train_y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "4524c6e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Logging some variables\n",
    "D['ln_ArableLand'] =  np.log(D['Arable_Land'] + 1)\n",
    "D['ln_CerealYield'] =  np.log(D['Cereal_Yield'] + 1)\n",
    "D['ln_Poverty'] =  np.log(D['Poverty_Share'] + 1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "id": "fe7536c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Checking log effect on Arable Land\n",
    "l1 = (\n",
    "    ggplot(D,aes(x=\"ln_ArableLand\")) +\n",
    "    geom_histogram(bins=25, color=\"black\", fill=\"lightblue\") +\n",
    "    ggtitle(\"Ln Arable Land\")+\n",
    "    theme(figure_size=(5,3),\n",
    "          subplots_adjust={'wspace':0.25})\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "id": "427ae6d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "ggsave(filename=\"logarableland.png\",\n",
    "       plot=l1,\n",
    "       device='png',\n",
    "       dpi=300)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "id": "727a1c45",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Checking log effect on Cereal Yield\n",
    "l2 = (\n",
    "    ggplot(D,aes(x=\"ln_CerealYield\")) +\n",
    "    geom_histogram(bins=25, color=\"black\", fill=\"lightblue\") +\n",
    "    ggtitle(\"Ln Cereal Yield\")+\n",
    "    theme(figure_size=(5,3),\n",
    "          subplots_adjust={'wspace':0.25})\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "id": "7f21676f",
   "metadata": {},
   "outputs": [],
   "source": [
    "ggsave(filename=\"logcerealyeild.png\",\n",
    "       plot=l2,\n",
    "       device='png',\n",
    "       dpi=300)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "id": "d625cd69",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Checking log effect on Poverty \n",
    "l3 = (\n",
    "    ggplot(D,aes(x=\"ln_Poverty\")) +\n",
    "    geom_histogram(bins=25, color=\"black\", fill=\"lightblue\") +\n",
    "    ggtitle(\"Ln Poverty\")+\n",
    "    theme(figure_size=(5,3),\n",
    "          subplots_adjust={'wspace':0.25})\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "id": "ba19ab98",
   "metadata": {},
   "outputs": [],
   "source": [
    "ggsave(filename=\"logpoverty.png\",\n",
    "       plot=l3,\n",
    "       device='png',\n",
    "       dpi=300)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "161c082c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Convert the Index to numeric type to be able to plot it. \n",
    "D[\"GFSI_Index\"] = pd.to_numeric(D[\"GFSI_Index\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "9d473fff",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Drop the previous Arable Land, Cereal Yield, and Poverty Share from D \n",
    "D = D.drop(['Arable_Land', 'Cereal_Yield', 'Poverty_Share'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "id": "082576e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "v3 = (\n",
    "    ggplot(D.melt(id_vars=[\"GFSI_Index\"]),\n",
    "           aes(x=\"value\",y=\"GFSI_Index\"))+\n",
    "    geom_point(alpha=.5, color=\"red\", size=0.5) +\n",
    "    facet_wrap(\"variable\",scales=\"free\") +\n",
    "    theme_bw() +\n",
    "    ggtitle(\"Correlation of predictors with target\")+\n",
    "    theme(figure_size = (12,10), subplots_adjust={'wspace':0.25, 'hspace':0.25}) +\n",
    "    scale_y_continuous(limits=[30, 86])\n",
    "    \n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "id": "5e2302d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x1000 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "print(v3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "id": "b9702d8b",
   "metadata": {},
   "outputs": [],
   "source": [
    "ggsave(filename=\"correlation.png\",\n",
    "       plot=v3,\n",
    "       device='png',\n",
    "       dpi=80)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "995bc7c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Make these changes on the original dataframe and then split again \n",
    "y = df_complete[['GFSI_Index']]\n",
    "y1 = df_complete[['GFSI_Index', 'Country']]\n",
    "X = df_complete.drop(columns=['GFSI_Index', 'Country'])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "d69b5378",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Logging the variables\n",
    "X['ln_ArableLand'] =  np.log(X['Arable_Land'] + 1)\n",
    "X['ln_CerealYield'] =  np.log(X['Cereal_Yield'] + 1)\n",
    "X['ln_Poverty'] =  np.log(X['Poverty_Share'] + 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "1b3c9243",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Drop the previous Arable Land, Cereal Yield, and Poverty Share \n",
    "X = X.drop(['Arable_Land', 'Cereal_Yield', 'Poverty_Share'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d3621b7",
   "metadata": {},
   "source": [
    "### Re-splitting the data and Modelling Pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "ecd2cd9a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Now we are going to re-split the data into traning and testing since the changes have been made\n",
    "train_X, test_X, train_y, test_y = train_test_split(X,y,test_size = .25,random_state=123)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "0420a8e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set the folds index to ensure comparable samples\n",
    "fold_generator = KFold(n_splits=10, shuffle=True,random_state=1234)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "189e1057",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Initialize pipeline\n",
    "pipe = Pipeline(steps=[('pre_process', pp.MinMaxScaler()),('model',None)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "5c932b3a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Specify the models \n",
    "search_space = [\n",
    "        \n",
    "   # Linear Model\n",
    "    {'model' : [LM()]},\n",
    "    \n",
    "   #Support Vector Regression\n",
    "    {'model': [SVR(kernel ='rbf')]},\n",
    "    \n",
    "    #Decision Tree Regressor\n",
    "    {'model' :[DR(random_state=42)]},\n",
    "    \n",
    "    #Random Forest Regressor \n",
    "    {'model' :[RR(random_state=42)]}\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "93531b07",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Putting it all together in the grid search \n",
    "search = GridSearchCV(pipe, search_space, \n",
    "                      cv = fold_generator,\n",
    "                      scoring='neg_mean_squared_error',\n",
    "                      n_jobs=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "836b341b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=KFold(n_splits=10, random_state=1234, shuffle=True),\n",
       "             estimator=Pipeline(steps=[('pre_process', MinMaxScaler()),\n",
       "                                       ('model', None)]),\n",
       "             n_jobs=4,\n",
       "             param_grid=[{'model': [LinearRegression()]}, {'model': [SVR()]},\n",
       "                         {'model': [DecisionTreeRegressor(random_state=42)]},\n",
       "                         {'model': [RandomForestRegressor(random_state=42)]}],\n",
       "             scoring='neg_mean_squared_error')"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Fit the model to the training data\n",
    "search.fit(train_X,train_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "6075b3c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-16.821935035573123"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check the best score\n",
    "search.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "bfe02771",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'model': RandomForestRegressor(random_state=42)}"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check the best model\n",
    "search.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b67a9c8",
   "metadata": {},
   "source": [
    "### Test Performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "9b2a6723",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predict() method will use the best model out of the scan\n",
    "pred_y = search.predict(test_X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "afb1a142",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15.334517316666759"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check the mean squared error\n",
    "m.mean_squared_error(test_y,pred_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "db0dbc43",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pipeline(steps=[('pre_process', MinMaxScaler()),\n",
      "                ('model', RandomForestRegressor(random_state=42))])\n"
     ]
    }
   ],
   "source": [
    "#The best estimator\n",
    "mod = search.best_estimator_\n",
    "print(mod)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "74fca08e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9140338840740502"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m.r2_score(test_y,pred_y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7aa0ce8d",
   "metadata": {},
   "source": [
    "## Model Interpretation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b540ffdc",
   "metadata": {},
   "source": [
    "### Permutation Importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "c9fde76c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Permutation Importance\n",
    "vi = permutation_importance(mod,train_X,train_y,n_repeats=25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "id": "193d0133",
   "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>variable</th>\n",
       "      <th>vi</th>\n",
       "      <th>std</th>\n",
       "      <th>low</th>\n",
       "      <th>high</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ln_Poverty</td>\n",
       "      <td>0.878</td>\n",
       "      <td>0.061</td>\n",
       "      <td>0.757</td>\n",
       "      <td>0.999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LPI_Score</td>\n",
       "      <td>0.250</td>\n",
       "      <td>0.017</td>\n",
       "      <td>0.216</td>\n",
       "      <td>0.284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>SalariedEmp</td>\n",
       "      <td>0.064</td>\n",
       "      <td>0.004</td>\n",
       "      <td>0.055</td>\n",
       "      <td>0.073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ln_ArableLand</td>\n",
       "      <td>0.028</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.024</td>\n",
       "      <td>0.032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ln_CerealYield</td>\n",
       "      <td>0.023</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.019</td>\n",
       "      <td>0.028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Net_Food_Trade</td>\n",
       "      <td>0.009</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.007</td>\n",
       "      <td>0.010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2018</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>CCPI</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2016</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.001</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         variable    vi   std   low  high\n",
       "0      ln_Poverty 0.878 0.061 0.757 0.999\n",
       "1       LPI_Score 0.250 0.017 0.216 0.284\n",
       "2     SalariedEmp 0.064 0.004 0.055 0.073\n",
       "3   ln_ArableLand 0.028 0.002 0.024 0.032\n",
       "4  ln_CerealYield 0.023 0.002 0.019 0.028\n",
       "5  Net_Food_Trade 0.009 0.001 0.007 0.010\n",
       "6            2018 0.002 0.000 0.002 0.003\n",
       "7            CCPI 0.002 0.000 0.001 0.003\n",
       "8            2016 0.001 0.000 0.000 0.001"
      ]
     },
     "execution_count": 221,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Organize as a data frame \n",
    "vi_dat = pd.DataFrame(dict(variable=train_X.columns,\n",
    "                           vi = vi['importances_mean'],\n",
    "                           std = vi['importances_std']))\n",
    "\n",
    "# Generate intervals\n",
    "vi_dat['low'] = vi_dat['vi'] - 2*vi_dat['std']\n",
    "vi_dat['high'] = vi_dat['vi'] + 2*vi_dat['std']\n",
    "\n",
    "# But in order from most to least important\n",
    "vi_dat = vi_dat.sort_values(by=\"vi\",ascending=False).reset_index(drop=True)\n",
    "\n",
    "\n",
    "vi_dat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "id": "6e388ccb",
   "metadata": {},
   "outputs": [],
   "source": [
    "vi_dat = vi_dat.rename(columns={'vi': 'Variable-Importance', \"std\" : \"Standard-Deviation\"})  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "id": "88794adb",
   "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>variable</th>\n",
       "      <th>Variable-Importance</th>\n",
       "      <th>Standard-Deviation</th>\n",
       "      <th>low</th>\n",
       "      <th>high</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ln_Poverty</td>\n",
       "      <td>0.878</td>\n",
       "      <td>0.061</td>\n",
       "      <td>0.757</td>\n",
       "      <td>0.999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LPI_Score</td>\n",
       "      <td>0.250</td>\n",
       "      <td>0.017</td>\n",
       "      <td>0.216</td>\n",
       "      <td>0.284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>SalariedEmp</td>\n",
       "      <td>0.064</td>\n",
       "      <td>0.004</td>\n",
       "      <td>0.055</td>\n",
       "      <td>0.073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ln_ArableLand</td>\n",
       "      <td>0.028</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.024</td>\n",
       "      <td>0.032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ln_CerealYield</td>\n",
       "      <td>0.023</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.019</td>\n",
       "      <td>0.028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Net_Food_Trade</td>\n",
       "      <td>0.009</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.007</td>\n",
       "      <td>0.010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2018</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>CCPI</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2016</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.001</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         variable  Variable-Importance  Standard-Deviation   low  high\n",
       "0      ln_Poverty                0.878               0.061 0.757 0.999\n",
       "1       LPI_Score                0.250               0.017 0.216 0.284\n",
       "2     SalariedEmp                0.064               0.004 0.055 0.073\n",
       "3   ln_ArableLand                0.028               0.002 0.024 0.032\n",
       "4  ln_CerealYield                0.023               0.002 0.019 0.028\n",
       "5  Net_Food_Trade                0.009               0.001 0.007 0.010\n",
       "6            2018                0.002               0.000 0.002 0.003\n",
       "7            CCPI                0.002               0.000 0.001 0.003\n",
       "8            2016                0.001               0.000 0.000 0.001"
      ]
     },
     "execution_count": 223,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vi_dat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "id": "bf3e9462",
   "metadata": {},
   "outputs": [],
   "source": [
    "dfi.export(vi_dat, 'dataframe.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90b23c9a",
   "metadata": {},
   "source": [
    "### Partial Dependency Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "id": "d9283155",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Target specific features\n",
    "features = ['ln_Poverty','LPI_Score','SalariedEmp']\n",
    "\n",
    "# Calculate the partial dependency\n",
    "fig, ax = plt.subplots(figsize=(15, 4))\n",
    "display = plot_partial_dependence(\n",
    "    mod, train_X, features,n_cols=5,\n",
    "    n_jobs=4, grid_resolution=30,ax=ax\n",
    ")\n",
    "fig.suptitle(\"Partial Dependency Plots\", fontsize = 20)\n",
    "plt.savefig('PDP.png', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "id": "ba4c53f8",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Feed in the ineraction as a nested list\n",
    "interacted_features = [['ln_Poverty','LPI_Score'],['ln_Poverty','SalariedEmp'],['SalariedEmp','LPI_Score']] \n",
    "\n",
    "# To view the interactions between the required variables\n",
    "fig, ax = plt.subplots(figsize=(12, 4))\n",
    "display = plot_partial_dependence(\n",
    "    mod, train_X, interacted_features,\n",
    "    n_cols=3,n_jobs=4, grid_resolution=20,ax=ax\n",
    ")\n",
    "fig.suptitle(\"Interactions between predictors\", fontsize = 20)\n",
    "fig.tight_layout()\n",
    "plt.savefig('interaction.png', dpi=300)"
   ]
  }
 ],
 "metadata": {
  "celltoolbar": "Slideshow",
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}