Clustering Project.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing Pandas and NumPy\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>child_mort</th>\n",
       "      <th>exports</th>\n",
       "      <th>health</th>\n",
       "      <th>imports</th>\n",
       "      <th>income</th>\n",
       "      <th>inflation</th>\n",
       "      <th>life_expec</th>\n",
       "      <th>total_fer</th>\n",
       "      <th>gdpp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>90.2</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7.58</td>\n",
       "      <td>44.9</td>\n",
       "      <td>1610</td>\n",
       "      <td>9.44</td>\n",
       "      <td>56.2</td>\n",
       "      <td>5.82</td>\n",
       "      <td>553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>16.6</td>\n",
       "      <td>28.0</td>\n",
       "      <td>6.55</td>\n",
       "      <td>48.6</td>\n",
       "      <td>9930</td>\n",
       "      <td>4.49</td>\n",
       "      <td>76.3</td>\n",
       "      <td>1.65</td>\n",
       "      <td>4090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>27.3</td>\n",
       "      <td>38.4</td>\n",
       "      <td>4.17</td>\n",
       "      <td>31.4</td>\n",
       "      <td>12900</td>\n",
       "      <td>16.10</td>\n",
       "      <td>76.5</td>\n",
       "      <td>2.89</td>\n",
       "      <td>4460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Angola</td>\n",
       "      <td>119.0</td>\n",
       "      <td>62.3</td>\n",
       "      <td>2.85</td>\n",
       "      <td>42.9</td>\n",
       "      <td>5900</td>\n",
       "      <td>22.40</td>\n",
       "      <td>60.1</td>\n",
       "      <td>6.16</td>\n",
       "      <td>3530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Antigua and Barbuda</td>\n",
       "      <td>10.3</td>\n",
       "      <td>45.5</td>\n",
       "      <td>6.03</td>\n",
       "      <td>58.9</td>\n",
       "      <td>19100</td>\n",
       "      <td>1.44</td>\n",
       "      <td>76.8</td>\n",
       "      <td>2.13</td>\n",
       "      <td>12200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               country  child_mort  exports  health  imports  income  \\\n",
       "0          Afghanistan        90.2     10.0    7.58     44.9    1610   \n",
       "1              Albania        16.6     28.0    6.55     48.6    9930   \n",
       "2              Algeria        27.3     38.4    4.17     31.4   12900   \n",
       "3               Angola       119.0     62.3    2.85     42.9    5900   \n",
       "4  Antigua and Barbuda        10.3     45.5    6.03     58.9   19100   \n",
       "\n",
       "   inflation  life_expec  total_fer   gdpp  \n",
       "0       9.44        56.2       5.82    553  \n",
       "1       4.49        76.3       1.65   4090  \n",
       "2      16.10        76.5       2.89   4460  \n",
       "3      22.40        60.1       6.16   3530  \n",
       "4       1.44        76.8       2.13  12200  "
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Loading the dataset\n",
    "\n",
    "country_data = pd.read_csv(\"Country-data.csv\")\n",
    "\n",
    "country_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>child_mort</th>\n",
       "      <th>exports</th>\n",
       "      <th>health</th>\n",
       "      <th>imports</th>\n",
       "      <th>income</th>\n",
       "      <th>inflation</th>\n",
       "      <th>life_expec</th>\n",
       "      <th>total_fer</th>\n",
       "      <th>gdpp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>167.000000</td>\n",
       "      <td>167.000000</td>\n",
       "      <td>167.000000</td>\n",
       "      <td>167.000000</td>\n",
       "      <td>167.000000</td>\n",
       "      <td>167.000000</td>\n",
       "      <td>167.000000</td>\n",
       "      <td>167.000000</td>\n",
       "      <td>167.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>38.270060</td>\n",
       "      <td>41.108976</td>\n",
       "      <td>6.815689</td>\n",
       "      <td>46.890215</td>\n",
       "      <td>17144.688623</td>\n",
       "      <td>7.781832</td>\n",
       "      <td>70.555689</td>\n",
       "      <td>2.947964</td>\n",
       "      <td>12964.155689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>40.328931</td>\n",
       "      <td>27.412010</td>\n",
       "      <td>2.746837</td>\n",
       "      <td>24.209589</td>\n",
       "      <td>19278.067698</td>\n",
       "      <td>10.570704</td>\n",
       "      <td>8.893172</td>\n",
       "      <td>1.513848</td>\n",
       "      <td>18328.704809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2.600000</td>\n",
       "      <td>0.109000</td>\n",
       "      <td>1.810000</td>\n",
       "      <td>0.065900</td>\n",
       "      <td>609.000000</td>\n",
       "      <td>-4.210000</td>\n",
       "      <td>32.100000</td>\n",
       "      <td>1.150000</td>\n",
       "      <td>231.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>8.250000</td>\n",
       "      <td>23.800000</td>\n",
       "      <td>4.920000</td>\n",
       "      <td>30.200000</td>\n",
       "      <td>3355.000000</td>\n",
       "      <td>1.810000</td>\n",
       "      <td>65.300000</td>\n",
       "      <td>1.795000</td>\n",
       "      <td>1330.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>19.300000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>6.320000</td>\n",
       "      <td>43.300000</td>\n",
       "      <td>9960.000000</td>\n",
       "      <td>5.390000</td>\n",
       "      <td>73.100000</td>\n",
       "      <td>2.410000</td>\n",
       "      <td>4660.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>62.100000</td>\n",
       "      <td>51.350000</td>\n",
       "      <td>8.600000</td>\n",
       "      <td>58.750000</td>\n",
       "      <td>22800.000000</td>\n",
       "      <td>10.750000</td>\n",
       "      <td>76.800000</td>\n",
       "      <td>3.880000</td>\n",
       "      <td>14050.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90%</th>\n",
       "      <td>100.220000</td>\n",
       "      <td>70.800000</td>\n",
       "      <td>10.940000</td>\n",
       "      <td>75.420000</td>\n",
       "      <td>41220.000000</td>\n",
       "      <td>16.640000</td>\n",
       "      <td>80.400000</td>\n",
       "      <td>5.322000</td>\n",
       "      <td>41840.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95%</th>\n",
       "      <td>116.000000</td>\n",
       "      <td>80.570000</td>\n",
       "      <td>11.570000</td>\n",
       "      <td>81.140000</td>\n",
       "      <td>48290.000000</td>\n",
       "      <td>20.870000</td>\n",
       "      <td>81.400000</td>\n",
       "      <td>5.861000</td>\n",
       "      <td>48610.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99%</th>\n",
       "      <td>153.400000</td>\n",
       "      <td>160.480000</td>\n",
       "      <td>13.474000</td>\n",
       "      <td>146.080000</td>\n",
       "      <td>84374.000000</td>\n",
       "      <td>41.478000</td>\n",
       "      <td>82.370000</td>\n",
       "      <td>6.563600</td>\n",
       "      <td>79088.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>208.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>17.900000</td>\n",
       "      <td>174.000000</td>\n",
       "      <td>125000.000000</td>\n",
       "      <td>104.000000</td>\n",
       "      <td>82.800000</td>\n",
       "      <td>7.490000</td>\n",
       "      <td>105000.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       child_mort     exports      health     imports         income  \\\n",
       "count  167.000000  167.000000  167.000000  167.000000     167.000000   \n",
       "mean    38.270060   41.108976    6.815689   46.890215   17144.688623   \n",
       "std     40.328931   27.412010    2.746837   24.209589   19278.067698   \n",
       "min      2.600000    0.109000    1.810000    0.065900     609.000000   \n",
       "25%      8.250000   23.800000    4.920000   30.200000    3355.000000   \n",
       "50%     19.300000   35.000000    6.320000   43.300000    9960.000000   \n",
       "75%     62.100000   51.350000    8.600000   58.750000   22800.000000   \n",
       "90%    100.220000   70.800000   10.940000   75.420000   41220.000000   \n",
       "95%    116.000000   80.570000   11.570000   81.140000   48290.000000   \n",
       "99%    153.400000  160.480000   13.474000  146.080000   84374.000000   \n",
       "max    208.000000  200.000000   17.900000  174.000000  125000.000000   \n",
       "\n",
       "        inflation  life_expec   total_fer           gdpp  \n",
       "count  167.000000  167.000000  167.000000     167.000000  \n",
       "mean     7.781832   70.555689    2.947964   12964.155689  \n",
       "std     10.570704    8.893172    1.513848   18328.704809  \n",
       "min     -4.210000   32.100000    1.150000     231.000000  \n",
       "25%      1.810000   65.300000    1.795000    1330.000000  \n",
       "50%      5.390000   73.100000    2.410000    4660.000000  \n",
       "75%     10.750000   76.800000    3.880000   14050.000000  \n",
       "90%     16.640000   80.400000    5.322000   41840.000000  \n",
       "95%     20.870000   81.400000    5.861000   48610.000000  \n",
       "99%     41.478000   82.370000    6.563600   79088.000000  \n",
       "max    104.000000   82.800000    7.490000  105000.000000  "
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking outliers at 25%,50%,75%,90%,95% and 99%\n",
    "# Outliers didn't seem significant enough\n",
    "\n",
    "country_data.describe(percentiles=[.25,.5,.75,.90,.95,.99])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>child_mort</th>\n",
       "      <th>exports</th>\n",
       "      <th>health</th>\n",
       "      <th>imports</th>\n",
       "      <th>income</th>\n",
       "      <th>inflation</th>\n",
       "      <th>life_expec</th>\n",
       "      <th>total_fer</th>\n",
       "      <th>gdpp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>167.000000</td>\n",
       "      <td>167.000000</td>\n",
       "      <td>167.000000</td>\n",
       "      <td>167.000000</td>\n",
       "      <td>167.000000</td>\n",
       "      <td>167.000000</td>\n",
       "      <td>167.000000</td>\n",
       "      <td>167.000000</td>\n",
       "      <td>167.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>38.270060</td>\n",
       "      <td>41.108976</td>\n",
       "      <td>6.815689</td>\n",
       "      <td>46.890215</td>\n",
       "      <td>17144.688623</td>\n",
       "      <td>7.781832</td>\n",
       "      <td>70.555689</td>\n",
       "      <td>2.947964</td>\n",
       "      <td>12964.155689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>40.328931</td>\n",
       "      <td>27.412010</td>\n",
       "      <td>2.746837</td>\n",
       "      <td>24.209589</td>\n",
       "      <td>19278.067698</td>\n",
       "      <td>10.570704</td>\n",
       "      <td>8.893172</td>\n",
       "      <td>1.513848</td>\n",
       "      <td>18328.704809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2.600000</td>\n",
       "      <td>0.109000</td>\n",
       "      <td>1.810000</td>\n",
       "      <td>0.065900</td>\n",
       "      <td>609.000000</td>\n",
       "      <td>-4.210000</td>\n",
       "      <td>32.100000</td>\n",
       "      <td>1.150000</td>\n",
       "      <td>231.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>8.250000</td>\n",
       "      <td>23.800000</td>\n",
       "      <td>4.920000</td>\n",
       "      <td>30.200000</td>\n",
       "      <td>3355.000000</td>\n",
       "      <td>1.810000</td>\n",
       "      <td>65.300000</td>\n",
       "      <td>1.795000</td>\n",
       "      <td>1330.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>19.300000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>6.320000</td>\n",
       "      <td>43.300000</td>\n",
       "      <td>9960.000000</td>\n",
       "      <td>5.390000</td>\n",
       "      <td>73.100000</td>\n",
       "      <td>2.410000</td>\n",
       "      <td>4660.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>62.100000</td>\n",
       "      <td>51.350000</td>\n",
       "      <td>8.600000</td>\n",
       "      <td>58.750000</td>\n",
       "      <td>22800.000000</td>\n",
       "      <td>10.750000</td>\n",
       "      <td>76.800000</td>\n",
       "      <td>3.880000</td>\n",
       "      <td>14050.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>208.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>17.900000</td>\n",
       "      <td>174.000000</td>\n",
       "      <td>125000.000000</td>\n",
       "      <td>104.000000</td>\n",
       "      <td>82.800000</td>\n",
       "      <td>7.490000</td>\n",
       "      <td>105000.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       child_mort     exports      health     imports         income  \\\n",
       "count  167.000000  167.000000  167.000000  167.000000     167.000000   \n",
       "mean    38.270060   41.108976    6.815689   46.890215   17144.688623   \n",
       "std     40.328931   27.412010    2.746837   24.209589   19278.067698   \n",
       "min      2.600000    0.109000    1.810000    0.065900     609.000000   \n",
       "25%      8.250000   23.800000    4.920000   30.200000    3355.000000   \n",
       "50%     19.300000   35.000000    6.320000   43.300000    9960.000000   \n",
       "75%     62.100000   51.350000    8.600000   58.750000   22800.000000   \n",
       "max    208.000000  200.000000   17.900000  174.000000  125000.000000   \n",
       "\n",
       "        inflation  life_expec   total_fer           gdpp  \n",
       "count  167.000000  167.000000  167.000000     167.000000  \n",
       "mean     7.781832   70.555689    2.947964   12964.155689  \n",
       "std     10.570704    8.893172    1.513848   18328.704809  \n",
       "min     -4.210000   32.100000    1.150000     231.000000  \n",
       "25%      1.810000   65.300000    1.795000    1330.000000  \n",
       "50%      5.390000   73.100000    2.410000    4660.000000  \n",
       "75%     10.750000   76.800000    3.880000   14050.000000  \n",
       "max    104.000000   82.800000    7.490000  105000.000000  "
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Getting an overview of the data\n",
    "\n",
    "country_data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 167 entries, 0 to 166\n",
      "Data columns (total 10 columns):\n",
      "country       167 non-null object\n",
      "child_mort    167 non-null float64\n",
      "exports       167 non-null float64\n",
      "health        167 non-null float64\n",
      "imports       167 non-null float64\n",
      "income        167 non-null int64\n",
      "inflation     167 non-null float64\n",
      "life_expec    167 non-null float64\n",
      "total_fer     167 non-null float64\n",
      "gdpp          167 non-null int64\n",
      "dtypes: float64(7), int64(2), object(1)\n",
      "memory usage: 13.1+ KB\n"
     ]
    }
   ],
   "source": [
    "# Checking to see both the data types and any null values\n",
    "# Since there are no null values, we can leave the data as is\n",
    "\n",
    "country_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing libraries for clustering analysis\n",
    "\n",
    "import sklearn\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.metrics import silhouette_score\n",
    "\n",
    "from scipy.cluster.hierarchy import linkage\n",
    "from scipy.cluster.hierarchy import dendrogram\n",
    "from scipy.cluster.hierarchy import cut_tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(167, 9)"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Rescaling the data using StandardScaler\n",
    "\n",
    "new_df = country_data[['child_mort','exports','health','imports','income','inflation','life_expec','total_fer','gdpp']]\n",
    "\n",
    "scaler = StandardScaler()\n",
    "\n",
    "new_df_scaled = scaler.fit_transform(new_df)\n",
    "new_df_scaled.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "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>child_mort</th>\n",
       "      <th>exports</th>\n",
       "      <th>health</th>\n",
       "      <th>imports</th>\n",
       "      <th>income</th>\n",
       "      <th>inflation</th>\n",
       "      <th>life_expec</th>\n",
       "      <th>total_fer</th>\n",
       "      <th>gdpp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.291532</td>\n",
       "      <td>-1.138280</td>\n",
       "      <td>0.279088</td>\n",
       "      <td>-0.082455</td>\n",
       "      <td>-0.808245</td>\n",
       "      <td>0.157336</td>\n",
       "      <td>-1.619092</td>\n",
       "      <td>1.902882</td>\n",
       "      <td>-0.679180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.538949</td>\n",
       "      <td>-0.479658</td>\n",
       "      <td>-0.097016</td>\n",
       "      <td>0.070837</td>\n",
       "      <td>-0.375369</td>\n",
       "      <td>-0.312347</td>\n",
       "      <td>0.647866</td>\n",
       "      <td>-0.859973</td>\n",
       "      <td>-0.485623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.272833</td>\n",
       "      <td>-0.099122</td>\n",
       "      <td>-0.966073</td>\n",
       "      <td>-0.641762</td>\n",
       "      <td>-0.220844</td>\n",
       "      <td>0.789274</td>\n",
       "      <td>0.670423</td>\n",
       "      <td>-0.038404</td>\n",
       "      <td>-0.465376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.007808</td>\n",
       "      <td>0.775381</td>\n",
       "      <td>-1.448071</td>\n",
       "      <td>-0.165315</td>\n",
       "      <td>-0.585043</td>\n",
       "      <td>1.387054</td>\n",
       "      <td>-1.179234</td>\n",
       "      <td>2.128151</td>\n",
       "      <td>-0.516268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.695634</td>\n",
       "      <td>0.160668</td>\n",
       "      <td>-0.286894</td>\n",
       "      <td>0.497568</td>\n",
       "      <td>0.101732</td>\n",
       "      <td>-0.601749</td>\n",
       "      <td>0.704258</td>\n",
       "      <td>-0.541946</td>\n",
       "      <td>-0.041817</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   child_mort   exports    health   imports    income  inflation  life_expec  \\\n",
       "0    1.291532 -1.138280  0.279088 -0.082455 -0.808245   0.157336   -1.619092   \n",
       "1   -0.538949 -0.479658 -0.097016  0.070837 -0.375369  -0.312347    0.647866   \n",
       "2   -0.272833 -0.099122 -0.966073 -0.641762 -0.220844   0.789274    0.670423   \n",
       "3    2.007808  0.775381 -1.448071 -0.165315 -0.585043   1.387054   -1.179234   \n",
       "4   -0.695634  0.160668 -0.286894  0.497568  0.101732  -0.601749    0.704258   \n",
       "\n",
       "   total_fer      gdpp  \n",
       "0   1.902882 -0.679180  \n",
       "1  -0.859973 -0.485623  \n",
       "2  -0.038404 -0.465376  \n",
       "3   2.128151 -0.516268  \n",
       "4  -0.541946 -0.041817  "
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking to see the transformed dataframe\n",
    "\n",
    "new_df_scaled = pd.DataFrame(new_df_scaled)\n",
    "new_df_scaled.columns = ['child_mort','exports','health','imports','income','inflation','life_expec','total_fer','gdpp']\n",
    "new_df_scaled.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=50,\n",
       "       n_clusters=5, n_init=10, n_jobs=None, precompute_distances='auto',\n",
       "       random_state=None, tol=0.0001, verbose=0)"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Performing a k-means clustering with an arbitrary k of 5 \n",
    "\n",
    "kmeans = KMeans(n_clusters=5, max_iter=50)\n",
    "kmeans.fit(new_df_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 0, 1, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 2, 0, 1, 0, 0, 0, 1,\n",
       "       0, 2, 0, 1, 1, 0, 1, 2, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 2, 0,\n",
       "       2, 0, 0, 0, 0, 1, 1, 0, 0, 2, 2, 1, 1, 0, 2, 1, 2, 0, 0, 1, 1, 0,\n",
       "       1, 0, 2, 0, 0, 0, 1, 2, 2, 2, 0, 2, 0, 0, 1, 1, 2, 0, 1, 0, 0, 1,\n",
       "       1, 0, 0, 3, 0, 1, 1, 0, 0, 1, 3, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0,\n",
       "       2, 2, 1, 4, 2, 0, 1, 0, 0, 0, 0, 0, 2, 2, 0, 0, 1, 0, 0, 1, 0, 0,\n",
       "       1, 3, 0, 2, 1, 1, 0, 2, 0, 0, 1, 0, 2, 2, 0, 1, 0, 1, 1, 0, 0, 0,\n",
       "       0, 1, 0, 2, 2, 2, 0, 0, 0, 0, 0, 1, 1])"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Getting the labels of each point\n",
    "\n",
    "kmeans.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0xf489711ef0>]"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Performing an elbow-curve/SSD to find the optimum number of clusters\n",
    "# And, plotting the SSDs for each n_clusters\n",
    "\n",
    "ssd = []\n",
    "range_n_clusters = [2, 3, 4, 5, 6, 7, 8]\n",
    "for num_clusters in range_n_clusters:\n",
    "    kmeans = KMeans(n_clusters=num_clusters, max_iter=50)\n",
    "    kmeans.fit(new_df_scaled)\n",
    "    \n",
    "    ssd.append(kmeans.inertia_)\n",
    "    \n",
    "plt.plot(ssd)\n",
    "\n",
    "# From the elbow curve it seems that 2 is the most optimum number of clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For n_clusters=2, the silhouette score is 0.28735668921406704\n",
      "For n_clusters=3, the silhouette score is 0.28329575683463126\n",
      "For n_clusters=4, the silhouette score is 0.29750893814718204\n",
      "For n_clusters=5, the silhouette score is 0.2182493210148211\n",
      "For n_clusters=6, the silhouette score is 0.23293312221599696\n",
      "For n_clusters=7, the silhouette score is 0.24827056701134226\n",
      "For n_clusters=8, the silhouette score is 0.2297199321818917\n"
     ]
    }
   ],
   "source": [
    "# Performing a silhouette analysis\n",
    "\n",
    "range_n_clusters = [2, 3, 4, 5, 6, 7, 8]\n",
    "\n",
    "for num_clusters in range_n_clusters:\n",
    "    \n",
    "    # Intializing kmeans\n",
    "    kmeans = KMeans(n_clusters=num_clusters, max_iter=50)\n",
    "    kmeans.fit(new_df_scaled)\n",
    "    \n",
    "    cluster_labels = kmeans.labels_\n",
    "    \n",
    "    # Calculating the silhouette score\n",
    "    silhouette_avg = silhouette_score(new_df_scaled, cluster_labels)\n",
    "    print(\"For n_clusters={0}, the silhouette score is {1}\".format(num_clusters, silhouette_avg))\n",
    "    \n",
    "# From the analysis, it seems that 4 has the highest silhouette score. However, a k value of 4 doesn't seem appropriate as \n",
    "# the aim of the entire analysis is to determine which country is worse of in the lot. So, either a country should belong to\n",
    "# a cluster of \"better\" countries or a cluster of \"worse\" countries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=50,\n",
       "       n_clusters=2, n_init=10, n_jobs=None, precompute_distances='auto',\n",
       "       random_state=None, tol=0.0001, verbose=0)"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Therefore, we use a final model with k=2\n",
    "\n",
    "kmeans = KMeans(n_clusters=2, max_iter=50)\n",
    "kmeans.fit(new_df_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0,\n",
       "       1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1,\n",
       "       1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0,\n",
       "       0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0,\n",
       "       0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0,\n",
       "       1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1,\n",
       "       0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 1,\n",
       "       0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0])"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Relabelling each point \n",
    "\n",
    "kmeans.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "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>child_mort</th>\n",
       "      <th>exports</th>\n",
       "      <th>health</th>\n",
       "      <th>imports</th>\n",
       "      <th>income</th>\n",
       "      <th>inflation</th>\n",
       "      <th>life_expec</th>\n",
       "      <th>total_fer</th>\n",
       "      <th>gdpp</th>\n",
       "      <th>cluster_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>90.2</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7.58</td>\n",
       "      <td>44.9</td>\n",
       "      <td>1610</td>\n",
       "      <td>9.44</td>\n",
       "      <td>56.2</td>\n",
       "      <td>5.82</td>\n",
       "      <td>553</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>16.6</td>\n",
       "      <td>28.0</td>\n",
       "      <td>6.55</td>\n",
       "      <td>48.6</td>\n",
       "      <td>9930</td>\n",
       "      <td>4.49</td>\n",
       "      <td>76.3</td>\n",
       "      <td>1.65</td>\n",
       "      <td>4090</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>27.3</td>\n",
       "      <td>38.4</td>\n",
       "      <td>4.17</td>\n",
       "      <td>31.4</td>\n",
       "      <td>12900</td>\n",
       "      <td>16.10</td>\n",
       "      <td>76.5</td>\n",
       "      <td>2.89</td>\n",
       "      <td>4460</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Angola</td>\n",
       "      <td>119.0</td>\n",
       "      <td>62.3</td>\n",
       "      <td>2.85</td>\n",
       "      <td>42.9</td>\n",
       "      <td>5900</td>\n",
       "      <td>22.40</td>\n",
       "      <td>60.1</td>\n",
       "      <td>6.16</td>\n",
       "      <td>3530</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Antigua and Barbuda</td>\n",
       "      <td>10.3</td>\n",
       "      <td>45.5</td>\n",
       "      <td>6.03</td>\n",
       "      <td>58.9</td>\n",
       "      <td>19100</td>\n",
       "      <td>1.44</td>\n",
       "      <td>76.8</td>\n",
       "      <td>2.13</td>\n",
       "      <td>12200</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               country  child_mort  exports  health  imports  income  \\\n",
       "0          Afghanistan        90.2     10.0    7.58     44.9    1610   \n",
       "1              Albania        16.6     28.0    6.55     48.6    9930   \n",
       "2              Algeria        27.3     38.4    4.17     31.4   12900   \n",
       "3               Angola       119.0     62.3    2.85     42.9    5900   \n",
       "4  Antigua and Barbuda        10.3     45.5    6.03     58.9   19100   \n",
       "\n",
       "   inflation  life_expec  total_fer   gdpp  cluster_id  \n",
       "0       9.44        56.2       5.82    553           0  \n",
       "1       4.49        76.3       1.65   4090           1  \n",
       "2      16.10        76.5       2.89   4460           1  \n",
       "3      22.40        60.1       6.16   3530           0  \n",
       "4       1.44        76.8       2.13  12200           1  "
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Assigning the cluster label as a new column \"cluster_id\"\n",
    "\n",
    "country_data['cluster_id'] = kmeans.labels_\n",
    "country_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xf4891765c0>"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting a boxplot of each cluster in terms of its GDPP to see what the variation is within each cluster\n",
    "\n",
    "sns.boxplot(x='cluster_id', y='gdpp', data=country_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xf480d072e8>"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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wGCRJBSefpQ61Y8cOfr2vd1ImGzV1bNvXy/E7dtT6HvYYJEkFewxSh+rr6+OZ4V2erqrCJ+48keP6+mp9D3sMkqSCwSBJKjiUJHWwh55y8hngkf2tv2HnvfRgw5U076GnellQ83sYDFKHcsG45/ymWkTvuLP8P1lA/Z+NjguGiFgErAB6gc9n5g0NlyQ1olPW0HKV11I3rP7bUXMMEdEL/AtwIXAO8N6IOKfZqiQ1bebMma7+O4k6rcfwRmAoMx8EiIivApcCP2u0KqmLTfe/jnW4juoxAH3Aw6Pub6/aJEmTpNOCIcZoO2yLuYhYGhGDETG4e/fuSShLkrpHpwXDduCMUfdPB3YeelBmrsrMgcwcmDt37qQVJ0ndoNOC4cfAgog4OyJeDLwHWNtwTZLUVTpq8jkzhyPig8DttE5XvSkz7224LEnqKh0VDACZuQ5Y13QdktStOm0oSZLUMINBklSIzMPOBp1SImI3sK3pOqaROcBjTRchjcHP5sQ6KzPHPK1zygeDJlZEDGbmQNN1SIfyszl5HEqSJBUMBklSwWDQoVY1XYD0PPxsThLnGCRJBXsMkqSCwSBJKhgMAlpbqkbEzyNiKCKubroeaURE3BQRj0bEPU3X0i0MBrmlqjrdF4FFTRfRTQwGwagtVTPzN8DIlqpS4zLzDmBv03V0E4NB4JaqkkYxGATj3FJVUncwGATj3FJVUncwGARuqSppFINBZOYwMLKl6n3AzW6pqk4REV8Bvg+8IiK2R8QHmq5punNJDElSwR6DJKlgMEiSCgaDJKlgMEiSCgaDJKlgMEiSCgaDdIiI+HhE/M0xPO+kiPiLCaxjXUScNEb7MdUnjZfBIE2ck4C2giFaxvw5zMyLMvNXE1KZ1AaDQV0vIhZHxN0R8dOI+NIhj307Igaq23MiYmt1+9yI+FFE3FU9dwFwA/Dyqu2fquM+HBE/ro65tmqbHxH3RcRngDsp16ka/d5bI2JOdftj1UZKG4FX1PM/IbXMaLoAqUkRcS7wMeDNmflYRJwCXDWOp/45sCIzv1ytL9ULXA28OjPPq177AmABrf0uAlgbEX8APETrl/sVmXnUHkZEvIHW+lWvo/Uzeyewub1/qTR+BoO63VuBr2fmYwCZuTdirFXID/N94GMRcTpwS2Y+MMbzLqguP6nuz6IVFA8B2zLzB+Os8feBWzNzP0BEuMChauVQkrpdcOS9J4Z57ufkJSONmflvwCXA08DtEfHW53ntf8jM86pLf2Z+oXrs123W6aJmmjQGg7rdJuCyiJgNUA0ljbYVeEN1+09GGiPiZcCDmXkjrSXKXwPsA04Y9dzbgfdHxKzqOX0Rceox1HgH8M6ImBkRJwAXH8NrSOPmUJK6WmbeGxHXA9+JiAO0hn22jjrkk8DNEfE+4L9Htf8pcHlE/Bb4JXBdNQz1vYi4B/hmZn44Il4FfL8aZnoKuBw40GaNd0bE14C7gG3A/xzLv1UaL5fdliQVHEqSJBUcSpIaFhE/BI47pPl9mbmliXokh5IkSQWHkiRJBYNBklQwGCRJBYNBklT4f5NozSARW32EAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting a boxplot of each cluster in terms of its Child Mortality to see what the variation is within each cluster\n",
    "\n",
    "sns.boxplot(x='cluster_id', y='child_mort', data=country_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xf480d68f28>"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting a boxplot of each cluster in terms of its Income to see what the variation is within each cluster\n",
    "\n",
    "sns.boxplot(x='cluster_id', y='income', data=country_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Perfomrimg a hierarchical clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "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>child_mort</th>\n",
       "      <th>exports</th>\n",
       "      <th>health</th>\n",
       "      <th>imports</th>\n",
       "      <th>income</th>\n",
       "      <th>inflation</th>\n",
       "      <th>life_expec</th>\n",
       "      <th>total_fer</th>\n",
       "      <th>gdpp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.291532</td>\n",
       "      <td>-1.138280</td>\n",
       "      <td>0.279088</td>\n",
       "      <td>-0.082455</td>\n",
       "      <td>-0.808245</td>\n",
       "      <td>0.157336</td>\n",
       "      <td>-1.619092</td>\n",
       "      <td>1.902882</td>\n",
       "      <td>-0.679180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.538949</td>\n",
       "      <td>-0.479658</td>\n",
       "      <td>-0.097016</td>\n",
       "      <td>0.070837</td>\n",
       "      <td>-0.375369</td>\n",
       "      <td>-0.312347</td>\n",
       "      <td>0.647866</td>\n",
       "      <td>-0.859973</td>\n",
       "      <td>-0.485623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.272833</td>\n",
       "      <td>-0.099122</td>\n",
       "      <td>-0.966073</td>\n",
       "      <td>-0.641762</td>\n",
       "      <td>-0.220844</td>\n",
       "      <td>0.789274</td>\n",
       "      <td>0.670423</td>\n",
       "      <td>-0.038404</td>\n",
       "      <td>-0.465376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.007808</td>\n",
       "      <td>0.775381</td>\n",
       "      <td>-1.448071</td>\n",
       "      <td>-0.165315</td>\n",
       "      <td>-0.585043</td>\n",
       "      <td>1.387054</td>\n",
       "      <td>-1.179234</td>\n",
       "      <td>2.128151</td>\n",
       "      <td>-0.516268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.695634</td>\n",
       "      <td>0.160668</td>\n",
       "      <td>-0.286894</td>\n",
       "      <td>0.497568</td>\n",
       "      <td>0.101732</td>\n",
       "      <td>-0.601749</td>\n",
       "      <td>0.704258</td>\n",
       "      <td>-0.541946</td>\n",
       "      <td>-0.041817</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   child_mort   exports    health   imports    income  inflation  life_expec  \\\n",
       "0    1.291532 -1.138280  0.279088 -0.082455 -0.808245   0.157336   -1.619092   \n",
       "1   -0.538949 -0.479658 -0.097016  0.070837 -0.375369  -0.312347    0.647866   \n",
       "2   -0.272833 -0.099122 -0.966073 -0.641762 -0.220844   0.789274    0.670423   \n",
       "3    2.007808  0.775381 -1.448071 -0.165315 -0.585043   1.387054   -1.179234   \n",
       "4   -0.695634  0.160668 -0.286894  0.497568  0.101732  -0.601749    0.704258   \n",
       "\n",
       "   total_fer      gdpp  \n",
       "0   1.902882 -0.679180  \n",
       "1  -0.859973 -0.485623  \n",
       "2  -0.038404 -0.465376  \n",
       "3   2.128151 -0.516268  \n",
       "4  -0.541946 -0.041817  "
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Visualizing the rescaled dataset\n",
    "\n",
    "new_df_scaled.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Performing a single linkage first shows us 2 clusters\n",
    "\n",
    "mergings = linkage(new_df_scaled, method=\"single\", metric='euclidean')\n",
    "dendrogram(mergings)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Perfoming a complete linkage shows 3 clusters\n",
    "\n",
    "mergings = linkage(new_df_scaled, method=\"complete\", metric='euclidean')\n",
    "dendrogram(mergings)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0,\n",
       "       1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0,\n",
       "       0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0,\n",
       "       0, 1, 1, 2, 1, 0, 0, 1, 1, 0, 2, 0, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1,\n",
       "       1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1,\n",
       "       0, 2, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 1,\n",
       "       1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0])"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Choosing 3 clusters to see if it is a better clustering approach. Also, to see whether we can arrive to a list of the \n",
    "# extreme countries that direly need help\n",
    "\n",
    "cluster_labels = cut_tree(mergings, n_clusters=3).reshape(-1, )\n",
    "cluster_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "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>child_mort</th>\n",
       "      <th>exports</th>\n",
       "      <th>health</th>\n",
       "      <th>imports</th>\n",
       "      <th>income</th>\n",
       "      <th>inflation</th>\n",
       "      <th>life_expec</th>\n",
       "      <th>total_fer</th>\n",
       "      <th>gdpp</th>\n",
       "      <th>cluster_id</th>\n",
       "      <th>cluster_labels</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>90.2</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7.58</td>\n",
       "      <td>44.9</td>\n",
       "      <td>1610</td>\n",
       "      <td>9.44</td>\n",
       "      <td>56.2</td>\n",
       "      <td>5.82</td>\n",
       "      <td>553</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>16.6</td>\n",
       "      <td>28.0</td>\n",
       "      <td>6.55</td>\n",
       "      <td>48.6</td>\n",
       "      <td>9930</td>\n",
       "      <td>4.49</td>\n",
       "      <td>76.3</td>\n",
       "      <td>1.65</td>\n",
       "      <td>4090</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>27.3</td>\n",
       "      <td>38.4</td>\n",
       "      <td>4.17</td>\n",
       "      <td>31.4</td>\n",
       "      <td>12900</td>\n",
       "      <td>16.10</td>\n",
       "      <td>76.5</td>\n",
       "      <td>2.89</td>\n",
       "      <td>4460</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Angola</td>\n",
       "      <td>119.0</td>\n",
       "      <td>62.3</td>\n",
       "      <td>2.85</td>\n",
       "      <td>42.9</td>\n",
       "      <td>5900</td>\n",
       "      <td>22.40</td>\n",
       "      <td>60.1</td>\n",
       "      <td>6.16</td>\n",
       "      <td>3530</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Antigua and Barbuda</td>\n",
       "      <td>10.3</td>\n",
       "      <td>45.5</td>\n",
       "      <td>6.03</td>\n",
       "      <td>58.9</td>\n",
       "      <td>19100</td>\n",
       "      <td>1.44</td>\n",
       "      <td>76.8</td>\n",
       "      <td>2.13</td>\n",
       "      <td>12200</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               country  child_mort  exports  health  imports  income  \\\n",
       "0          Afghanistan        90.2     10.0    7.58     44.9    1610   \n",
       "1              Albania        16.6     28.0    6.55     48.6    9930   \n",
       "2              Algeria        27.3     38.4    4.17     31.4   12900   \n",
       "3               Angola       119.0     62.3    2.85     42.9    5900   \n",
       "4  Antigua and Barbuda        10.3     45.5    6.03     58.9   19100   \n",
       "\n",
       "   inflation  life_expec  total_fer   gdpp  cluster_id  cluster_labels  \n",
       "0       9.44        56.2       5.82    553           0               0  \n",
       "1       4.49        76.3       1.65   4090           1               1  \n",
       "2      16.10        76.5       2.89   4460           1               1  \n",
       "3      22.40        60.1       6.16   3530           0               0  \n",
       "4       1.44        76.8       2.13  12200           1               1  "
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Assigning hierarchical clustering labels as \"cluster_labels\"\n",
    "\n",
    "country_data['cluster_labels'] = cluster_labels\n",
    "country_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xf489574358>"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting a boxplot of each cluster in terms of its GDPP to see what the variation is within each cluster\n",
    "\n",
    "sns.boxplot(x='cluster_labels', y='gdpp', data=country_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xf4895d8630>"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting a boxplot of each cluster in terms of its Child Mortality to see what the variation is within each cluster\n",
    "\n",
    "sns.boxplot(x='cluster_labels', y='child_mort', data=country_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xf4894f95f8>"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting a boxplot of each cluster in terms of its Income to see what the variation is within each cluster\n",
    "\n",
    "sns.boxplot(x='cluster_labels', y='income', data=country_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "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>child_mort</th>\n",
       "      <th>exports</th>\n",
       "      <th>health</th>\n",
       "      <th>imports</th>\n",
       "      <th>income</th>\n",
       "      <th>inflation</th>\n",
       "      <th>life_expec</th>\n",
       "      <th>total_fer</th>\n",
       "      <th>gdpp</th>\n",
       "      <th>cluster_id</th>\n",
       "      <th>cluster_labels</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>90.2</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7.58</td>\n",
       "      <td>44.9</td>\n",
       "      <td>1610</td>\n",
       "      <td>9.44</td>\n",
       "      <td>56.2</td>\n",
       "      <td>5.82</td>\n",
       "      <td>553</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>16.6</td>\n",
       "      <td>28.0</td>\n",
       "      <td>6.55</td>\n",
       "      <td>48.6</td>\n",
       "      <td>9930</td>\n",
       "      <td>4.49</td>\n",
       "      <td>76.3</td>\n",
       "      <td>1.65</td>\n",
       "      <td>4090</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>27.3</td>\n",
       "      <td>38.4</td>\n",
       "      <td>4.17</td>\n",
       "      <td>31.4</td>\n",
       "      <td>12900</td>\n",
       "      <td>16.10</td>\n",
       "      <td>76.5</td>\n",
       "      <td>2.89</td>\n",
       "      <td>4460</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Angola</td>\n",
       "      <td>119.0</td>\n",
       "      <td>62.3</td>\n",
       "      <td>2.85</td>\n",
       "      <td>42.9</td>\n",
       "      <td>5900</td>\n",
       "      <td>22.40</td>\n",
       "      <td>60.1</td>\n",
       "      <td>6.16</td>\n",
       "      <td>3530</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Antigua and Barbuda</td>\n",
       "      <td>10.3</td>\n",
       "      <td>45.5</td>\n",
       "      <td>6.03</td>\n",
       "      <td>58.9</td>\n",
       "      <td>19100</td>\n",
       "      <td>1.44</td>\n",
       "      <td>76.8</td>\n",
       "      <td>2.13</td>\n",
       "      <td>12200</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               country  child_mort  exports  health  imports  income  \\\n",
       "0          Afghanistan        90.2     10.0    7.58     44.9    1610   \n",
       "1              Albania        16.6     28.0    6.55     48.6    9930   \n",
       "2              Algeria        27.3     38.4    4.17     31.4   12900   \n",
       "3               Angola       119.0     62.3    2.85     42.9    5900   \n",
       "4  Antigua and Barbuda        10.3     45.5    6.03     58.9   19100   \n",
       "\n",
       "   inflation  life_expec  total_fer   gdpp  cluster_id  cluster_labels  \n",
       "0       9.44        56.2       5.82    553           0               0  \n",
       "1       4.49        76.3       1.65   4090           1               1  \n",
       "2      16.10        76.5       2.89   4460           1               1  \n",
       "3      22.40        60.1       6.16   3530           0               0  \n",
       "4       1.44        76.8       2.13  12200           1               1  "
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The countries' that would need the most help will be the ones that are catgorized into cluster '0' of both k-means \n",
    "# and hierarhical clustering\n",
    "\n",
    "country_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Building on the previous hypothesis that countries categorized as \"0\" will be the ones that are worse through both k means \n",
    "# and hierarchical clustering exercises\n",
    "\n",
    "country_new = country_data[(country_data['cluster_id'] == 0) & (country_data['cluster_labels'] == 0)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>child_mort</th>\n",
       "      <th>exports</th>\n",
       "      <th>health</th>\n",
       "      <th>imports</th>\n",
       "      <th>income</th>\n",
       "      <th>inflation</th>\n",
       "      <th>life_expec</th>\n",
       "      <th>total_fer</th>\n",
       "      <th>gdpp</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>53.000000</td>\n",
       "      <td>53.000000</td>\n",
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       "      <td>53.0</td>\n",
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       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>84.071698</td>\n",
       "      <td>30.700377</td>\n",
       "      <td>6.691698</td>\n",
       "      <td>47.826415</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>38.760455</td>\n",
       "      <td>17.671113</td>\n",
       "      <td>2.600933</td>\n",
       "      <td>18.831695</td>\n",
       "      <td>5081.531433</td>\n",
       "      <td>14.841667</td>\n",
       "      <td>6.795745</td>\n",
       "      <td>1.144635</td>\n",
       "      <td>2648.649299</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>17.400000</td>\n",
       "      <td>2.200000</td>\n",
       "      <td>2.460000</td>\n",
       "      <td>17.200000</td>\n",
       "      <td>609.000000</td>\n",
       "      <td>0.885000</td>\n",
       "      <td>32.100000</td>\n",
       "      <td>2.590000</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>56.300000</td>\n",
       "      <td>18.700000</td>\n",
       "      <td>5.070000</td>\n",
       "      <td>34.400000</td>\n",
       "      <td>1410.000000</td>\n",
       "      <td>3.560000</td>\n",
       "      <td>57.100000</td>\n",
       "      <td>3.910000</td>\n",
       "      <td>563.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>81.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>5.890000</td>\n",
       "      <td>43.500000</td>\n",
       "      <td>2110.000000</td>\n",
       "      <td>6.810000</td>\n",
       "      <td>60.500000</td>\n",
       "      <td>4.980000</td>\n",
       "      <td>967.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>111.000000</td>\n",
       "      <td>40.200000</td>\n",
       "      <td>8.300000</td>\n",
       "      <td>58.600000</td>\n",
       "      <td>3370.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>65.400000</td>\n",
       "      <td>5.430000</td>\n",
       "      <td>2330.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <th>max</th>\n",
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       "      <td>85.800000</td>\n",
       "      <td>14.200000</td>\n",
       "      <td>101.000000</td>\n",
       "      <td>33700.000000</td>\n",
       "      <td>104.000000</td>\n",
       "      <td>71.500000</td>\n",
       "      <td>7.490000</td>\n",
       "      <td>17100.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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      ],
      "text/plain": [
       "       child_mort    exports     health     imports        income   inflation  \\\n",
       "count   53.000000  53.000000  53.000000   53.000000     53.000000   53.000000   \n",
       "mean    84.071698  30.700377   6.691698   47.826415   3696.283019   10.864245   \n",
       "std     38.760455  17.671113   2.600933   18.831695   5081.531433   14.841667   \n",
       "min     17.400000   2.200000   2.460000   17.200000    609.000000    0.885000   \n",
       "25%     56.300000  18.700000   5.070000   34.400000   1410.000000    3.560000   \n",
       "50%     81.000000  25.000000   5.890000   43.500000   2110.000000    6.810000   \n",
       "75%    111.000000  40.200000   8.300000   58.600000   3370.000000   14.000000   \n",
       "max    208.000000  85.800000  14.200000  101.000000  33700.000000  104.000000   \n",
       "\n",
       "       life_expec  total_fer          gdpp  cluster_id  cluster_labels  \n",
       "count   53.000000  53.000000     53.000000        53.0            53.0  \n",
       "mean    60.247170   4.800566   1880.075472         0.0             0.0  \n",
       "std      6.795745   1.144635   2648.649299         0.0             0.0  \n",
       "min     32.100000   2.590000    231.000000         0.0             0.0  \n",
       "25%     57.100000   3.910000    563.000000         0.0             0.0  \n",
       "50%     60.500000   4.980000    967.000000         0.0             0.0  \n",
       "75%     65.400000   5.430000   2330.000000         0.0             0.0  \n",
       "max     71.500000   7.490000  17100.000000         0.0             0.0  "
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Getting an overview of the new dataset\n",
    "\n",
    "country_new.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Neerja\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n",
      "C:\\Users\\Neerja\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  import sys\n",
      "C:\\Users\\Neerja\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:11: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  # This is added back by InteractiveShellApp.init_path()\n",
      "C:\\Users\\Neerja\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:15: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  from ipykernel import kernelapp as app\n"
     ]
    },
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>child_mort</th>\n",
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       "      <td>Afghanistan</td>\n",
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       "      <td>-20</td>\n",
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       "      <th>17</th>\n",
       "      <td>Benin</td>\n",
       "      <td>111.0</td>\n",
       "      <td>23.80</td>\n",
       "      <td>4.10</td>\n",
       "      <td>37.2</td>\n",
       "      <td>1820</td>\n",
       "      <td>0.885</td>\n",
       "      <td>61.8</td>\n",
       "      <td>5.36</td>\n",
       "      <td>758</td>\n",
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       "      <td>-5</td>\n",
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       "      <th>21</th>\n",
       "      <td>Botswana</td>\n",
       "      <td>52.5</td>\n",
       "      <td>43.60</td>\n",
       "      <td>8.30</td>\n",
       "      <td>51.3</td>\n",
       "      <td>13300</td>\n",
       "      <td>8.920</td>\n",
       "      <td>57.1</td>\n",
       "      <td>2.88</td>\n",
       "      <td>6350</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>-5</td>\n",
       "      <td>-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Burkina Faso</td>\n",
       "      <td>116.0</td>\n",
       "      <td>19.20</td>\n",
       "      <td>6.74</td>\n",
       "      <td>29.6</td>\n",
       "      <td>1430</td>\n",
       "      <td>6.810</td>\n",
       "      <td>57.9</td>\n",
       "      <td>5.87</td>\n",
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       "      <th>26</th>\n",
       "      <td>Burundi</td>\n",
       "      <td>93.6</td>\n",
       "      <td>8.92</td>\n",
       "      <td>11.60</td>\n",
       "      <td>39.2</td>\n",
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       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Cambodia</td>\n",
       "      <td>44.4</td>\n",
       "      <td>54.10</td>\n",
       "      <td>5.68</td>\n",
       "      <td>59.5</td>\n",
       "      <td>2520</td>\n",
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       "      <td>Cameroon</td>\n",
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       "      <td>22.20</td>\n",
       "      <td>5.13</td>\n",
       "      <td>27.0</td>\n",
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       "      <td>57.3</td>\n",
       "      <td>5.11</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Central African Republic</td>\n",
       "      <td>149.0</td>\n",
       "      <td>11.80</td>\n",
       "      <td>3.98</td>\n",
       "      <td>26.5</td>\n",
       "      <td>888</td>\n",
       "      <td>2.010</td>\n",
       "      <td>47.5</td>\n",
       "      <td>5.21</td>\n",
       "      <td>446</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Chad</td>\n",
       "      <td>150.0</td>\n",
       "      <td>36.80</td>\n",
       "      <td>4.53</td>\n",
       "      <td>43.5</td>\n",
       "      <td>1930</td>\n",
       "      <td>6.390</td>\n",
       "      <td>56.5</td>\n",
       "      <td>6.59</td>\n",
       "      <td>897</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Comoros</td>\n",
       "      <td>88.2</td>\n",
       "      <td>16.50</td>\n",
       "      <td>4.51</td>\n",
       "      <td>51.7</td>\n",
       "      <td>1410</td>\n",
       "      <td>3.870</td>\n",
       "      <td>65.9</td>\n",
       "      <td>4.75</td>\n",
       "      <td>769</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-10</td>\n",
       "      <td>0</td>\n",
       "      <td>-20</td>\n",
       "      <td>-5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Congo, Dem. Rep.</td>\n",
       "      <td>116.0</td>\n",
       "      <td>41.10</td>\n",
       "      <td>7.91</td>\n",
       "      <td>49.6</td>\n",
       "      <td>609</td>\n",
       "      <td>20.800</td>\n",
       "      <td>57.5</td>\n",
       "      <td>6.54</td>\n",
       "      <td>334</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-20</td>\n",
       "      <td>-10</td>\n",
       "      <td>-5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Congo, Rep.</td>\n",
       "      <td>63.9</td>\n",
       "      <td>85.10</td>\n",
       "      <td>2.46</td>\n",
       "      <td>54.7</td>\n",
       "      <td>5190</td>\n",
       "      <td>20.700</td>\n",
       "      <td>60.4</td>\n",
       "      <td>4.95</td>\n",
       "      <td>2740</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-5</td>\n",
       "      <td>0</td>\n",
       "      <td>-10</td>\n",
       "      <td>-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>Cote d'Ivoire</td>\n",
       "      <td>111.0</td>\n",
       "      <td>50.60</td>\n",
       "      <td>5.30</td>\n",
       "      <td>43.3</td>\n",
       "      <td>2690</td>\n",
       "      <td>5.390</td>\n",
       "      <td>56.3</td>\n",
       "      <td>5.27</td>\n",
       "      <td>1220</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-10</td>\n",
       "      <td>-5</td>\n",
       "      <td>0</td>\n",
       "      <td>-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>Equatorial Guinea</td>\n",
       "      <td>111.0</td>\n",
       "      <td>85.80</td>\n",
       "      <td>4.48</td>\n",
       "      <td>58.9</td>\n",
       "      <td>33700</td>\n",
       "      <td>24.900</td>\n",
       "      <td>60.9</td>\n",
       "      <td>5.21</td>\n",
       "      <td>17100</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-10</td>\n",
       "      <td>0</td>\n",
       "      <td>-10</td>\n",
       "      <td>-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>Gambia</td>\n",
       "      <td>80.3</td>\n",
       "      <td>23.80</td>\n",
       "      <td>5.69</td>\n",
       "      <td>42.7</td>\n",
       "      <td>1660</td>\n",
       "      <td>4.300</td>\n",
       "      <td>65.5</td>\n",
       "      <td>5.71</td>\n",
       "      <td>562</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-5</td>\n",
       "      <td>-5</td>\n",
       "      <td>-20</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>Ghana</td>\n",
       "      <td>74.7</td>\n",
       "      <td>29.50</td>\n",
       "      <td>5.22</td>\n",
       "      <td>45.9</td>\n",
       "      <td>3060</td>\n",
       "      <td>16.600</td>\n",
       "      <td>62.2</td>\n",
       "      <td>4.27</td>\n",
       "      <td>1310</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-5</td>\n",
       "      <td>-5</td>\n",
       "      <td>-10</td>\n",
       "      <td>-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>Guinea</td>\n",
       "      <td>109.0</td>\n",
       "      <td>30.30</td>\n",
       "      <td>4.93</td>\n",
       "      <td>43.2</td>\n",
       "      <td>1190</td>\n",
       "      <td>16.100</td>\n",
       "      <td>58.0</td>\n",
       "      <td>5.34</td>\n",
       "      <td>648</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-10</td>\n",
       "      <td>0</td>\n",
       "      <td>-5</td>\n",
       "      <td>-5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>Guinea-Bissau</td>\n",
       "      <td>114.0</td>\n",
       "      <td>14.90</td>\n",
       "      <td>8.50</td>\n",
       "      <td>35.2</td>\n",
       "      <td>1390</td>\n",
       "      <td>2.970</td>\n",
       "      <td>55.6</td>\n",
       "      <td>5.05</td>\n",
       "      <td>547</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-20</td>\n",
       "      <td>-20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>Guyana</td>\n",
       "      <td>37.6</td>\n",
       "      <td>51.40</td>\n",
       "      <td>5.38</td>\n",
       "      <td>79.1</td>\n",
       "      <td>5840</td>\n",
       "      <td>5.730</td>\n",
       "      <td>65.5</td>\n",
       "      <td>2.65</td>\n",
       "      <td>3040</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-5</td>\n",
       "      <td>-20</td>\n",
       "      <td>-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>Haiti</td>\n",
       "      <td>208.0</td>\n",
       "      <td>15.30</td>\n",
       "      <td>6.91</td>\n",
       "      <td>64.7</td>\n",
       "      <td>1500</td>\n",
       "      <td>5.450</td>\n",
       "      <td>32.1</td>\n",
       "      <td>3.33</td>\n",
       "      <td>662</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-20</td>\n",
       "      <td>-10</td>\n",
       "      <td>0</td>\n",
       "      <td>-5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>Iraq</td>\n",
       "      <td>36.9</td>\n",
       "      <td>39.40</td>\n",
       "      <td>8.41</td>\n",
       "      <td>34.1</td>\n",
       "      <td>12700</td>\n",
       "      <td>16.600</td>\n",
       "      <td>67.2</td>\n",
       "      <td>4.56</td>\n",
       "      <td>4500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-20</td>\n",
       "      <td>-20</td>\n",
       "      <td>-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>Kenya</td>\n",
       "      <td>62.2</td>\n",
       "      <td>20.70</td>\n",
       "      <td>4.75</td>\n",
       "      <td>33.6</td>\n",
       "      <td>2480</td>\n",
       "      <td>2.090</td>\n",
       "      <td>62.8</td>\n",
       "      <td>4.37</td>\n",
       "      <td>967</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-5</td>\n",
       "      <td>0</td>\n",
       "      <td>-10</td>\n",
       "      <td>-5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>Kiribati</td>\n",
       "      <td>62.7</td>\n",
       "      <td>13.30</td>\n",
       "      <td>11.30</td>\n",
       "      <td>79.9</td>\n",
       "      <td>1730</td>\n",
       "      <td>1.520</td>\n",
       "      <td>60.7</td>\n",
       "      <td>3.84</td>\n",
       "      <td>1490</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-5</td>\n",
       "      <td>-20</td>\n",
       "      <td>-10</td>\n",
       "      <td>-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>Kyrgyz Republic</td>\n",
       "      <td>29.6</td>\n",
       "      <td>51.60</td>\n",
       "      <td>6.18</td>\n",
       "      <td>81.7</td>\n",
       "      <td>2790</td>\n",
       "      <td>10.000</td>\n",
       "      <td>68.5</td>\n",
       "      <td>3.10</td>\n",
       "      <td>880</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-10</td>\n",
       "      <td>-20</td>\n",
       "      <td>-5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>Lao</td>\n",
       "      <td>78.9</td>\n",
       "      <td>35.40</td>\n",
       "      <td>4.47</td>\n",
       "      <td>49.3</td>\n",
       "      <td>3980</td>\n",
       "      <td>9.200</td>\n",
       "      <td>63.8</td>\n",
       "      <td>3.15</td>\n",
       "      <td>1140</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-5</td>\n",
       "      <td>0</td>\n",
       "      <td>-10</td>\n",
       "      <td>-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>Lesotho</td>\n",
       "      <td>99.7</td>\n",
       "      <td>39.40</td>\n",
       "      <td>11.10</td>\n",
       "      <td>101.0</td>\n",
       "      <td>2380</td>\n",
       "      <td>4.150</td>\n",
       "      <td>46.5</td>\n",
       "      <td>3.30</td>\n",
       "      <td>1170</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-10</td>\n",
       "      <td>-20</td>\n",
       "      <td>0</td>\n",
       "      <td>-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>Liberia</td>\n",
       "      <td>89.3</td>\n",
       "      <td>19.10</td>\n",
       "      <td>11.80</td>\n",
       "      <td>92.6</td>\n",
       "      <td>700</td>\n",
       "      <td>5.470</td>\n",
       "      <td>60.8</td>\n",
       "      <td>5.02</td>\n",
       "      <td>327</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-10</td>\n",
       "      <td>-20</td>\n",
       "      <td>-10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>Madagascar</td>\n",
       "      <td>62.2</td>\n",
       "      <td>25.00</td>\n",
       "      <td>3.77</td>\n",
       "      <td>43.0</td>\n",
       "      <td>1390</td>\n",
       "      <td>8.790</td>\n",
       "      <td>60.8</td>\n",
       "      <td>4.60</td>\n",
       "      <td>413</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-5</td>\n",
       "      <td>0</td>\n",
       "      <td>-10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>Malawi</td>\n",
       "      <td>90.5</td>\n",
       "      <td>22.80</td>\n",
       "      <td>6.59</td>\n",
       "      <td>34.9</td>\n",
       "      <td>1030</td>\n",
       "      <td>12.100</td>\n",
       "      <td>53.1</td>\n",
       "      <td>5.31</td>\n",
       "      <td>459</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-10</td>\n",
       "      <td>-10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>Mali</td>\n",
       "      <td>137.0</td>\n",
       "      <td>22.80</td>\n",
       "      <td>4.98</td>\n",
       "      <td>35.1</td>\n",
       "      <td>1870</td>\n",
       "      <td>4.370</td>\n",
       "      <td>59.5</td>\n",
       "      <td>6.55</td>\n",
       "      <td>708</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-20</td>\n",
       "      <td>0</td>\n",
       "      <td>-5</td>\n",
       "      <td>-5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>Mauritania</td>\n",
       "      <td>97.4</td>\n",
       "      <td>50.70</td>\n",
       "      <td>4.41</td>\n",
       "      <td>61.2</td>\n",
       "      <td>3320</td>\n",
       "      <td>18.900</td>\n",
       "      <td>68.2</td>\n",
       "      <td>4.98</td>\n",
       "      <td>1200</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-10</td>\n",
       "      <td>0</td>\n",
       "      <td>-20</td>\n",
       "      <td>-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>Micronesia, Fed. Sts.</td>\n",
       "      <td>40.0</td>\n",
       "      <td>23.50</td>\n",
       "      <td>14.20</td>\n",
       "      <td>81.0</td>\n",
       "      <td>3340</td>\n",
       "      <td>3.800</td>\n",
       "      <td>65.4</td>\n",
       "      <td>3.46</td>\n",
       "      <td>2860</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-20</td>\n",
       "      <td>-20</td>\n",
       "      <td>-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>Mozambique</td>\n",
       "      <td>101.0</td>\n",
       "      <td>31.50</td>\n",
       "      <td>5.21</td>\n",
       "      <td>46.2</td>\n",
       "      <td>918</td>\n",
       "      <td>7.640</td>\n",
       "      <td>54.5</td>\n",
       "      <td>5.56</td>\n",
       "      <td>419</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-10</td>\n",
       "      <td>-5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>Namibia</td>\n",
       "      <td>56.0</td>\n",
       "      <td>47.80</td>\n",
       "      <td>6.78</td>\n",
       "      <td>60.7</td>\n",
       "      <td>8460</td>\n",
       "      <td>3.560</td>\n",
       "      <td>58.6</td>\n",
       "      <td>3.60</td>\n",
       "      <td>5190</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-10</td>\n",
       "      <td>-5</td>\n",
       "      <td>-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>Niger</td>\n",
       "      <td>123.0</td>\n",
       "      <td>22.20</td>\n",
       "      <td>5.16</td>\n",
       "      <td>49.1</td>\n",
       "      <td>814</td>\n",
       "      <td>2.550</td>\n",
       "      <td>58.8</td>\n",
       "      <td>7.49</td>\n",
       "      <td>348</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-20</td>\n",
       "      <td>-5</td>\n",
       "      <td>-5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>Nigeria</td>\n",
       "      <td>130.0</td>\n",
       "      <td>25.30</td>\n",
       "      <td>5.07</td>\n",
       "      <td>17.4</td>\n",
       "      <td>5150</td>\n",
       "      <td>104.000</td>\n",
       "      <td>60.5</td>\n",
       "      <td>5.84</td>\n",
       "      <td>2330</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-20</td>\n",
       "      <td>-5</td>\n",
       "      <td>-10</td>\n",
       "      <td>-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>Rwanda</td>\n",
       "      <td>63.6</td>\n",
       "      <td>12.00</td>\n",
       "      <td>10.50</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1350</td>\n",
       "      <td>2.610</td>\n",
       "      <td>64.6</td>\n",
       "      <td>4.51</td>\n",
       "      <td>563</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-5</td>\n",
       "      <td>-20</td>\n",
       "      <td>-10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>Samoa</td>\n",
       "      <td>18.9</td>\n",
       "      <td>29.20</td>\n",
       "      <td>6.47</td>\n",
       "      <td>53.1</td>\n",
       "      <td>5400</td>\n",
       "      <td>1.720</td>\n",
       "      <td>71.5</td>\n",
       "      <td>4.34</td>\n",
       "      <td>3450</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-10</td>\n",
       "      <td>-20</td>\n",
       "      <td>-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>Senegal</td>\n",
       "      <td>66.8</td>\n",
       "      <td>24.90</td>\n",
       "      <td>5.66</td>\n",
       "      <td>40.3</td>\n",
       "      <td>2180</td>\n",
       "      <td>1.850</td>\n",
       "      <td>64.0</td>\n",
       "      <td>5.06</td>\n",
       "      <td>1000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-5</td>\n",
       "      <td>-5</td>\n",
       "      <td>-10</td>\n",
       "      <td>-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>Sierra Leone</td>\n",
       "      <td>160.0</td>\n",
       "      <td>16.80</td>\n",
       "      <td>13.10</td>\n",
       "      <td>34.5</td>\n",
       "      <td>1220</td>\n",
       "      <td>17.200</td>\n",
       "      <td>55.0</td>\n",
       "      <td>5.20</td>\n",
       "      <td>399</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-20</td>\n",
       "      <td>-20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>Solomon Islands</td>\n",
       "      <td>28.1</td>\n",
       "      <td>49.30</td>\n",
       "      <td>8.55</td>\n",
       "      <td>81.2</td>\n",
       "      <td>1780</td>\n",
       "      <td>6.810</td>\n",
       "      <td>61.7</td>\n",
       "      <td>4.24</td>\n",
       "      <td>1290</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-20</td>\n",
       "      <td>-10</td>\n",
       "      <td>-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>South Africa</td>\n",
       "      <td>53.7</td>\n",
       "      <td>28.60</td>\n",
       "      <td>8.94</td>\n",
       "      <td>27.4</td>\n",
       "      <td>12000</td>\n",
       "      <td>6.350</td>\n",
       "      <td>54.3</td>\n",
       "      <td>2.59</td>\n",
       "      <td>7280</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-20</td>\n",
       "      <td>0</td>\n",
       "      <td>-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>Sudan</td>\n",
       "      <td>76.7</td>\n",
       "      <td>19.70</td>\n",
       "      <td>6.32</td>\n",
       "      <td>17.2</td>\n",
       "      <td>3370</td>\n",
       "      <td>19.600</td>\n",
       "      <td>66.3</td>\n",
       "      <td>4.88</td>\n",
       "      <td>1480</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-5</td>\n",
       "      <td>-10</td>\n",
       "      <td>-20</td>\n",
       "      <td>-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>Tajikistan</td>\n",
       "      <td>52.4</td>\n",
       "      <td>14.90</td>\n",
       "      <td>5.98</td>\n",
       "      <td>58.6</td>\n",
       "      <td>2110</td>\n",
       "      <td>12.500</td>\n",
       "      <td>69.6</td>\n",
       "      <td>3.51</td>\n",
       "      <td>738</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-5</td>\n",
       "      <td>-20</td>\n",
       "      <td>-5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>Tanzania</td>\n",
       "      <td>71.9</td>\n",
       "      <td>18.70</td>\n",
       "      <td>6.01</td>\n",
       "      <td>29.1</td>\n",
       "      <td>2090</td>\n",
       "      <td>9.250</td>\n",
       "      <td>59.3</td>\n",
       "      <td>5.43</td>\n",
       "      <td>702</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-5</td>\n",
       "      <td>-10</td>\n",
       "      <td>-5</td>\n",
       "      <td>-5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>Timor-Leste</td>\n",
       "      <td>62.6</td>\n",
       "      <td>2.20</td>\n",
       "      <td>9.12</td>\n",
       "      <td>27.8</td>\n",
       "      <td>1850</td>\n",
       "      <td>26.500</td>\n",
       "      <td>71.1</td>\n",
       "      <td>6.23</td>\n",
       "      <td>3600</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-5</td>\n",
       "      <td>-20</td>\n",
       "      <td>-20</td>\n",
       "      <td>-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>Togo</td>\n",
       "      <td>90.3</td>\n",
       "      <td>40.20</td>\n",
       "      <td>7.65</td>\n",
       "      <td>57.3</td>\n",
       "      <td>1210</td>\n",
       "      <td>1.180</td>\n",
       "      <td>58.7</td>\n",
       "      <td>4.87</td>\n",
       "      <td>488</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-10</td>\n",
       "      <td>-10</td>\n",
       "      <td>-5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>Tonga</td>\n",
       "      <td>17.4</td>\n",
       "      <td>12.40</td>\n",
       "      <td>5.07</td>\n",
       "      <td>60.3</td>\n",
       "      <td>4980</td>\n",
       "      <td>3.680</td>\n",
       "      <td>69.9</td>\n",
       "      <td>3.91</td>\n",
       "      <td>3550</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-5</td>\n",
       "      <td>-20</td>\n",
       "      <td>-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>Uganda</td>\n",
       "      <td>81.0</td>\n",
       "      <td>17.10</td>\n",
       "      <td>9.01</td>\n",
       "      <td>28.6</td>\n",
       "      <td>1540</td>\n",
       "      <td>10.600</td>\n",
       "      <td>56.8</td>\n",
       "      <td>6.15</td>\n",
       "      <td>595</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-5</td>\n",
       "      <td>-20</td>\n",
       "      <td>0</td>\n",
       "      <td>-5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162</th>\n",
       "      <td>Vanuatu</td>\n",
       "      <td>29.2</td>\n",
       "      <td>46.60</td>\n",
       "      <td>5.25</td>\n",
       "      <td>52.7</td>\n",
       "      <td>2950</td>\n",
       "      <td>2.620</td>\n",
       "      <td>63.0</td>\n",
       "      <td>3.50</td>\n",
       "      <td>2970</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-5</td>\n",
       "      <td>-10</td>\n",
       "      <td>-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>Yemen</td>\n",
       "      <td>56.3</td>\n",
       "      <td>30.00</td>\n",
       "      <td>5.18</td>\n",
       "      <td>34.4</td>\n",
       "      <td>4480</td>\n",
       "      <td>23.600</td>\n",
       "      <td>67.5</td>\n",
       "      <td>4.67</td>\n",
       "      <td>1310</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-5</td>\n",
       "      <td>-20</td>\n",
       "      <td>-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>83.1</td>\n",
       "      <td>37.00</td>\n",
       "      <td>5.89</td>\n",
       "      <td>30.9</td>\n",
       "      <td>3280</td>\n",
       "      <td>14.000</td>\n",
       "      <td>52.0</td>\n",
       "      <td>5.40</td>\n",
       "      <td>1460</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-10</td>\n",
       "      <td>-5</td>\n",
       "      <td>0</td>\n",
       "      <td>-10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      country  child_mort  exports  health  imports  income  \\\n",
       "0                 Afghanistan        90.2    10.00    7.58     44.9    1610   \n",
       "3                      Angola       119.0    62.30    2.85     42.9    5900   \n",
       "17                      Benin       111.0    23.80    4.10     37.2    1820   \n",
       "21                   Botswana        52.5    43.60    8.30     51.3   13300   \n",
       "25               Burkina Faso       116.0    19.20    6.74     29.6    1430   \n",
       "26                    Burundi        93.6     8.92   11.60     39.2     764   \n",
       "27                   Cambodia        44.4    54.10    5.68     59.5    2520   \n",
       "28                   Cameroon       108.0    22.20    5.13     27.0    2660   \n",
       "31   Central African Republic       149.0    11.80    3.98     26.5     888   \n",
       "32                       Chad       150.0    36.80    4.53     43.5    1930   \n",
       "36                    Comoros        88.2    16.50    4.51     51.7    1410   \n",
       "37           Congo, Dem. Rep.       116.0    41.10    7.91     49.6     609   \n",
       "38                Congo, Rep.        63.9    85.10    2.46     54.7    5190   \n",
       "40              Cote d'Ivoire       111.0    50.60    5.30     43.3    2690   \n",
       "49          Equatorial Guinea       111.0    85.80    4.48     58.9   33700   \n",
       "56                     Gambia        80.3    23.80    5.69     42.7    1660   \n",
       "59                      Ghana        74.7    29.50    5.22     45.9    3060   \n",
       "63                     Guinea       109.0    30.30    4.93     43.2    1190   \n",
       "64              Guinea-Bissau       114.0    14.90    8.50     35.2    1390   \n",
       "65                     Guyana        37.6    51.40    5.38     79.1    5840   \n",
       "66                      Haiti       208.0    15.30    6.91     64.7    1500   \n",
       "72                       Iraq        36.9    39.40    8.41     34.1   12700   \n",
       "80                      Kenya        62.2    20.70    4.75     33.6    2480   \n",
       "81                   Kiribati        62.7    13.30   11.30     79.9    1730   \n",
       "83            Kyrgyz Republic        29.6    51.60    6.18     81.7    2790   \n",
       "84                        Lao        78.9    35.40    4.47     49.3    3980   \n",
       "87                    Lesotho        99.7    39.40   11.10    101.0    2380   \n",
       "88                    Liberia        89.3    19.10   11.80     92.6     700   \n",
       "93                 Madagascar        62.2    25.00    3.77     43.0    1390   \n",
       "94                     Malawi        90.5    22.80    6.59     34.9    1030   \n",
       "97                       Mali       137.0    22.80    4.98     35.1    1870   \n",
       "99                 Mauritania        97.4    50.70    4.41     61.2    3320   \n",
       "101     Micronesia, Fed. Sts.        40.0    23.50   14.20     81.0    3340   \n",
       "106                Mozambique       101.0    31.50    5.21     46.2     918   \n",
       "108                   Namibia        56.0    47.80    6.78     60.7    8460   \n",
       "112                     Niger       123.0    22.20    5.16     49.1     814   \n",
       "113                   Nigeria       130.0    25.30    5.07     17.4    5150   \n",
       "126                    Rwanda        63.6    12.00   10.50     30.0    1350   \n",
       "127                     Samoa        18.9    29.20    6.47     53.1    5400   \n",
       "129                   Senegal        66.8    24.90    5.66     40.3    2180   \n",
       "132              Sierra Leone       160.0    16.80   13.10     34.5    1220   \n",
       "136           Solomon Islands        28.1    49.30    8.55     81.2    1780   \n",
       "137              South Africa        53.7    28.60    8.94     27.4   12000   \n",
       "142                     Sudan        76.7    19.70    6.32     17.2    3370   \n",
       "146                Tajikistan        52.4    14.90    5.98     58.6    2110   \n",
       "147                  Tanzania        71.9    18.70    6.01     29.1    2090   \n",
       "149               Timor-Leste        62.6     2.20    9.12     27.8    1850   \n",
       "150                      Togo        90.3    40.20    7.65     57.3    1210   \n",
       "151                     Tonga        17.4    12.40    5.07     60.3    4980   \n",
       "155                    Uganda        81.0    17.10    9.01     28.6    1540   \n",
       "162                   Vanuatu        29.2    46.60    5.25     52.7    2950   \n",
       "165                     Yemen        56.3    30.00    5.18     34.4    4480   \n",
       "166                    Zambia        83.1    37.00    5.89     30.9    3280   \n",
       "\n",
       "     inflation  life_expec  total_fer   gdpp  cluster_id  cluster_labels  \\\n",
       "0        9.440        56.2       5.82    553           0               0   \n",
       "3       22.400        60.1       6.16   3530           0               0   \n",
       "17       0.885        61.8       5.36    758           0               0   \n",
       "21       8.920        57.1       2.88   6350           0               0   \n",
       "25       6.810        57.9       5.87    575           0               0   \n",
       "26      12.300        57.7       6.26    231           0               0   \n",
       "27       3.120        66.1       2.88    786           0               0   \n",
       "28       1.910        57.3       5.11   1310           0               0   \n",
       "31       2.010        47.5       5.21    446           0               0   \n",
       "32       6.390        56.5       6.59    897           0               0   \n",
       "36       3.870        65.9       4.75    769           0               0   \n",
       "37      20.800        57.5       6.54    334           0               0   \n",
       "38      20.700        60.4       4.95   2740           0               0   \n",
       "40       5.390        56.3       5.27   1220           0               0   \n",
       "49      24.900        60.9       5.21  17100           0               0   \n",
       "56       4.300        65.5       5.71    562           0               0   \n",
       "59      16.600        62.2       4.27   1310           0               0   \n",
       "63      16.100        58.0       5.34    648           0               0   \n",
       "64       2.970        55.6       5.05    547           0               0   \n",
       "65       5.730        65.5       2.65   3040           0               0   \n",
       "66       5.450        32.1       3.33    662           0               0   \n",
       "72      16.600        67.2       4.56   4500           0               0   \n",
       "80       2.090        62.8       4.37    967           0               0   \n",
       "81       1.520        60.7       3.84   1490           0               0   \n",
       "83      10.000        68.5       3.10    880           0               0   \n",
       "84       9.200        63.8       3.15   1140           0               0   \n",
       "87       4.150        46.5       3.30   1170           0               0   \n",
       "88       5.470        60.8       5.02    327           0               0   \n",
       "93       8.790        60.8       4.60    413           0               0   \n",
       "94      12.100        53.1       5.31    459           0               0   \n",
       "97       4.370        59.5       6.55    708           0               0   \n",
       "99      18.900        68.2       4.98   1200           0               0   \n",
       "101      3.800        65.4       3.46   2860           0               0   \n",
       "106      7.640        54.5       5.56    419           0               0   \n",
       "108      3.560        58.6       3.60   5190           0               0   \n",
       "112      2.550        58.8       7.49    348           0               0   \n",
       "113    104.000        60.5       5.84   2330           0               0   \n",
       "126      2.610        64.6       4.51    563           0               0   \n",
       "127      1.720        71.5       4.34   3450           0               0   \n",
       "129      1.850        64.0       5.06   1000           0               0   \n",
       "132     17.200        55.0       5.20    399           0               0   \n",
       "136      6.810        61.7       4.24   1290           0               0   \n",
       "137      6.350        54.3       2.59   7280           0               0   \n",
       "142     19.600        66.3       4.88   1480           0               0   \n",
       "146     12.500        69.6       3.51    738           0               0   \n",
       "147      9.250        59.3       5.43    702           0               0   \n",
       "149     26.500        71.1       6.23   3600           0               0   \n",
       "150      1.180        58.7       4.87    488           0               0   \n",
       "151      3.680        69.9       3.91   3550           0               0   \n",
       "155     10.600        56.8       6.15    595           0               0   \n",
       "162      2.620        63.0       3.50   2970           0               0   \n",
       "165     23.600        67.5       4.67   1310           0               0   \n",
       "166     14.000        52.0       5.40   1460           0               0   \n",
       "\n",
       "    Child Mortality Rating Health Rating Life Expectancy Rating GDPP Rating  \n",
       "0                      -10           -10                      0           0  \n",
       "3                      -20             0                    -10         -20  \n",
       "17                     -10             0                    -10          -5  \n",
       "21                       0           -20                     -5         -20  \n",
       "25                     -20           -10                     -5          -5  \n",
       "26                     -10           -20                     -5           0  \n",
       "27                       0            -5                    -20          -5  \n",
       "28                     -10            -5                     -5         -10  \n",
       "31                     -20             0                      0           0  \n",
       "32                     -20             0                      0          -5  \n",
       "36                     -10             0                    -20          -5  \n",
       "37                     -20           -10                     -5           0  \n",
       "38                      -5             0                    -10         -20  \n",
       "40                     -10            -5                      0         -10  \n",
       "49                     -10             0                    -10         -20  \n",
       "56                      -5            -5                    -20           0  \n",
       "59                      -5            -5                    -10         -10  \n",
       "63                     -10             0                     -5          -5  \n",
       "64                     -20           -20                      0           0  \n",
       "65                       0            -5                    -20         -20  \n",
       "66                     -20           -10                      0          -5  \n",
       "72                       0           -20                    -20         -20  \n",
       "80                      -5             0                    -10          -5  \n",
       "81                      -5           -20                    -10         -10  \n",
       "83                       0           -10                    -20          -5  \n",
       "84                      -5             0                    -10         -10  \n",
       "87                     -10           -20                      0         -10  \n",
       "88                     -10           -20                    -10           0  \n",
       "93                      -5             0                    -10           0  \n",
       "94                     -10           -10                      0           0  \n",
       "97                     -20             0                     -5          -5  \n",
       "99                     -10             0                    -20         -10  \n",
       "101                      0           -20                    -20         -20  \n",
       "106                    -10            -5                      0           0  \n",
       "108                      0           -10                     -5         -20  \n",
       "112                    -20            -5                     -5           0  \n",
       "113                    -20            -5                    -10         -10  \n",
       "126                     -5           -20                    -10           0  \n",
       "127                      0           -10                    -20         -20  \n",
       "129                     -5            -5                    -10         -10  \n",
       "132                    -20           -20                      0           0  \n",
       "136                      0           -20                    -10         -10  \n",
       "137                      0           -20                      0         -20  \n",
       "142                     -5           -10                    -20         -10  \n",
       "146                      0            -5                    -20          -5  \n",
       "147                     -5           -10                     -5          -5  \n",
       "149                     -5           -20                    -20         -20  \n",
       "150                    -10           -10                     -5           0  \n",
       "151                      0            -5                    -20         -20  \n",
       "155                     -5           -20                      0          -5  \n",
       "162                      0            -5                    -10         -20  \n",
       "165                      0            -5                    -20         -10  \n",
       "166                    -10            -5                      0         -10  "
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating categories for each column and assigning them scores from 0 to -20, where 0 is the best and -20 is the worst\n",
    "\n",
    "bins_1 = [17.0,56.3, 81.0, 111.0, 208.0]\n",
    "\n",
    "country_new['Child Mortality Rating'] = pd.cut(country_new['child_mort'], bins=bins_1, labels = [\"0\",\"-5\",\"-10\",\"-20\"])\n",
    "\n",
    "bins_2 = [1, 5, 6, 8, 15]\n",
    "\n",
    "country_new['Health Rating'] = pd.cut(country_new['health'], bins=bins_2, labels = [\"0\",\"-5\",\"-10\",\"-20\"])\n",
    "\n",
    "bins_3 = [31, 57, 60, 65, 72]\n",
    "\n",
    "country_new['Life Expectancy Rating'] = pd.cut(country_new['life_expec'], bins=bins_3, labels = [\"0\",\"-5\",\"-10\",\"-20\"])\n",
    "\n",
    "bins_4 = [230, 563, 967, 2330, 17200]\n",
    "\n",
    "country_new['GDPP Rating'] = pd.cut(country_new['gdpp'], bins=bins_4, labels = [\"0\",\"-5\",\"-10\",\"-20\"])\n",
    "country_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "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>Child Mortality Rating</th>\n",
       "      <th>Health Rating</th>\n",
       "      <th>Life Expectancy Rating</th>\n",
       "      <th>GDPP Rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>-10</td>\n",
       "      <td>-10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Angola</td>\n",
       "      <td>-20</td>\n",
       "      <td>0</td>\n",
       "      <td>-10</td>\n",
       "      <td>-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Benin</td>\n",
       "      <td>-10</td>\n",
       "      <td>0</td>\n",
       "      <td>-10</td>\n",
       "      <td>-5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Botswana</td>\n",
       "      <td>0</td>\n",
       "      <td>-20</td>\n",
       "      <td>-5</td>\n",
       "      <td>-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Burkina Faso</td>\n",
       "      <td>-20</td>\n",
       "      <td>-10</td>\n",
       "      <td>-5</td>\n",
       "      <td>-5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         country Child Mortality Rating Health Rating Life Expectancy Rating  \\\n",
       "0    Afghanistan                    -10           -10                      0   \n",
       "3         Angola                    -20             0                    -10   \n",
       "17         Benin                    -10             0                    -10   \n",
       "21      Botswana                      0           -20                     -5   \n",
       "25  Burkina Faso                    -20           -10                     -5   \n",
       "\n",
       "   GDPP Rating  \n",
       "0            0  \n",
       "3          -20  \n",
       "17          -5  \n",
       "21         -20  \n",
       "25          -5  "
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Keeping only the new columns and removing redundancy\n",
    "\n",
    "country_nex = country_new[['country', 'Child Mortality Rating', 'Health Rating', 'Life Expectancy Rating', 'GDPP Rating']]\n",
    "country_nex.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Neerja\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  after removing the cwd from sys.path.\n",
      "C:\\Users\\Neerja\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"\n",
      "C:\\Users\\Neerja\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n",
      "C:\\Users\\Neerja\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  import sys\n"
     ]
    }
   ],
   "source": [
    "# Converting the datatypeto float in irder to perform further analysis\n",
    "\n",
    "col_list = list(country_nex)\n",
    "col_list.remove('country')\n",
    "\n",
    "country_nex['Child Mortality Rating'] = country_nex['Child Mortality Rating'].astype(float)\n",
    "country_nex['Health Rating'] = country_nex['Health Rating'].astype(float)\n",
    "country_nex['Life Expectancy Rating'] = country_nex['Life Expectancy Rating'].astype(float)\n",
    "country_nex['GDPP Rating'] = country_nex['GDPP Rating'].astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Neerja\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "# Creating a new variable \"Total Score\" which is essentially the sum of the individual scores\n",
    "# This is basically a derived metric. The higher the \"Total Score\", the better off the country.\n",
    "# So, we need to pay attention to the high negative value countries.\n",
    "\n",
    "country_nex['Total Score'] = country_nex[col_list].sum(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "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>Child Mortality Rating</th>\n",
       "      <th>Health Rating</th>\n",
       "      <th>Life Expectancy Rating</th>\n",
       "      <th>GDPP Rating</th>\n",
       "      <th>Total Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Angola</td>\n",
       "      <td>-20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>-20.0</td>\n",
       "      <td>-50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Benin</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>-25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Botswana</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-20.0</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>-20.0</td>\n",
       "      <td>-45.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Burkina Faso</td>\n",
       "      <td>-20.0</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>-40.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         country  Child Mortality Rating  Health Rating  \\\n",
       "0    Afghanistan                   -10.0          -10.0   \n",
       "3         Angola                   -20.0            0.0   \n",
       "17         Benin                   -10.0            0.0   \n",
       "21      Botswana                     0.0          -20.0   \n",
       "25  Burkina Faso                   -20.0          -10.0   \n",
       "\n",
       "    Life Expectancy Rating  GDPP Rating  Total Score  \n",
       "0                      0.0          0.0        -20.0  \n",
       "3                    -10.0        -20.0        -50.0  \n",
       "17                   -10.0         -5.0        -25.0  \n",
       "21                    -5.0        -20.0        -45.0  \n",
       "25                    -5.0         -5.0        -40.0  "
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "country_nex.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "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>Child Mortality Rating</th>\n",
       "      <th>Health Rating</th>\n",
       "      <th>Life Expectancy Rating</th>\n",
       "      <th>GDPP Rating</th>\n",
       "      <th>Total Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>Timor-Leste</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>-20.0</td>\n",
       "      <td>-20.0</td>\n",
       "      <td>-20.0</td>\n",
       "      <td>-65.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>Micronesia, Fed. Sts.</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-20.0</td>\n",
       "      <td>-20.0</td>\n",
       "      <td>-20.0</td>\n",
       "      <td>-60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>Iraq</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-20.0</td>\n",
       "      <td>-20.0</td>\n",
       "      <td>-20.0</td>\n",
       "      <td>-60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Angola</td>\n",
       "      <td>-20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>-20.0</td>\n",
       "      <td>-50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>Samoa</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>-20.0</td>\n",
       "      <td>-20.0</td>\n",
       "      <td>-50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Botswana</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-20.0</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>-20.0</td>\n",
       "      <td>-45.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>Nigeria</td>\n",
       "      <td>-20.0</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>-45.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>Guyana</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>-20.0</td>\n",
       "      <td>-20.0</td>\n",
       "      <td>-45.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>Kiribati</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>-20.0</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>-45.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>Tonga</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>-20.0</td>\n",
       "      <td>-20.0</td>\n",
       "      <td>-45.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>Sudan</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>-20.0</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>-45.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>Lesotho</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>-20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>-40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>Guinea-Bissau</td>\n",
       "      <td>-20.0</td>\n",
       "      <td>-20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>Mauritania</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-20.0</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>-40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>Solomon Islands</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-20.0</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>-40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>Liberia</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>-20.0</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>South Africa</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-20.0</td>\n",
       "      <td>-40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>Sierra Leone</td>\n",
       "      <td>-20.0</td>\n",
       "      <td>-20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>Equatorial Guinea</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>-20.0</td>\n",
       "      <td>-40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Burkina Faso</td>\n",
       "      <td>-20.0</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>-40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>Haiti</td>\n",
       "      <td>-20.0</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>-35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162</th>\n",
       "      <td>Vanuatu</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>-20.0</td>\n",
       "      <td>-35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>Rwanda</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>-20.0</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Congo, Rep.</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>-20.0</td>\n",
       "      <td>-35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>Kyrgyz Republic</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>-20.0</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>-35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>Yemen</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>-20.0</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>-35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Congo, Dem. Rep.</td>\n",
       "      <td>-20.0</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Comoros</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-20.0</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>-35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>Namibia</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>-20.0</td>\n",
       "      <td>-35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Burundi</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>-20.0</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>Niger</td>\n",
       "      <td>-20.0</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Cambodia</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>-20.0</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>-30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>Gambia</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>-20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>Mali</td>\n",
       "      <td>-20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>-30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>Ghana</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>-30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>Tajikistan</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>-20.0</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>-30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>Senegal</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>-30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Cameroon</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>-30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>Uganda</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>-20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>-30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>Tanzania</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>-25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>Togo</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>-25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>Lao</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>-25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>Cote d'Ivoire</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>-25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Chad</td>\n",
       "      <td>-20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>-25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Benin</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>-25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>Malawi</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>Kenya</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>-20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>Guinea</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>-20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Central African Republic</td>\n",
       "      <td>-20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>Madagascar</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>Mozambique</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-15.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      country  Child Mortality Rating  Health Rating  \\\n",
       "149               Timor-Leste                    -5.0          -20.0   \n",
       "101     Micronesia, Fed. Sts.                     0.0          -20.0   \n",
       "72                       Iraq                     0.0          -20.0   \n",
       "3                      Angola                   -20.0            0.0   \n",
       "127                     Samoa                     0.0          -10.0   \n",
       "21                   Botswana                     0.0          -20.0   \n",
       "113                   Nigeria                   -20.0           -5.0   \n",
       "65                     Guyana                     0.0           -5.0   \n",
       "81                   Kiribati                    -5.0          -20.0   \n",
       "151                     Tonga                     0.0           -5.0   \n",
       "142                     Sudan                    -5.0          -10.0   \n",
       "87                    Lesotho                   -10.0          -20.0   \n",
       "64              Guinea-Bissau                   -20.0          -20.0   \n",
       "99                 Mauritania                   -10.0            0.0   \n",
       "136           Solomon Islands                     0.0          -20.0   \n",
       "88                    Liberia                   -10.0          -20.0   \n",
       "137              South Africa                     0.0          -20.0   \n",
       "132              Sierra Leone                   -20.0          -20.0   \n",
       "49          Equatorial Guinea                   -10.0            0.0   \n",
       "25               Burkina Faso                   -20.0          -10.0   \n",
       "66                      Haiti                   -20.0          -10.0   \n",
       "162                   Vanuatu                     0.0           -5.0   \n",
       "126                    Rwanda                    -5.0          -20.0   \n",
       "38                Congo, Rep.                    -5.0            0.0   \n",
       "83            Kyrgyz Republic                     0.0          -10.0   \n",
       "165                     Yemen                     0.0           -5.0   \n",
       "37           Congo, Dem. Rep.                   -20.0          -10.0   \n",
       "36                    Comoros                   -10.0            0.0   \n",
       "108                   Namibia                     0.0          -10.0   \n",
       "26                    Burundi                   -10.0          -20.0   \n",
       "112                     Niger                   -20.0           -5.0   \n",
       "27                   Cambodia                     0.0           -5.0   \n",
       "56                     Gambia                    -5.0           -5.0   \n",
       "97                       Mali                   -20.0            0.0   \n",
       "59                      Ghana                    -5.0           -5.0   \n",
       "146                Tajikistan                     0.0           -5.0   \n",
       "129                   Senegal                    -5.0           -5.0   \n",
       "28                   Cameroon                   -10.0           -5.0   \n",
       "155                    Uganda                    -5.0          -20.0   \n",
       "147                  Tanzania                    -5.0          -10.0   \n",
       "150                      Togo                   -10.0          -10.0   \n",
       "166                    Zambia                   -10.0           -5.0   \n",
       "84                        Lao                    -5.0            0.0   \n",
       "40              Cote d'Ivoire                   -10.0           -5.0   \n",
       "32                       Chad                   -20.0            0.0   \n",
       "17                      Benin                   -10.0            0.0   \n",
       "94                     Malawi                   -10.0          -10.0   \n",
       "80                      Kenya                    -5.0            0.0   \n",
       "63                     Guinea                   -10.0            0.0   \n",
       "31   Central African Republic                   -20.0            0.0   \n",
       "0                 Afghanistan                   -10.0          -10.0   \n",
       "93                 Madagascar                    -5.0            0.0   \n",
       "106                Mozambique                   -10.0           -5.0   \n",
       "\n",
       "     Life Expectancy Rating  GDPP Rating  Total Score  \n",
       "149                   -20.0        -20.0        -65.0  \n",
       "101                   -20.0        -20.0        -60.0  \n",
       "72                    -20.0        -20.0        -60.0  \n",
       "3                     -10.0        -20.0        -50.0  \n",
       "127                   -20.0        -20.0        -50.0  \n",
       "21                     -5.0        -20.0        -45.0  \n",
       "113                   -10.0        -10.0        -45.0  \n",
       "65                    -20.0        -20.0        -45.0  \n",
       "81                    -10.0        -10.0        -45.0  \n",
       "151                   -20.0        -20.0        -45.0  \n",
       "142                   -20.0        -10.0        -45.0  \n",
       "87                      0.0        -10.0        -40.0  \n",
       "64                      0.0          0.0        -40.0  \n",
       "99                    -20.0        -10.0        -40.0  \n",
       "136                   -10.0        -10.0        -40.0  \n",
       "88                    -10.0          0.0        -40.0  \n",
       "137                     0.0        -20.0        -40.0  \n",
       "132                     0.0          0.0        -40.0  \n",
       "49                    -10.0        -20.0        -40.0  \n",
       "25                     -5.0         -5.0        -40.0  \n",
       "66                      0.0         -5.0        -35.0  \n",
       "162                   -10.0        -20.0        -35.0  \n",
       "126                   -10.0          0.0        -35.0  \n",
       "38                    -10.0        -20.0        -35.0  \n",
       "83                    -20.0         -5.0        -35.0  \n",
       "165                   -20.0        -10.0        -35.0  \n",
       "37                     -5.0          0.0        -35.0  \n",
       "36                    -20.0         -5.0        -35.0  \n",
       "108                    -5.0        -20.0        -35.0  \n",
       "26                     -5.0          0.0        -35.0  \n",
       "112                    -5.0          0.0        -30.0  \n",
       "27                    -20.0         -5.0        -30.0  \n",
       "56                    -20.0          0.0        -30.0  \n",
       "97                     -5.0         -5.0        -30.0  \n",
       "59                    -10.0        -10.0        -30.0  \n",
       "146                   -20.0         -5.0        -30.0  \n",
       "129                   -10.0        -10.0        -30.0  \n",
       "28                     -5.0        -10.0        -30.0  \n",
       "155                     0.0         -5.0        -30.0  \n",
       "147                    -5.0         -5.0        -25.0  \n",
       "150                    -5.0          0.0        -25.0  \n",
       "166                     0.0        -10.0        -25.0  \n",
       "84                    -10.0        -10.0        -25.0  \n",
       "40                      0.0        -10.0        -25.0  \n",
       "32                      0.0         -5.0        -25.0  \n",
       "17                    -10.0         -5.0        -25.0  \n",
       "94                      0.0          0.0        -20.0  \n",
       "80                    -10.0         -5.0        -20.0  \n",
       "63                     -5.0         -5.0        -20.0  \n",
       "31                      0.0          0.0        -20.0  \n",
       "0                       0.0          0.0        -20.0  \n",
       "93                    -10.0          0.0        -15.0  \n",
       "106                     0.0          0.0        -15.0  "
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sorting the countries according to descending values of Total Score to find the countries requiring\n",
    "# help desperately.\n",
    "\n",
    "# From the analysis, the top 5 countries that are in dire need of help are: Timor-Leste, Micronesia, Iraq, Angola and Samoa\n",
    "\n",
    "country_nex = country_nex.sort_values(by=['Total Score'], ascending=True)\n",
    "country_nex"
   ]
  }
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