Ejercicio_Final_Python.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "año_2015 = pd.read_csv('2015.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "año_2016 = pd.read_csv('2016.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method NDFrame.head of          Country                           Region  Happiness Rank  \\\n",
       "0    Switzerland                   Western Europe               1   \n",
       "1        Iceland                   Western Europe               2   \n",
       "2        Denmark                   Western Europe               3   \n",
       "3         Norway                   Western Europe               4   \n",
       "4         Canada                    North America               5   \n",
       "..           ...                              ...             ...   \n",
       "153       Rwanda               Sub-Saharan Africa             154   \n",
       "154        Benin               Sub-Saharan Africa             155   \n",
       "155        Syria  Middle East and Northern Africa             156   \n",
       "156      Burundi               Sub-Saharan Africa             157   \n",
       "157         Togo               Sub-Saharan Africa             158   \n",
       "\n",
       "     Happiness Score  Standard Error  Economy (GDP per Capita)   Family  \\\n",
       "0              7.587         0.03411                   1.39651  1.34951   \n",
       "1              7.561         0.04884                   1.30232  1.40223   \n",
       "2              7.527         0.03328                   1.32548  1.36058   \n",
       "3              7.522         0.03880                   1.45900  1.33095   \n",
       "4              7.427         0.03553                   1.32629  1.32261   \n",
       "..               ...             ...                       ...      ...   \n",
       "153            3.465         0.03464                   0.22208  0.77370   \n",
       "154            3.340         0.03656                   0.28665  0.35386   \n",
       "155            3.006         0.05015                   0.66320  0.47489   \n",
       "156            2.905         0.08658                   0.01530  0.41587   \n",
       "157            2.839         0.06727                   0.20868  0.13995   \n",
       "\n",
       "     Health (Life Expectancy)  Freedom  Trust (Government Corruption)  \\\n",
       "0                     0.94143  0.66557                        0.41978   \n",
       "1                     0.94784  0.62877                        0.14145   \n",
       "2                     0.87464  0.64938                        0.48357   \n",
       "3                     0.88521  0.66973                        0.36503   \n",
       "4                     0.90563  0.63297                        0.32957   \n",
       "..                        ...      ...                            ...   \n",
       "153                   0.42864  0.59201                        0.55191   \n",
       "154                   0.31910  0.48450                        0.08010   \n",
       "155                   0.72193  0.15684                        0.18906   \n",
       "156                   0.22396  0.11850                        0.10062   \n",
       "157                   0.28443  0.36453                        0.10731   \n",
       "\n",
       "     Generosity  Dystopia Residual  \n",
       "0       0.29678            2.51738  \n",
       "1       0.43630            2.70201  \n",
       "2       0.34139            2.49204  \n",
       "3       0.34699            2.46531  \n",
       "4       0.45811            2.45176  \n",
       "..          ...                ...  \n",
       "153     0.22628            0.67042  \n",
       "154     0.18260            1.63328  \n",
       "155     0.47179            0.32858  \n",
       "156     0.19727            1.83302  \n",
       "157     0.16681            1.56726  \n",
       "\n",
       "[158 rows x 12 columns]>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "año_2015.head"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method NDFrame.head of          Country                           Region  Happiness Rank  \\\n",
       "0        Denmark                   Western Europe               1   \n",
       "1    Switzerland                   Western Europe               2   \n",
       "2        Iceland                   Western Europe               3   \n",
       "3         Norway                   Western Europe               4   \n",
       "4        Finland                   Western Europe               5   \n",
       "..           ...                              ...             ...   \n",
       "152        Benin               Sub-Saharan Africa             153   \n",
       "153  Afghanistan                    Southern Asia             154   \n",
       "154         Togo               Sub-Saharan Africa             155   \n",
       "155        Syria  Middle East and Northern Africa             156   \n",
       "156      Burundi               Sub-Saharan Africa             157   \n",
       "\n",
       "     Happiness Score  Lower Confidence Interval  Upper Confidence Interval  \\\n",
       "0              7.526                      7.460                      7.592   \n",
       "1              7.509                      7.428                      7.590   \n",
       "2              7.501                      7.333                      7.669   \n",
       "3              7.498                      7.421                      7.575   \n",
       "4              7.413                      7.351                      7.475   \n",
       "..               ...                        ...                        ...   \n",
       "152            3.484                      3.404                      3.564   \n",
       "153            3.360                      3.288                      3.432   \n",
       "154            3.303                      3.192                      3.414   \n",
       "155            3.069                      2.936                      3.202   \n",
       "156            2.905                      2.732                      3.078   \n",
       "\n",
       "     Economy (GDP per Capita)   Family  Health (Life Expectancy)  Freedom  \\\n",
       "0                     1.44178  1.16374                   0.79504  0.57941   \n",
       "1                     1.52733  1.14524                   0.86303  0.58557   \n",
       "2                     1.42666  1.18326                   0.86733  0.56624   \n",
       "3                     1.57744  1.12690                   0.79579  0.59609   \n",
       "4                     1.40598  1.13464                   0.81091  0.57104   \n",
       "..                        ...      ...                       ...      ...   \n",
       "152                   0.39499  0.10419                   0.21028  0.39747   \n",
       "153                   0.38227  0.11037                   0.17344  0.16430   \n",
       "154                   0.28123  0.00000                   0.24811  0.34678   \n",
       "155                   0.74719  0.14866                   0.62994  0.06912   \n",
       "156                   0.06831  0.23442                   0.15747  0.04320   \n",
       "\n",
       "     Trust (Government Corruption)  Generosity  Dystopia Residual  \n",
       "0                          0.44453     0.36171            2.73939  \n",
       "1                          0.41203     0.28083            2.69463  \n",
       "2                          0.14975     0.47678            2.83137  \n",
       "3                          0.35776     0.37895            2.66465  \n",
       "4                          0.41004     0.25492            2.82596  \n",
       "..                             ...         ...                ...  \n",
       "152                        0.06681     0.20180            2.10812  \n",
       "153                        0.07112     0.31268            2.14558  \n",
       "154                        0.11587     0.17517            2.13540  \n",
       "155                        0.17233     0.48397            0.81789  \n",
       "156                        0.09419     0.20290            2.10404  \n",
       "\n",
       "[157 rows x 13 columns]>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "año_2016.head"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(158, 12)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "año_2015.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Country', 'Region', 'Happiness Rank', 'Happiness Score',\n",
       "       'Standard Error', 'Economy (GDP per Capita)', 'Family',\n",
       "       'Health (Life Expectancy)', 'Freedom', 'Trust (Government Corruption)',\n",
       "       'Generosity', 'Dystopia Residual'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "año_2015.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(157, 13)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "año_2016.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Country', 'Region', 'Happiness Rank', 'Happiness Score',\n",
       "       'Lower Confidence Interval', 'Upper Confidence Interval',\n",
       "       'Economy (GDP per Capita)', 'Family', 'Health (Life Expectancy)',\n",
       "       'Freedom', 'Trust (Government Corruption)', 'Generosity',\n",
       "       'Dystopia Residual'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "año_2016.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.concat([año_2015, año_2016], ignore_index=True, join='outer')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method NDFrame.head of          Country                           Region  Happiness Rank  \\\n",
       "0    Switzerland                   Western Europe               1   \n",
       "1        Iceland                   Western Europe               2   \n",
       "2        Denmark                   Western Europe               3   \n",
       "3         Norway                   Western Europe               4   \n",
       "4         Canada                    North America               5   \n",
       "..           ...                              ...             ...   \n",
       "310        Benin               Sub-Saharan Africa             153   \n",
       "311  Afghanistan                    Southern Asia             154   \n",
       "312         Togo               Sub-Saharan Africa             155   \n",
       "313        Syria  Middle East and Northern Africa             156   \n",
       "314      Burundi               Sub-Saharan Africa             157   \n",
       "\n",
       "     Happiness Score  Standard Error  Economy (GDP per Capita)   Family  \\\n",
       "0              7.587         0.03411                   1.39651  1.34951   \n",
       "1              7.561         0.04884                   1.30232  1.40223   \n",
       "2              7.527         0.03328                   1.32548  1.36058   \n",
       "3              7.522         0.03880                   1.45900  1.33095   \n",
       "4              7.427         0.03553                   1.32629  1.32261   \n",
       "..               ...             ...                       ...      ...   \n",
       "310            3.484             NaN                   0.39499  0.10419   \n",
       "311            3.360             NaN                   0.38227  0.11037   \n",
       "312            3.303             NaN                   0.28123  0.00000   \n",
       "313            3.069             NaN                   0.74719  0.14866   \n",
       "314            2.905             NaN                   0.06831  0.23442   \n",
       "\n",
       "     Health (Life Expectancy)  Freedom  Trust (Government Corruption)  \\\n",
       "0                     0.94143  0.66557                        0.41978   \n",
       "1                     0.94784  0.62877                        0.14145   \n",
       "2                     0.87464  0.64938                        0.48357   \n",
       "3                     0.88521  0.66973                        0.36503   \n",
       "4                     0.90563  0.63297                        0.32957   \n",
       "..                        ...      ...                            ...   \n",
       "310                   0.21028  0.39747                        0.06681   \n",
       "311                   0.17344  0.16430                        0.07112   \n",
       "312                   0.24811  0.34678                        0.11587   \n",
       "313                   0.62994  0.06912                        0.17233   \n",
       "314                   0.15747  0.04320                        0.09419   \n",
       "\n",
       "     Generosity  Dystopia Residual  Lower Confidence Interval  \\\n",
       "0       0.29678            2.51738                        NaN   \n",
       "1       0.43630            2.70201                        NaN   \n",
       "2       0.34139            2.49204                        NaN   \n",
       "3       0.34699            2.46531                        NaN   \n",
       "4       0.45811            2.45176                        NaN   \n",
       "..          ...                ...                        ...   \n",
       "310     0.20180            2.10812                      3.404   \n",
       "311     0.31268            2.14558                      3.288   \n",
       "312     0.17517            2.13540                      3.192   \n",
       "313     0.48397            0.81789                      2.936   \n",
       "314     0.20290            2.10404                      2.732   \n",
       "\n",
       "     Upper Confidence Interval  \n",
       "0                          NaN  \n",
       "1                          NaN  \n",
       "2                          NaN  \n",
       "3                          NaN  \n",
       "4                          NaN  \n",
       "..                         ...  \n",
       "310                      3.564  \n",
       "311                      3.432  \n",
       "312                      3.414  \n",
       "313                      3.202  \n",
       "314                      3.078  \n",
       "\n",
       "[315 rows x 14 columns]>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(315, 14)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def show_nulls(df):\n",
    "    total_nan = df.isnull().sum()\n",
    "    perc_nan = total_nan/len(df)*100\n",
    "    \n",
    "    return total_nan, perc_nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total nulos  Country                          0\n",
      "Region                           0\n",
      "Happiness Rank                   0\n",
      "Happiness Score                  0\n",
      "Economy (GDP per Capita)         0\n",
      "Family                           0\n",
      "Health (Life Expectancy)         0\n",
      "Freedom                          0\n",
      "Trust (Government Corruption)    0\n",
      "Generosity                       0\n",
      "Dystopia Residual                0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print('Total nulos ', show_nulls(df=df)[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Porcentaje de nulos  Country                           0.00000\n",
      "Region                            0.00000\n",
      "Happiness Rank                    0.00000\n",
      "Happiness Score                   0.00000\n",
      "Standard Error                   49.84127\n",
      "Economy (GDP per Capita)          0.00000\n",
      "Family                            0.00000\n",
      "Health (Life Expectancy)          0.00000\n",
      "Freedom                           0.00000\n",
      "Trust (Government Corruption)     0.00000\n",
      "Generosity                        0.00000\n",
      "Dystopia Residual                 0.00000\n",
      "Lower Confidence Interval        50.15873\n",
      "Upper Confidence Interval        50.15873\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print('Porcentaje de nulos ', show_nulls(df=df)[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Lower Confidence Interval'] = df['Lower Confidence Interval'].fillna(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Region</th>\n",
       "      <th>Happiness Rank</th>\n",
       "      <th>Happiness Score</th>\n",
       "      <th>Standard Error</th>\n",
       "      <th>Economy (GDP per Capita)</th>\n",
       "      <th>Family</th>\n",
       "      <th>Health (Life Expectancy)</th>\n",
       "      <th>Freedom</th>\n",
       "      <th>Trust (Government Corruption)</th>\n",
       "      <th>Generosity</th>\n",
       "      <th>Dystopia Residual</th>\n",
       "      <th>Lower Confidence Interval</th>\n",
       "      <th>Upper Confidence Interval</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Switzerland</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>1</td>\n",
       "      <td>7.587</td>\n",
       "      <td>0.03411</td>\n",
       "      <td>1.39651</td>\n",
       "      <td>1.34951</td>\n",
       "      <td>0.94143</td>\n",
       "      <td>0.66557</td>\n",
       "      <td>0.41978</td>\n",
       "      <td>0.29678</td>\n",
       "      <td>2.51738</td>\n",
       "      <td>3.000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Iceland</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>2</td>\n",
       "      <td>7.561</td>\n",
       "      <td>0.04884</td>\n",
       "      <td>1.30232</td>\n",
       "      <td>1.40223</td>\n",
       "      <td>0.94784</td>\n",
       "      <td>0.62877</td>\n",
       "      <td>0.14145</td>\n",
       "      <td>0.43630</td>\n",
       "      <td>2.70201</td>\n",
       "      <td>3.000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Denmark</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>3</td>\n",
       "      <td>7.527</td>\n",
       "      <td>0.03328</td>\n",
       "      <td>1.32548</td>\n",
       "      <td>1.36058</td>\n",
       "      <td>0.87464</td>\n",
       "      <td>0.64938</td>\n",
       "      <td>0.48357</td>\n",
       "      <td>0.34139</td>\n",
       "      <td>2.49204</td>\n",
       "      <td>3.000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Norway</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>4</td>\n",
       "      <td>7.522</td>\n",
       "      <td>0.03880</td>\n",
       "      <td>1.45900</td>\n",
       "      <td>1.33095</td>\n",
       "      <td>0.88521</td>\n",
       "      <td>0.66973</td>\n",
       "      <td>0.36503</td>\n",
       "      <td>0.34699</td>\n",
       "      <td>2.46531</td>\n",
       "      <td>3.000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Canada</td>\n",
       "      <td>North America</td>\n",
       "      <td>5</td>\n",
       "      <td>7.427</td>\n",
       "      <td>0.03553</td>\n",
       "      <td>1.32629</td>\n",
       "      <td>1.32261</td>\n",
       "      <td>0.90563</td>\n",
       "      <td>0.63297</td>\n",
       "      <td>0.32957</td>\n",
       "      <td>0.45811</td>\n",
       "      <td>2.45176</td>\n",
       "      <td>3.000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>310</th>\n",
       "      <td>Benin</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>153</td>\n",
       "      <td>3.484</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.39499</td>\n",
       "      <td>0.10419</td>\n",
       "      <td>0.21028</td>\n",
       "      <td>0.39747</td>\n",
       "      <td>0.06681</td>\n",
       "      <td>0.20180</td>\n",
       "      <td>2.10812</td>\n",
       "      <td>3.404</td>\n",
       "      <td>3.564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>311</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Southern Asia</td>\n",
       "      <td>154</td>\n",
       "      <td>3.360</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.38227</td>\n",
       "      <td>0.11037</td>\n",
       "      <td>0.17344</td>\n",
       "      <td>0.16430</td>\n",
       "      <td>0.07112</td>\n",
       "      <td>0.31268</td>\n",
       "      <td>2.14558</td>\n",
       "      <td>3.288</td>\n",
       "      <td>3.432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>312</th>\n",
       "      <td>Togo</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>155</td>\n",
       "      <td>3.303</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.28123</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.24811</td>\n",
       "      <td>0.34678</td>\n",
       "      <td>0.11587</td>\n",
       "      <td>0.17517</td>\n",
       "      <td>2.13540</td>\n",
       "      <td>3.192</td>\n",
       "      <td>3.414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>313</th>\n",
       "      <td>Syria</td>\n",
       "      <td>Middle East and Northern Africa</td>\n",
       "      <td>156</td>\n",
       "      <td>3.069</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.74719</td>\n",
       "      <td>0.14866</td>\n",
       "      <td>0.62994</td>\n",
       "      <td>0.06912</td>\n",
       "      <td>0.17233</td>\n",
       "      <td>0.48397</td>\n",
       "      <td>0.81789</td>\n",
       "      <td>2.936</td>\n",
       "      <td>3.202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>314</th>\n",
       "      <td>Burundi</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>157</td>\n",
       "      <td>2.905</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.06831</td>\n",
       "      <td>0.23442</td>\n",
       "      <td>0.15747</td>\n",
       "      <td>0.04320</td>\n",
       "      <td>0.09419</td>\n",
       "      <td>0.20290</td>\n",
       "      <td>2.10404</td>\n",
       "      <td>2.732</td>\n",
       "      <td>3.078</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>315 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Country                           Region  Happiness Rank  \\\n",
       "0    Switzerland                   Western Europe               1   \n",
       "1        Iceland                   Western Europe               2   \n",
       "2        Denmark                   Western Europe               3   \n",
       "3         Norway                   Western Europe               4   \n",
       "4         Canada                    North America               5   \n",
       "..           ...                              ...             ...   \n",
       "310        Benin               Sub-Saharan Africa             153   \n",
       "311  Afghanistan                    Southern Asia             154   \n",
       "312         Togo               Sub-Saharan Africa             155   \n",
       "313        Syria  Middle East and Northern Africa             156   \n",
       "314      Burundi               Sub-Saharan Africa             157   \n",
       "\n",
       "     Happiness Score  Standard Error  Economy (GDP per Capita)   Family  \\\n",
       "0              7.587         0.03411                   1.39651  1.34951   \n",
       "1              7.561         0.04884                   1.30232  1.40223   \n",
       "2              7.527         0.03328                   1.32548  1.36058   \n",
       "3              7.522         0.03880                   1.45900  1.33095   \n",
       "4              7.427         0.03553                   1.32629  1.32261   \n",
       "..               ...             ...                       ...      ...   \n",
       "310            3.484             NaN                   0.39499  0.10419   \n",
       "311            3.360             NaN                   0.38227  0.11037   \n",
       "312            3.303             NaN                   0.28123  0.00000   \n",
       "313            3.069             NaN                   0.74719  0.14866   \n",
       "314            2.905             NaN                   0.06831  0.23442   \n",
       "\n",
       "     Health (Life Expectancy)  Freedom  Trust (Government Corruption)  \\\n",
       "0                     0.94143  0.66557                        0.41978   \n",
       "1                     0.94784  0.62877                        0.14145   \n",
       "2                     0.87464  0.64938                        0.48357   \n",
       "3                     0.88521  0.66973                        0.36503   \n",
       "4                     0.90563  0.63297                        0.32957   \n",
       "..                        ...      ...                            ...   \n",
       "310                   0.21028  0.39747                        0.06681   \n",
       "311                   0.17344  0.16430                        0.07112   \n",
       "312                   0.24811  0.34678                        0.11587   \n",
       "313                   0.62994  0.06912                        0.17233   \n",
       "314                   0.15747  0.04320                        0.09419   \n",
       "\n",
       "     Generosity  Dystopia Residual  Lower Confidence Interval  \\\n",
       "0       0.29678            2.51738                      3.000   \n",
       "1       0.43630            2.70201                      3.000   \n",
       "2       0.34139            2.49204                      3.000   \n",
       "3       0.34699            2.46531                      3.000   \n",
       "4       0.45811            2.45176                      3.000   \n",
       "..          ...                ...                        ...   \n",
       "310     0.20180            2.10812                      3.404   \n",
       "311     0.31268            2.14558                      3.288   \n",
       "312     0.17517            2.13540                      3.192   \n",
       "313     0.48397            0.81789                      2.936   \n",
       "314     0.20290            2.10404                      2.732   \n",
       "\n",
       "     Upper Confidence Interval  \n",
       "0                          NaN  \n",
       "1                          NaN  \n",
       "2                          NaN  \n",
       "3                          NaN  \n",
       "4                          NaN  \n",
       "..                         ...  \n",
       "310                      3.564  \n",
       "311                      3.432  \n",
       "312                      3.414  \n",
       "313                      3.202  \n",
       "314                      3.078  \n",
       "\n",
       "[315 rows x 14 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Upper Confidence Interval'] = df['Upper Confidence Interval'].fillna(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>Region</th>\n",
       "      <th>Happiness Rank</th>\n",
       "      <th>Happiness Score</th>\n",
       "      <th>Standard Error</th>\n",
       "      <th>Economy (GDP per Capita)</th>\n",
       "      <th>Family</th>\n",
       "      <th>Health (Life Expectancy)</th>\n",
       "      <th>Freedom</th>\n",
       "      <th>Trust (Government Corruption)</th>\n",
       "      <th>Generosity</th>\n",
       "      <th>Dystopia Residual</th>\n",
       "      <th>Lower Confidence Interval</th>\n",
       "      <th>Upper Confidence Interval</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Switzerland</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>1</td>\n",
       "      <td>7.587</td>\n",
       "      <td>0.03411</td>\n",
       "      <td>1.39651</td>\n",
       "      <td>1.34951</td>\n",
       "      <td>0.94143</td>\n",
       "      <td>0.66557</td>\n",
       "      <td>0.41978</td>\n",
       "      <td>0.29678</td>\n",
       "      <td>2.51738</td>\n",
       "      <td>3.000</td>\n",
       "      <td>3.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Iceland</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>2</td>\n",
       "      <td>7.561</td>\n",
       "      <td>0.04884</td>\n",
       "      <td>1.30232</td>\n",
       "      <td>1.40223</td>\n",
       "      <td>0.94784</td>\n",
       "      <td>0.62877</td>\n",
       "      <td>0.14145</td>\n",
       "      <td>0.43630</td>\n",
       "      <td>2.70201</td>\n",
       "      <td>3.000</td>\n",
       "      <td>3.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Denmark</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>3</td>\n",
       "      <td>7.527</td>\n",
       "      <td>0.03328</td>\n",
       "      <td>1.32548</td>\n",
       "      <td>1.36058</td>\n",
       "      <td>0.87464</td>\n",
       "      <td>0.64938</td>\n",
       "      <td>0.48357</td>\n",
       "      <td>0.34139</td>\n",
       "      <td>2.49204</td>\n",
       "      <td>3.000</td>\n",
       "      <td>3.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Norway</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>4</td>\n",
       "      <td>7.522</td>\n",
       "      <td>0.03880</td>\n",
       "      <td>1.45900</td>\n",
       "      <td>1.33095</td>\n",
       "      <td>0.88521</td>\n",
       "      <td>0.66973</td>\n",
       "      <td>0.36503</td>\n",
       "      <td>0.34699</td>\n",
       "      <td>2.46531</td>\n",
       "      <td>3.000</td>\n",
       "      <td>3.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Canada</td>\n",
       "      <td>North America</td>\n",
       "      <td>5</td>\n",
       "      <td>7.427</td>\n",
       "      <td>0.03553</td>\n",
       "      <td>1.32629</td>\n",
       "      <td>1.32261</td>\n",
       "      <td>0.90563</td>\n",
       "      <td>0.63297</td>\n",
       "      <td>0.32957</td>\n",
       "      <td>0.45811</td>\n",
       "      <td>2.45176</td>\n",
       "      <td>3.000</td>\n",
       "      <td>3.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>310</th>\n",
       "      <td>Benin</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>153</td>\n",
       "      <td>3.484</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.39499</td>\n",
       "      <td>0.10419</td>\n",
       "      <td>0.21028</td>\n",
       "      <td>0.39747</td>\n",
       "      <td>0.06681</td>\n",
       "      <td>0.20180</td>\n",
       "      <td>2.10812</td>\n",
       "      <td>3.404</td>\n",
       "      <td>3.564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>311</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Southern Asia</td>\n",
       "      <td>154</td>\n",
       "      <td>3.360</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.38227</td>\n",
       "      <td>0.11037</td>\n",
       "      <td>0.17344</td>\n",
       "      <td>0.16430</td>\n",
       "      <td>0.07112</td>\n",
       "      <td>0.31268</td>\n",
       "      <td>2.14558</td>\n",
       "      <td>3.288</td>\n",
       "      <td>3.432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>312</th>\n",
       "      <td>Togo</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>155</td>\n",
       "      <td>3.303</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.28123</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.24811</td>\n",
       "      <td>0.34678</td>\n",
       "      <td>0.11587</td>\n",
       "      <td>0.17517</td>\n",
       "      <td>2.13540</td>\n",
       "      <td>3.192</td>\n",
       "      <td>3.414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>313</th>\n",
       "      <td>Syria</td>\n",
       "      <td>Middle East and Northern Africa</td>\n",
       "      <td>156</td>\n",
       "      <td>3.069</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.74719</td>\n",
       "      <td>0.14866</td>\n",
       "      <td>0.62994</td>\n",
       "      <td>0.06912</td>\n",
       "      <td>0.17233</td>\n",
       "      <td>0.48397</td>\n",
       "      <td>0.81789</td>\n",
       "      <td>2.936</td>\n",
       "      <td>3.202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>314</th>\n",
       "      <td>Burundi</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>157</td>\n",
       "      <td>2.905</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.06831</td>\n",
       "      <td>0.23442</td>\n",
       "      <td>0.15747</td>\n",
       "      <td>0.04320</td>\n",
       "      <td>0.09419</td>\n",
       "      <td>0.20290</td>\n",
       "      <td>2.10404</td>\n",
       "      <td>2.732</td>\n",
       "      <td>3.078</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>315 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Country                           Region  Happiness Rank  \\\n",
       "0    Switzerland                   Western Europe               1   \n",
       "1        Iceland                   Western Europe               2   \n",
       "2        Denmark                   Western Europe               3   \n",
       "3         Norway                   Western Europe               4   \n",
       "4         Canada                    North America               5   \n",
       "..           ...                              ...             ...   \n",
       "310        Benin               Sub-Saharan Africa             153   \n",
       "311  Afghanistan                    Southern Asia             154   \n",
       "312         Togo               Sub-Saharan Africa             155   \n",
       "313        Syria  Middle East and Northern Africa             156   \n",
       "314      Burundi               Sub-Saharan Africa             157   \n",
       "\n",
       "     Happiness Score  Standard Error  Economy (GDP per Capita)   Family  \\\n",
       "0              7.587         0.03411                   1.39651  1.34951   \n",
       "1              7.561         0.04884                   1.30232  1.40223   \n",
       "2              7.527         0.03328                   1.32548  1.36058   \n",
       "3              7.522         0.03880                   1.45900  1.33095   \n",
       "4              7.427         0.03553                   1.32629  1.32261   \n",
       "..               ...             ...                       ...      ...   \n",
       "310            3.484             NaN                   0.39499  0.10419   \n",
       "311            3.360             NaN                   0.38227  0.11037   \n",
       "312            3.303             NaN                   0.28123  0.00000   \n",
       "313            3.069             NaN                   0.74719  0.14866   \n",
       "314            2.905             NaN                   0.06831  0.23442   \n",
       "\n",
       "     Health (Life Expectancy)  Freedom  Trust (Government Corruption)  \\\n",
       "0                     0.94143  0.66557                        0.41978   \n",
       "1                     0.94784  0.62877                        0.14145   \n",
       "2                     0.87464  0.64938                        0.48357   \n",
       "3                     0.88521  0.66973                        0.36503   \n",
       "4                     0.90563  0.63297                        0.32957   \n",
       "..                        ...      ...                            ...   \n",
       "310                   0.21028  0.39747                        0.06681   \n",
       "311                   0.17344  0.16430                        0.07112   \n",
       "312                   0.24811  0.34678                        0.11587   \n",
       "313                   0.62994  0.06912                        0.17233   \n",
       "314                   0.15747  0.04320                        0.09419   \n",
       "\n",
       "     Generosity  Dystopia Residual  Lower Confidence Interval  \\\n",
       "0       0.29678            2.51738                      3.000   \n",
       "1       0.43630            2.70201                      3.000   \n",
       "2       0.34139            2.49204                      3.000   \n",
       "3       0.34699            2.46531                      3.000   \n",
       "4       0.45811            2.45176                      3.000   \n",
       "..          ...                ...                        ...   \n",
       "310     0.20180            2.10812                      3.404   \n",
       "311     0.31268            2.14558                      3.288   \n",
       "312     0.17517            2.13540                      3.192   \n",
       "313     0.48397            0.81789                      2.936   \n",
       "314     0.20290            2.10404                      2.732   \n",
       "\n",
       "     Upper Confidence Interval  \n",
       "0                        3.000  \n",
       "1                        3.000  \n",
       "2                        3.000  \n",
       "3                        3.000  \n",
       "4                        3.000  \n",
       "..                         ...  \n",
       "310                      3.564  \n",
       "311                      3.432  \n",
       "312                      3.414  \n",
       "313                      3.202  \n",
       "314                      3.078  \n",
       "\n",
       "[315 rows x 14 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      0.03411\n",
       "1      0.04884\n",
       "2      0.03328\n",
       "3      0.03880\n",
       "4      0.03553\n",
       "        ...   \n",
       "310        NaN\n",
       "311        NaN\n",
       "312        NaN\n",
       "313        NaN\n",
       "314        NaN\n",
       "Name: Standard Error, Length: 315, dtype: float64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Standard Error']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.04788474683544306"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(df['Standard Error'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.002292948979494474"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(df['Standard Error'])**2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "mean_squared = 0.002292948979494474 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Standard Error'] = df['Standard Error'].fillna(mean_squared)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Region</th>\n",
       "      <th>Happiness Rank</th>\n",
       "      <th>Happiness Score</th>\n",
       "      <th>Standard Error</th>\n",
       "      <th>Economy (GDP per Capita)</th>\n",
       "      <th>Family</th>\n",
       "      <th>Health (Life Expectancy)</th>\n",
       "      <th>Freedom</th>\n",
       "      <th>Trust (Government Corruption)</th>\n",
       "      <th>Generosity</th>\n",
       "      <th>Dystopia Residual</th>\n",
       "      <th>Lower Confidence Interval</th>\n",
       "      <th>Upper Confidence Interval</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Switzerland</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>1</td>\n",
       "      <td>7.587</td>\n",
       "      <td>0.034110</td>\n",
       "      <td>1.39651</td>\n",
       "      <td>1.34951</td>\n",
       "      <td>0.94143</td>\n",
       "      <td>0.66557</td>\n",
       "      <td>0.41978</td>\n",
       "      <td>0.29678</td>\n",
       "      <td>2.51738</td>\n",
       "      <td>3.000</td>\n",
       "      <td>3.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Iceland</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>2</td>\n",
       "      <td>7.561</td>\n",
       "      <td>0.048840</td>\n",
       "      <td>1.30232</td>\n",
       "      <td>1.40223</td>\n",
       "      <td>0.94784</td>\n",
       "      <td>0.62877</td>\n",
       "      <td>0.14145</td>\n",
       "      <td>0.43630</td>\n",
       "      <td>2.70201</td>\n",
       "      <td>3.000</td>\n",
       "      <td>3.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Denmark</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>3</td>\n",
       "      <td>7.527</td>\n",
       "      <td>0.033280</td>\n",
       "      <td>1.32548</td>\n",
       "      <td>1.36058</td>\n",
       "      <td>0.87464</td>\n",
       "      <td>0.64938</td>\n",
       "      <td>0.48357</td>\n",
       "      <td>0.34139</td>\n",
       "      <td>2.49204</td>\n",
       "      <td>3.000</td>\n",
       "      <td>3.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Norway</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>4</td>\n",
       "      <td>7.522</td>\n",
       "      <td>0.038800</td>\n",
       "      <td>1.45900</td>\n",
       "      <td>1.33095</td>\n",
       "      <td>0.88521</td>\n",
       "      <td>0.66973</td>\n",
       "      <td>0.36503</td>\n",
       "      <td>0.34699</td>\n",
       "      <td>2.46531</td>\n",
       "      <td>3.000</td>\n",
       "      <td>3.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Canada</td>\n",
       "      <td>North America</td>\n",
       "      <td>5</td>\n",
       "      <td>7.427</td>\n",
       "      <td>0.035530</td>\n",
       "      <td>1.32629</td>\n",
       "      <td>1.32261</td>\n",
       "      <td>0.90563</td>\n",
       "      <td>0.63297</td>\n",
       "      <td>0.32957</td>\n",
       "      <td>0.45811</td>\n",
       "      <td>2.45176</td>\n",
       "      <td>3.000</td>\n",
       "      <td>3.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>310</th>\n",
       "      <td>Benin</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>153</td>\n",
       "      <td>3.484</td>\n",
       "      <td>0.002293</td>\n",
       "      <td>0.39499</td>\n",
       "      <td>0.10419</td>\n",
       "      <td>0.21028</td>\n",
       "      <td>0.39747</td>\n",
       "      <td>0.06681</td>\n",
       "      <td>0.20180</td>\n",
       "      <td>2.10812</td>\n",
       "      <td>3.404</td>\n",
       "      <td>3.564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>311</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Southern Asia</td>\n",
       "      <td>154</td>\n",
       "      <td>3.360</td>\n",
       "      <td>0.002293</td>\n",
       "      <td>0.38227</td>\n",
       "      <td>0.11037</td>\n",
       "      <td>0.17344</td>\n",
       "      <td>0.16430</td>\n",
       "      <td>0.07112</td>\n",
       "      <td>0.31268</td>\n",
       "      <td>2.14558</td>\n",
       "      <td>3.288</td>\n",
       "      <td>3.432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>312</th>\n",
       "      <td>Togo</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>155</td>\n",
       "      <td>3.303</td>\n",
       "      <td>0.002293</td>\n",
       "      <td>0.28123</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.24811</td>\n",
       "      <td>0.34678</td>\n",
       "      <td>0.11587</td>\n",
       "      <td>0.17517</td>\n",
       "      <td>2.13540</td>\n",
       "      <td>3.192</td>\n",
       "      <td>3.414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>313</th>\n",
       "      <td>Syria</td>\n",
       "      <td>Middle East and Northern Africa</td>\n",
       "      <td>156</td>\n",
       "      <td>3.069</td>\n",
       "      <td>0.002293</td>\n",
       "      <td>0.74719</td>\n",
       "      <td>0.14866</td>\n",
       "      <td>0.62994</td>\n",
       "      <td>0.06912</td>\n",
       "      <td>0.17233</td>\n",
       "      <td>0.48397</td>\n",
       "      <td>0.81789</td>\n",
       "      <td>2.936</td>\n",
       "      <td>3.202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>314</th>\n",
       "      <td>Burundi</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>157</td>\n",
       "      <td>2.905</td>\n",
       "      <td>0.002293</td>\n",
       "      <td>0.06831</td>\n",
       "      <td>0.23442</td>\n",
       "      <td>0.15747</td>\n",
       "      <td>0.04320</td>\n",
       "      <td>0.09419</td>\n",
       "      <td>0.20290</td>\n",
       "      <td>2.10404</td>\n",
       "      <td>2.732</td>\n",
       "      <td>3.078</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>315 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Country                           Region  Happiness Rank  \\\n",
       "0    Switzerland                   Western Europe               1   \n",
       "1        Iceland                   Western Europe               2   \n",
       "2        Denmark                   Western Europe               3   \n",
       "3         Norway                   Western Europe               4   \n",
       "4         Canada                    North America               5   \n",
       "..           ...                              ...             ...   \n",
       "310        Benin               Sub-Saharan Africa             153   \n",
       "311  Afghanistan                    Southern Asia             154   \n",
       "312         Togo               Sub-Saharan Africa             155   \n",
       "313        Syria  Middle East and Northern Africa             156   \n",
       "314      Burundi               Sub-Saharan Africa             157   \n",
       "\n",
       "     Happiness Score  Standard Error  Economy (GDP per Capita)   Family  \\\n",
       "0              7.587        0.034110                   1.39651  1.34951   \n",
       "1              7.561        0.048840                   1.30232  1.40223   \n",
       "2              7.527        0.033280                   1.32548  1.36058   \n",
       "3              7.522        0.038800                   1.45900  1.33095   \n",
       "4              7.427        0.035530                   1.32629  1.32261   \n",
       "..               ...             ...                       ...      ...   \n",
       "310            3.484        0.002293                   0.39499  0.10419   \n",
       "311            3.360        0.002293                   0.38227  0.11037   \n",
       "312            3.303        0.002293                   0.28123  0.00000   \n",
       "313            3.069        0.002293                   0.74719  0.14866   \n",
       "314            2.905        0.002293                   0.06831  0.23442   \n",
       "\n",
       "     Health (Life Expectancy)  Freedom  Trust (Government Corruption)  \\\n",
       "0                     0.94143  0.66557                        0.41978   \n",
       "1                     0.94784  0.62877                        0.14145   \n",
       "2                     0.87464  0.64938                        0.48357   \n",
       "3                     0.88521  0.66973                        0.36503   \n",
       "4                     0.90563  0.63297                        0.32957   \n",
       "..                        ...      ...                            ...   \n",
       "310                   0.21028  0.39747                        0.06681   \n",
       "311                   0.17344  0.16430                        0.07112   \n",
       "312                   0.24811  0.34678                        0.11587   \n",
       "313                   0.62994  0.06912                        0.17233   \n",
       "314                   0.15747  0.04320                        0.09419   \n",
       "\n",
       "     Generosity  Dystopia Residual  Lower Confidence Interval  \\\n",
       "0       0.29678            2.51738                      3.000   \n",
       "1       0.43630            2.70201                      3.000   \n",
       "2       0.34139            2.49204                      3.000   \n",
       "3       0.34699            2.46531                      3.000   \n",
       "4       0.45811            2.45176                      3.000   \n",
       "..          ...                ...                        ...   \n",
       "310     0.20180            2.10812                      3.404   \n",
       "311     0.31268            2.14558                      3.288   \n",
       "312     0.17517            2.13540                      3.192   \n",
       "313     0.48397            0.81789                      2.936   \n",
       "314     0.20290            2.10404                      2.732   \n",
       "\n",
       "     Upper Confidence Interval  \n",
       "0                        3.000  \n",
       "1                        3.000  \n",
       "2                        3.000  \n",
       "3                        3.000  \n",
       "4                        3.000  \n",
       "..                         ...  \n",
       "310                      3.564  \n",
       "311                      3.432  \n",
       "312                      3.414  \n",
       "313                      3.202  \n",
       "314                      3.078  \n",
       "\n",
       "[315 rows x 14 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "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>Happiness Rank</th>\n",
       "      <th>Happiness Score</th>\n",
       "      <th>Standard Error</th>\n",
       "      <th>Economy (GDP per Capita)</th>\n",
       "      <th>Family</th>\n",
       "      <th>Health (Life Expectancy)</th>\n",
       "      <th>Freedom</th>\n",
       "      <th>Trust (Government Corruption)</th>\n",
       "      <th>Generosity</th>\n",
       "      <th>Dystopia Residual</th>\n",
       "      <th>Lower Confidence Interval</th>\n",
       "      <th>Upper Confidence Interval</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>315.000000</td>\n",
       "      <td>315.000000</td>\n",
       "      <td>315.000000</td>\n",
       "      <td>315.000000</td>\n",
       "      <td>315.000000</td>\n",
       "      <td>315.000000</td>\n",
       "      <td>315.000000</td>\n",
       "      <td>315.000000</td>\n",
       "      <td>315.000000</td>\n",
       "      <td>315.000000</td>\n",
       "      <td>315.000000</td>\n",
       "      <td>315.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>79.238095</td>\n",
       "      <td>5.378949</td>\n",
       "      <td>0.025161</td>\n",
       "      <td>0.899837</td>\n",
       "      <td>0.892647</td>\n",
       "      <td>0.594054</td>\n",
       "      <td>0.399896</td>\n",
       "      <td>0.140532</td>\n",
       "      <td>0.239957</td>\n",
       "      <td>2.212032</td>\n",
       "      <td>4.137575</td>\n",
       "      <td>4.237048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>45.538922</td>\n",
       "      <td>1.141531</td>\n",
       "      <td>0.025851</td>\n",
       "      <td>0.410780</td>\n",
       "      <td>0.286718</td>\n",
       "      <td>0.240790</td>\n",
       "      <td>0.150684</td>\n",
       "      <td>0.115490</td>\n",
       "      <td>0.130077</td>\n",
       "      <td>0.558728</td>\n",
       "      <td>1.400453</td>\n",
       "      <td>1.478727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.839000</td>\n",
       "      <td>0.002293</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.328580</td>\n",
       "      <td>2.732000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>40.000000</td>\n",
       "      <td>4.510000</td>\n",
       "      <td>0.002293</td>\n",
       "      <td>0.594900</td>\n",
       "      <td>0.739880</td>\n",
       "      <td>0.419645</td>\n",
       "      <td>0.295890</td>\n",
       "      <td>0.061315</td>\n",
       "      <td>0.151430</td>\n",
       "      <td>1.884135</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>79.000000</td>\n",
       "      <td>5.286000</td>\n",
       "      <td>0.018480</td>\n",
       "      <td>0.973060</td>\n",
       "      <td>0.937930</td>\n",
       "      <td>0.640450</td>\n",
       "      <td>0.413190</td>\n",
       "      <td>0.106130</td>\n",
       "      <td>0.218540</td>\n",
       "      <td>2.211260</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>118.500000</td>\n",
       "      <td>6.269000</td>\n",
       "      <td>0.043940</td>\n",
       "      <td>1.229000</td>\n",
       "      <td>1.087360</td>\n",
       "      <td>0.787640</td>\n",
       "      <td>0.516730</td>\n",
       "      <td>0.178610</td>\n",
       "      <td>0.311450</td>\n",
       "      <td>2.563470</td>\n",
       "      <td>5.231500</td>\n",
       "      <td>5.408500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>158.000000</td>\n",
       "      <td>7.587000</td>\n",
       "      <td>0.136930</td>\n",
       "      <td>1.824270</td>\n",
       "      <td>1.402230</td>\n",
       "      <td>1.025250</td>\n",
       "      <td>0.669730</td>\n",
       "      <td>0.551910</td>\n",
       "      <td>0.819710</td>\n",
       "      <td>3.837720</td>\n",
       "      <td>7.460000</td>\n",
       "      <td>7.669000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Happiness Rank  Happiness Score  Standard Error  \\\n",
       "count      315.000000       315.000000      315.000000   \n",
       "mean        79.238095         5.378949        0.025161   \n",
       "std         45.538922         1.141531        0.025851   \n",
       "min          1.000000         2.839000        0.002293   \n",
       "25%         40.000000         4.510000        0.002293   \n",
       "50%         79.000000         5.286000        0.018480   \n",
       "75%        118.500000         6.269000        0.043940   \n",
       "max        158.000000         7.587000        0.136930   \n",
       "\n",
       "       Economy (GDP per Capita)      Family  Health (Life Expectancy)  \\\n",
       "count                315.000000  315.000000                315.000000   \n",
       "mean                   0.899837    0.892647                  0.594054   \n",
       "std                    0.410780    0.286718                  0.240790   \n",
       "min                    0.000000    0.000000                  0.000000   \n",
       "25%                    0.594900    0.739880                  0.419645   \n",
       "50%                    0.973060    0.937930                  0.640450   \n",
       "75%                    1.229000    1.087360                  0.787640   \n",
       "max                    1.824270    1.402230                  1.025250   \n",
       "\n",
       "          Freedom  Trust (Government Corruption)  Generosity  \\\n",
       "count  315.000000                     315.000000  315.000000   \n",
       "mean     0.399896                       0.140532    0.239957   \n",
       "std      0.150684                       0.115490    0.130077   \n",
       "min      0.000000                       0.000000    0.000000   \n",
       "25%      0.295890                       0.061315    0.151430   \n",
       "50%      0.413190                       0.106130    0.218540   \n",
       "75%      0.516730                       0.178610    0.311450   \n",
       "max      0.669730                       0.551910    0.819710   \n",
       "\n",
       "       Dystopia Residual  Lower Confidence Interval  Upper Confidence Interval  \n",
       "count         315.000000                 315.000000                 315.000000  \n",
       "mean            2.212032                   4.137575                   4.237048  \n",
       "std             0.558728                   1.400453                   1.478727  \n",
       "min             0.328580                   2.732000                   3.000000  \n",
       "25%             1.884135                   3.000000                   3.000000  \n",
       "50%             2.211260                   3.000000                   3.000000  \n",
       "75%             2.563470                   5.231500                   5.408500  \n",
       "max             3.837720                   7.460000                   7.669000  "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0xa0fdee0>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(x=\"Family\", y = 'Health (Life Expectancy)', data = df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0xb3d8598>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(x=\"Happiness Score\", y = 'Trust (Government Corruption)', data = df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7046838>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df.corr(), cmap='Reds')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Country\n",
       "Afghanistan    3.575\n",
       "Albania        4.959\n",
       "Algeria        6.355\n",
       "Angola         4.033\n",
       "Argentina      6.650\n",
       "               ...  \n",
       "Venezuela      6.810\n",
       "Vietnam        5.360\n",
       "Yemen          4.077\n",
       "Zambia         5.129\n",
       "Zimbabwe       4.610\n",
       "Name: Happiness Score, Length: 164, dtype: float64"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['Country']).max()['Happiness Score']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "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>Region</th>\n",
       "      <th>Happiness Rank</th>\n",
       "      <th>Happiness Score</th>\n",
       "      <th>Standard Error</th>\n",
       "      <th>Economy (GDP per Capita)</th>\n",
       "      <th>Family</th>\n",
       "      <th>Health (Life Expectancy)</th>\n",
       "      <th>Freedom</th>\n",
       "      <th>Trust (Government Corruption)</th>\n",
       "      <th>Generosity</th>\n",
       "      <th>Dystopia Residual</th>\n",
       "      <th>Lower Confidence Interval</th>\n",
       "      <th>Upper Confidence Interval</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Switzerland</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>1</td>\n",
       "      <td>7.587</td>\n",
       "      <td>0.034110</td>\n",
       "      <td>1.39651</td>\n",
       "      <td>1.34951</td>\n",
       "      <td>0.94143</td>\n",
       "      <td>0.66557</td>\n",
       "      <td>0.41978</td>\n",
       "      <td>0.29678</td>\n",
       "      <td>2.51738</td>\n",
       "      <td>3.000</td>\n",
       "      <td>3.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Iceland</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>2</td>\n",
       "      <td>7.561</td>\n",
       "      <td>0.048840</td>\n",
       "      <td>1.30232</td>\n",
       "      <td>1.40223</td>\n",
       "      <td>0.94784</td>\n",
       "      <td>0.62877</td>\n",
       "      <td>0.14145</td>\n",
       "      <td>0.43630</td>\n",
       "      <td>2.70201</td>\n",
       "      <td>3.000</td>\n",
       "      <td>3.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Denmark</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>3</td>\n",
       "      <td>7.527</td>\n",
       "      <td>0.033280</td>\n",
       "      <td>1.32548</td>\n",
       "      <td>1.36058</td>\n",
       "      <td>0.87464</td>\n",
       "      <td>0.64938</td>\n",
       "      <td>0.48357</td>\n",
       "      <td>0.34139</td>\n",
       "      <td>2.49204</td>\n",
       "      <td>3.000</td>\n",
       "      <td>3.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Norway</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>4</td>\n",
       "      <td>7.522</td>\n",
       "      <td>0.038800</td>\n",
       "      <td>1.45900</td>\n",
       "      <td>1.33095</td>\n",
       "      <td>0.88521</td>\n",
       "      <td>0.66973</td>\n",
       "      <td>0.36503</td>\n",
       "      <td>0.34699</td>\n",
       "      <td>2.46531</td>\n",
       "      <td>3.000</td>\n",
       "      <td>3.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Canada</td>\n",
       "      <td>North America</td>\n",
       "      <td>5</td>\n",
       "      <td>7.427</td>\n",
       "      <td>0.035530</td>\n",
       "      <td>1.32629</td>\n",
       "      <td>1.32261</td>\n",
       "      <td>0.90563</td>\n",
       "      <td>0.63297</td>\n",
       "      <td>0.32957</td>\n",
       "      <td>0.45811</td>\n",
       "      <td>2.45176</td>\n",
       "      <td>3.000</td>\n",
       "      <td>3.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>310</th>\n",
       "      <td>Benin</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>153</td>\n",
       "      <td>3.484</td>\n",
       "      <td>0.002293</td>\n",
       "      <td>0.39499</td>\n",
       "      <td>0.10419</td>\n",
       "      <td>0.21028</td>\n",
       "      <td>0.39747</td>\n",
       "      <td>0.06681</td>\n",
       "      <td>0.20180</td>\n",
       "      <td>2.10812</td>\n",
       "      <td>3.404</td>\n",
       "      <td>3.564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>311</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Southern Asia</td>\n",
       "      <td>154</td>\n",
       "      <td>3.360</td>\n",
       "      <td>0.002293</td>\n",
       "      <td>0.38227</td>\n",
       "      <td>0.11037</td>\n",
       "      <td>0.17344</td>\n",
       "      <td>0.16430</td>\n",
       "      <td>0.07112</td>\n",
       "      <td>0.31268</td>\n",
       "      <td>2.14558</td>\n",
       "      <td>3.288</td>\n",
       "      <td>3.432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>312</th>\n",
       "      <td>Togo</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>155</td>\n",
       "      <td>3.303</td>\n",
       "      <td>0.002293</td>\n",
       "      <td>0.28123</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.24811</td>\n",
       "      <td>0.34678</td>\n",
       "      <td>0.11587</td>\n",
       "      <td>0.17517</td>\n",
       "      <td>2.13540</td>\n",
       "      <td>3.192</td>\n",
       "      <td>3.414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>313</th>\n",
       "      <td>Syria</td>\n",
       "      <td>Middle East and Northern Africa</td>\n",
       "      <td>156</td>\n",
       "      <td>3.069</td>\n",
       "      <td>0.002293</td>\n",
       "      <td>0.74719</td>\n",
       "      <td>0.14866</td>\n",
       "      <td>0.62994</td>\n",
       "      <td>0.06912</td>\n",
       "      <td>0.17233</td>\n",
       "      <td>0.48397</td>\n",
       "      <td>0.81789</td>\n",
       "      <td>2.936</td>\n",
       "      <td>3.202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>314</th>\n",
       "      <td>Burundi</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>157</td>\n",
       "      <td>2.905</td>\n",
       "      <td>0.002293</td>\n",
       "      <td>0.06831</td>\n",
       "      <td>0.23442</td>\n",
       "      <td>0.15747</td>\n",
       "      <td>0.04320</td>\n",
       "      <td>0.09419</td>\n",
       "      <td>0.20290</td>\n",
       "      <td>2.10404</td>\n",
       "      <td>2.732</td>\n",
       "      <td>3.078</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>315 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Country                           Region  Happiness Rank  \\\n",
       "0    Switzerland                   Western Europe               1   \n",
       "1        Iceland                   Western Europe               2   \n",
       "2        Denmark                   Western Europe               3   \n",
       "3         Norway                   Western Europe               4   \n",
       "4         Canada                    North America               5   \n",
       "..           ...                              ...             ...   \n",
       "310        Benin               Sub-Saharan Africa             153   \n",
       "311  Afghanistan                    Southern Asia             154   \n",
       "312         Togo               Sub-Saharan Africa             155   \n",
       "313        Syria  Middle East and Northern Africa             156   \n",
       "314      Burundi               Sub-Saharan Africa             157   \n",
       "\n",
       "     Happiness Score  Standard Error  Economy (GDP per Capita)   Family  \\\n",
       "0              7.587        0.034110                   1.39651  1.34951   \n",
       "1              7.561        0.048840                   1.30232  1.40223   \n",
       "2              7.527        0.033280                   1.32548  1.36058   \n",
       "3              7.522        0.038800                   1.45900  1.33095   \n",
       "4              7.427        0.035530                   1.32629  1.32261   \n",
       "..               ...             ...                       ...      ...   \n",
       "310            3.484        0.002293                   0.39499  0.10419   \n",
       "311            3.360        0.002293                   0.38227  0.11037   \n",
       "312            3.303        0.002293                   0.28123  0.00000   \n",
       "313            3.069        0.002293                   0.74719  0.14866   \n",
       "314            2.905        0.002293                   0.06831  0.23442   \n",
       "\n",
       "     Health (Life Expectancy)  Freedom  Trust (Government Corruption)  \\\n",
       "0                     0.94143  0.66557                        0.41978   \n",
       "1                     0.94784  0.62877                        0.14145   \n",
       "2                     0.87464  0.64938                        0.48357   \n",
       "3                     0.88521  0.66973                        0.36503   \n",
       "4                     0.90563  0.63297                        0.32957   \n",
       "..                        ...      ...                            ...   \n",
       "310                   0.21028  0.39747                        0.06681   \n",
       "311                   0.17344  0.16430                        0.07112   \n",
       "312                   0.24811  0.34678                        0.11587   \n",
       "313                   0.62994  0.06912                        0.17233   \n",
       "314                   0.15747  0.04320                        0.09419   \n",
       "\n",
       "     Generosity  Dystopia Residual  Lower Confidence Interval  \\\n",
       "0       0.29678            2.51738                      3.000   \n",
       "1       0.43630            2.70201                      3.000   \n",
       "2       0.34139            2.49204                      3.000   \n",
       "3       0.34699            2.46531                      3.000   \n",
       "4       0.45811            2.45176                      3.000   \n",
       "..          ...                ...                        ...   \n",
       "310     0.20180            2.10812                      3.404   \n",
       "311     0.31268            2.14558                      3.288   \n",
       "312     0.17517            2.13540                      3.192   \n",
       "313     0.48397            0.81789                      2.936   \n",
       "314     0.20290            2.10404                      2.732   \n",
       "\n",
       "     Upper Confidence Interval  \n",
       "0                        3.000  \n",
       "1                        3.000  \n",
       "2                        3.000  \n",
       "3                        3.000  \n",
       "4                        3.000  \n",
       "..                         ...  \n",
       "310                      3.564  \n",
       "311                      3.432  \n",
       "312                      3.414  \n",
       "313                      3.202  \n",
       "314                      3.078  \n",
       "\n",
       "[315 rows x 14 columns]"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0xb427130>"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(x=\"Generosity\", y = 'Freedom', data = df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\user\\anaconda3\\lib\\site-packages\\seaborn\\categorical.py:3666: UserWarning: The `factorplot` function has been renamed to `catplot`. The original name will be removed in a future release. Please update your code. Note that the default `kind` in `factorplot` (`'point'`) has changed `'strip'` in `catplot`.\n",
      "  warnings.warn(msg)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0xb67c250>"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.factorplot(x='Dystopia Residual',\n",
    "               y= 'Region',\n",
    "               data=df)\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}