ning_notebook.ipynb

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    "%matplotlib inline\n",
    "# Dependencies and Setup\n",
    "from pathlib import Path\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import scipy.stats as st\n",
    "\n",
    "# Study data files\n",
    "C2015_path = Path(\"data_NG/C2015.csv\")\n",
    "C2016_path = Path(\"data_NG/C2016.csv\")\n",
    "C2017_path = Path(\"data_NG/C2017.csv\")\n",
    "C2018_path = Path(\"data_NG/C2018.csv\")\n",
    "C2019_path = Path(\"data_NG/C2019.csv\")\n",
    "C2020_path = Path(\"data_NG/C2020.csv\")\n",
    "C2021_path = Path(\"data_NG/C2021.csv\")\n",
    "C2022_path = Path(\"data_NG/C2022.csv\")\n",
    "C2023_path = Path(\"data_NG/C2023.csv\")\n",
    "\n",
    "# Read all years' data into dataframes\n",
    "C2015 = pd.read_csv(C2015_path)\n",
    "C2016 = pd.read_csv(C2016_path)\n",
    "C2017 = pd.read_csv(C2017_path)\n",
    "C2018 = pd.read_csv(C2018_path)\n",
    "C2019 = pd.read_csv(C2019_path)\n",
    "C2020 = pd.read_csv(C2020_path)\n",
    "C2021 = pd.read_csv(C2021_path)\n",
    "C2022 = pd.read_csv(C2022_path)\n",
    "C2023 = pd.read_csv(C2023_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72ee8d35-65b7-4bd9-b878-1a1b82a759cd",
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   "source": [
    "## Part 1: List the Top and Bottom 5 contries through year 2015-2021"
   ]
  },
  {
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    "# Get the top 5 countries by happiness scores in each year, and simply the columns to the only needed ones\n",
    "C2015_top5 = (C2015.loc[0:4,['Country','Region','Happiness Rank','Happiness Score','Economy (GDP per Capita)','HALE','Social Support','Dystopia Residual']]).assign(Year = lambda x:'2015')\n",
    "C2016_top5 = (C2016.loc[0:4,['Country','Region','Happiness Rank','Happiness Score','Economy (GDP per Capita)','HALE','Social Support','Dystopia Residual']]).assign(Year = lambda x:'2016')\n",
    "C2017_top5 = (C2017.loc[0:4,['Country','Region','Happiness Rank','Happiness Score','Economy (GDP per Capita)','HALE','Social Support','Dystopia Residual']]).assign(Year = lambda x:'2017')\n",
    "C2018_top5 = (C2018.loc[0:4,['Country','Region','Happiness Rank','Happiness Score','Economy (GDP per Capita)','HALE','Social Support','Dystopia Residual']]).assign(Year = lambda x:'2018')\n",
    "C2019_top5 = (C2019.loc[0:4,['Country','Region','Happiness Rank','Happiness Score','Economy (GDP per Capita)','HALE','Social Support','Dystopia Residual']]).assign(Year = lambda x:'2019')\n",
    "C2020_top5 = (C2020.loc[0:4,['Country','Region','Happiness Rank','Happiness Score','Economy (GDP per Capita)','HALE','Social Support','Dystopia Residual']]).assign(Year = lambda x:'2020')\n",
    "C2021_top5 = (C2021.loc[0:4,['Country','Region','Happiness Rank','Happiness Score','Economy (GDP per Capita)','HALE','Social Support','Dystopia Residual']]).assign(Year = lambda x:'2021')\n",
    "C2022_top5 = (C2022.loc[0:4,['Country','Region','Happiness Rank','Happiness Score','Economy (GDP per Capita)','HALE','Social Support','Dystopia Residual']]).assign(Year = lambda x:'2022')\n",
    "C2023_top5 = (C2023.loc[0:4,['Country','Region','Happiness Rank','Happiness Score','Economy (GDP per Capita)','HALE','Social Support','Dystopia Residual']]).assign(Year = lambda x:'2023')\n"
   ]
  },
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   "cell_type": "code",
   "execution_count": 11,
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   "source": [
    "#all_list=[C2015,C2016,C2017,C2018,C2019,C2020,C2021,C2022,C2023]\n",
    "#for i in all_list:\n",
    "#   i_top5 = i.loc[0:4,['Country','Happiness Rank','Happiness Score','Economy (GDP per Capita)','HALE','Dystopia Residual']]"
   ]
  },
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    "\n",
    "# C2015_bottom = C2015.sort_values('Happiness Rank',ascending=False)\n",
    "#C2015_bottom5 = C2015.loc[-5:-1,['Country','Region','Happiness Rank','Happiness Score','Economy (GDP per Capita)','HALE','Social Support','Dystopia Residual']]\n",
    "C2016_bottom = C2016.sort_values('Happiness Rank',ascending=True)\n",
    "C2016_bottom5 = C2016.loc[0:4,['Country','Region','Happiness Rank','Happiness Score','Economy (GDP per Capita)','HALE','Social Support','Dystopia Residual']]\n",
    "C2017_bottom = C2017.sort_values('Happiness Rank',ascending=True)\n",
    "C2017_bottom5 = C2017.loc[0:4,['Country','Region','Happiness Rank','Happiness Score','Economy (GDP per Capita)','HALE','Social Support','Dystopia Residual']]\n",
    "C2018_bottom = C2018.sort_values('Happiness Rank',ascending=True)\n",
    "C2018_bottom5 = C2018.loc[0:4,['Country','Region','Happiness Rank','Happiness Score','Economy (GDP per Capita)','HALE','Social Support','Dystopia Residual']]\n",
    "C2019_bottom = C2019.sort_values('Happiness Rank',ascending=True)\n",
    "C2019_bottom5 = C2019.loc[0:4,['Country','Region','Happiness Rank','Happiness Score','Economy (GDP per Capita)','HALE','Social Support','Dystopia Residual']]\n",
    "C2020_bottom = C2020.sort_values('Happiness Rank',ascending=True)\n",
    "C2020_bottom5 = C2020.loc[0:4,['Country','Region','Happiness Rank','Happiness Score','Economy (GDP per Capita)','HALE','Social Support','Dystopia Residual']]\n",
    "C2021_bottom = C2021.sort_values('Happiness Rank',ascending=True)\n",
    "C2021_bottom5 = C2021.loc[0:4,['Country','Region','Happiness Rank','Happiness Score','Economy (GDP per Capita)','HALE','Social Support','Dystopia Residual']]\n"
   ]
  },
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    "# Get the bottom 5 countries by happiness scores in each year, and simply the columns to the only needed ones\n",
    "C2015_bottom5 = (C2015[['Country','Region','Happiness Rank','Happiness Score','Economy (GDP per Capita)','HALE','Social Support','Dystopia Residual']].tail(5)).assign(Year = lambda x:'2015')\n",
    "C2016_bottom5 = (C2016[['Country','Region','Happiness Rank','Happiness Score','Economy (GDP per Capita)','HALE','Social Support','Dystopia Residual']].tail(5)).assign(Year = lambda x:'2016')\n",
    "C2017_bottom5 = (C2017[['Country','Region','Happiness Rank','Happiness Score','Economy (GDP per Capita)','HALE','Social Support','Dystopia Residual']].tail(5)).assign(Year = lambda x:'2017')\n",
    "C2018_bottom5 = (C2018[['Country','Region','Happiness Rank','Happiness Score','Economy (GDP per Capita)','HALE','Social Support','Dystopia Residual']].tail(5)).assign(Year = lambda x:'2018')\n",
    "C2019_bottom5 = (C2019[['Country','Region','Happiness Rank','Happiness Score','Economy (GDP per Capita)','HALE','Social Support','Dystopia Residual']].tail(5)).assign(Year = lambda x:'2019')\n",
    "C2020_bottom5 = (C2020[['Country','Region','Happiness Rank','Happiness Score','Economy (GDP per Capita)','HALE','Social Support','Dystopia Residual']].tail(5)).assign(Year = lambda x:'2020')\n",
    "C2021_bottom5 = (C2021[['Country','Region','Happiness Rank','Happiness Score','Economy (GDP per Capita)','HALE','Social Support','Dystopia Residual']].tail(5)).assign(Year = lambda x:'2021')\n",
    "C2022_bottom5 = (C2022[['Country','Region','Happiness Rank','Happiness Score','Economy (GDP per Capita)','HALE','Social Support','Dystopia Residual']].tail(5)).assign(Year = lambda x:'2022')\n",
    "C2023_bottom5 = (C2023[['Country','Region','Happiness Rank','Happiness Score','Economy (GDP per Capita)','HALE','Social Support','Dystopia Residual']].tail(5)).assign(Year = lambda x:'2023')\n"
   ]
  },
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Region</th>\n",
       "      <th>Happiness Rank</th>\n",
       "      <th>Happiness Score</th>\n",
       "      <th>Economy (GDP per Capita)</th>\n",
       "      <th>HALE</th>\n",
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       "      <th>Year</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>1.396510</td>\n",
       "      <td>0.941430</td>\n",
       "      <td>1.349510</td>\n",
       "      <td>2.517380</td>\n",
       "      <td>2015</td>\n",
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       "    <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>1.302320</td>\n",
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       "      <td>1.402230</td>\n",
       "      <td>2.702010</td>\n",
       "      <td>2015</td>\n",
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       "    <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>1.325480</td>\n",
       "      <td>0.874640</td>\n",
       "      <td>1.360580</td>\n",
       "      <td>2.492040</td>\n",
       "      <td>2015</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>1.459000</td>\n",
       "      <td>0.885210</td>\n",
       "      <td>1.330950</td>\n",
       "      <td>2.465310</td>\n",
       "      <td>2015</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>1.326290</td>\n",
       "      <td>0.905630</td>\n",
       "      <td>1.322610</td>\n",
       "      <td>2.451760</td>\n",
       "      <td>2015</td>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Denmark</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>1</td>\n",
       "      <td>7.526</td>\n",
       "      <td>1.441780</td>\n",
       "      <td>0.795040</td>\n",
       "      <td>1.163740</td>\n",
       "      <td>2.739390</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Switzerland</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>2</td>\n",
       "      <td>7.509</td>\n",
       "      <td>1.527330</td>\n",
       "      <td>0.863030</td>\n",
       "      <td>1.145240</td>\n",
       "      <td>2.694630</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Iceland</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>3</td>\n",
       "      <td>7.501</td>\n",
       "      <td>1.426660</td>\n",
       "      <td>0.867330</td>\n",
       "      <td>1.183260</td>\n",
       "      <td>2.831370</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Norway</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>4</td>\n",
       "      <td>7.498</td>\n",
       "      <td>1.577440</td>\n",
       "      <td>0.795790</td>\n",
       "      <td>1.126900</td>\n",
       "      <td>2.664650</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Finland</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>5</td>\n",
       "      <td>7.413</td>\n",
       "      <td>1.405980</td>\n",
       "      <td>0.810910</td>\n",
       "      <td>1.134640</td>\n",
       "      <td>2.825960</td>\n",
       "      <td>2016</td>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Norway</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>7.537</td>\n",
       "      <td>1.616463</td>\n",
       "      <td>0.796667</td>\n",
       "      <td>1.533524</td>\n",
       "      <td>2.277027</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Denmark</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>7.522</td>\n",
       "      <td>1.482383</td>\n",
       "      <td>0.792566</td>\n",
       "      <td>1.551122</td>\n",
       "      <td>2.313707</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Iceland</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>7.504</td>\n",
       "      <td>1.480633</td>\n",
       "      <td>0.833552</td>\n",
       "      <td>1.610574</td>\n",
       "      <td>2.322715</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Switzerland</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>7.494</td>\n",
       "      <td>1.564980</td>\n",
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       "      <td>1.516912</td>\n",
       "      <td>2.276716</td>\n",
       "      <td>2017</td>\n",
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       "      <th>4</th>\n",
       "      <td>Finland</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>7.469</td>\n",
       "      <td>1.443572</td>\n",
       "      <td>0.809158</td>\n",
       "      <td>1.540247</td>\n",
       "      <td>2.430182</td>\n",
       "      <td>2017</td>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Finland</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>7.632</td>\n",
       "      <td>1.305000</td>\n",
       "      <td>0.874000</td>\n",
       "      <td>1.592000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Norway</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>7.594</td>\n",
       "      <td>1.456000</td>\n",
       "      <td>0.861000</td>\n",
       "      <td>1.582000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Denmark</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>7.555</td>\n",
       "      <td>1.351000</td>\n",
       "      <td>0.868000</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>2018</td>\n",
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       "      <th>3</th>\n",
       "      <td>Iceland</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>7.495</td>\n",
       "      <td>1.343000</td>\n",
       "      <td>0.914000</td>\n",
       "      <td>1.644000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Switzerland</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>7.487</td>\n",
       "      <td>1.420000</td>\n",
       "      <td>0.927000</td>\n",
       "      <td>1.549000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018</td>\n",
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       "      <td>Finland</td>\n",
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       "      <td>1</td>\n",
       "      <td>7.769</td>\n",
       "      <td>1.340000</td>\n",
       "      <td>0.986000</td>\n",
       "      <td>1.587000</td>\n",
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       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Denmark</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>7.600</td>\n",
       "      <td>1.383000</td>\n",
       "      <td>0.996000</td>\n",
       "      <td>1.573000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Norway</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>7.554</td>\n",
       "      <td>1.488000</td>\n",
       "      <td>1.028000</td>\n",
       "      <td>1.582000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Iceland</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>7.494</td>\n",
       "      <td>1.380000</td>\n",
       "      <td>1.026000</td>\n",
       "      <td>1.624000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Netherlands</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>7.488</td>\n",
       "      <td>1.396000</td>\n",
       "      <td>0.999000</td>\n",
       "      <td>1.522000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Country          Region  Happiness Rank  Happiness Score  \\\n",
       "0  Switzerland  Western Europe               1            7.587   \n",
       "1      Iceland  Western Europe               2            7.561   \n",
       "2      Denmark  Western Europe               3            7.527   \n",
       "3       Norway  Western Europe               4            7.522   \n",
       "4       Canada   North America               5            7.427   \n",
       "0      Denmark  Western Europe               1            7.526   \n",
       "1  Switzerland  Western Europe               2            7.509   \n",
       "2      Iceland  Western Europe               3            7.501   \n",
       "3       Norway  Western Europe               4            7.498   \n",
       "4      Finland  Western Europe               5            7.413   \n",
       "0       Norway             NaN               1            7.537   \n",
       "1      Denmark             NaN               2            7.522   \n",
       "2      Iceland             NaN               3            7.504   \n",
       "3  Switzerland             NaN               4            7.494   \n",
       "4      Finland             NaN               5            7.469   \n",
       "0      Finland             NaN               1            7.632   \n",
       "1       Norway             NaN               2            7.594   \n",
       "2      Denmark             NaN               3            7.555   \n",
       "3      Iceland             NaN               4            7.495   \n",
       "4  Switzerland             NaN               5            7.487   \n",
       "0      Finland             NaN               1            7.769   \n",
       "1      Denmark             NaN               2            7.600   \n",
       "2       Norway             NaN               3            7.554   \n",
       "3      Iceland             NaN               4            7.494   \n",
       "4  Netherlands             NaN               5            7.488   \n",
       "\n",
       "   Economy (GDP per Capita)      HALE  Social Support  Dystopia Residual  Year  \n",
       "0                  1.396510  0.941430        1.349510           2.517380  2015  \n",
       "1                  1.302320  0.947840        1.402230           2.702010  2015  \n",
       "2                  1.325480  0.874640        1.360580           2.492040  2015  \n",
       "3                  1.459000  0.885210        1.330950           2.465310  2015  \n",
       "4                  1.326290  0.905630        1.322610           2.451760  2015  \n",
       "0                  1.441780  0.795040        1.163740           2.739390  2016  \n",
       "1                  1.527330  0.863030        1.145240           2.694630  2016  \n",
       "2                  1.426660  0.867330        1.183260           2.831370  2016  \n",
       "3                  1.577440  0.795790        1.126900           2.664650  2016  \n",
       "4                  1.405980  0.810910        1.134640           2.825960  2016  \n",
       "0                  1.616463  0.796667        1.533524           2.277027  2017  \n",
       "1                  1.482383  0.792566        1.551122           2.313707  2017  \n",
       "2                  1.480633  0.833552        1.610574           2.322715  2017  \n",
       "3                  1.564980  0.858131        1.516912           2.276716  2017  \n",
       "4                  1.443572  0.809158        1.540247           2.430182  2017  \n",
       "0                  1.305000  0.874000        1.592000                NaN  2018  \n",
       "1                  1.456000  0.861000        1.582000                NaN  2018  \n",
       "2                  1.351000  0.868000        1.590000                NaN  2018  \n",
       "3                  1.343000  0.914000        1.644000                NaN  2018  \n",
       "4                  1.420000  0.927000        1.549000                NaN  2018  \n",
       "0                  1.340000  0.986000        1.587000                NaN  2019  \n",
       "1                  1.383000  0.996000        1.573000                NaN  2019  \n",
       "2                  1.488000  1.028000        1.582000                NaN  2019  \n",
       "3                  1.380000  1.026000        1.624000                NaN  2019  \n",
       "4                  1.396000  0.999000        1.522000                NaN  2019  "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#put all years top 5 countries in one df\n",
    "top5_all = pd.concat([C2015_top5,C2016_top5,C2017_top5,C2018_top5,C2019_top5])\n",
    "top5_all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "b2ed1e14-cd3e-439a-9416-12788c0e1338",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "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>Economy (GDP per Capita)</th>\n",
       "      <th>HALE</th>\n",
       "      <th>Social Support</th>\n",
       "      <th>Dystopia Residual</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>Rwanda</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>154</td>\n",
       "      <td>3.465</td>\n",
       "      <td>0.222080</td>\n",
       "      <td>0.428640</td>\n",
       "      <td>0.773700</td>\n",
       "      <td>0.670420</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>Benin</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>155</td>\n",
       "      <td>3.340</td>\n",
       "      <td>0.286650</td>\n",
       "      <td>0.319100</td>\n",
       "      <td>0.353860</td>\n",
       "      <td>1.633280</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>Syria</td>\n",
       "      <td>Middle East and Northern Africa</td>\n",
       "      <td>156</td>\n",
       "      <td>3.006</td>\n",
       "      <td>0.663200</td>\n",
       "      <td>0.721930</td>\n",
       "      <td>0.474890</td>\n",
       "      <td>0.328580</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>Burundi</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>157</td>\n",
       "      <td>2.905</td>\n",
       "      <td>0.015300</td>\n",
       "      <td>0.223960</td>\n",
       "      <td>0.415870</td>\n",
       "      <td>1.833020</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>Togo</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>158</td>\n",
       "      <td>2.839</td>\n",
       "      <td>0.208680</td>\n",
       "      <td>0.284430</td>\n",
       "      <td>0.139950</td>\n",
       "      <td>1.567260</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>Benin</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>153</td>\n",
       "      <td>3.484</td>\n",
       "      <td>0.394990</td>\n",
       "      <td>0.210280</td>\n",
       "      <td>0.104190</td>\n",
       "      <td>2.108120</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Southern Asia</td>\n",
       "      <td>154</td>\n",
       "      <td>3.360</td>\n",
       "      <td>0.382270</td>\n",
       "      <td>0.173440</td>\n",
       "      <td>0.110370</td>\n",
       "      <td>2.145580</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>Togo</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>155</td>\n",
       "      <td>3.303</td>\n",
       "      <td>0.281230</td>\n",
       "      <td>0.248110</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.135400</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>Syria</td>\n",
       "      <td>Middle East and Northern Africa</td>\n",
       "      <td>156</td>\n",
       "      <td>3.069</td>\n",
       "      <td>0.747190</td>\n",
       "      <td>0.629940</td>\n",
       "      <td>0.148660</td>\n",
       "      <td>0.817890</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>Burundi</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>157</td>\n",
       "      <td>2.905</td>\n",
       "      <td>0.068310</td>\n",
       "      <td>0.157470</td>\n",
       "      <td>0.234420</td>\n",
       "      <td>2.104040</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>Rwanda</td>\n",
       "      <td>NaN</td>\n",
       "      <td>151</td>\n",
       "      <td>3.471</td>\n",
       "      <td>0.368746</td>\n",
       "      <td>0.326425</td>\n",
       "      <td>0.945707</td>\n",
       "      <td>0.540061</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>Syria</td>\n",
       "      <td>NaN</td>\n",
       "      <td>152</td>\n",
       "      <td>3.462</td>\n",
       "      <td>0.777153</td>\n",
       "      <td>0.500533</td>\n",
       "      <td>0.396103</td>\n",
       "      <td>1.061574</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>Tanzania</td>\n",
       "      <td>NaN</td>\n",
       "      <td>153</td>\n",
       "      <td>3.349</td>\n",
       "      <td>0.511136</td>\n",
       "      <td>0.364509</td>\n",
       "      <td>1.041990</td>\n",
       "      <td>0.621130</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>Burundi</td>\n",
       "      <td>NaN</td>\n",
       "      <td>154</td>\n",
       "      <td>2.905</td>\n",
       "      <td>0.091623</td>\n",
       "      <td>0.151611</td>\n",
       "      <td>0.629794</td>\n",
       "      <td>1.683024</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>Central African Republic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>155</td>\n",
       "      <td>2.693</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.018773</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.066005</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>Yemen</td>\n",
       "      <td>NaN</td>\n",
       "      <td>152</td>\n",
       "      <td>3.355</td>\n",
       "      <td>0.442000</td>\n",
       "      <td>0.343000</td>\n",
       "      <td>1.073000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>Tanzania</td>\n",
       "      <td>NaN</td>\n",
       "      <td>153</td>\n",
       "      <td>3.303</td>\n",
       "      <td>0.455000</td>\n",
       "      <td>0.381000</td>\n",
       "      <td>0.991000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>South Sudan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>154</td>\n",
       "      <td>3.254</td>\n",
       "      <td>0.337000</td>\n",
       "      <td>0.177000</td>\n",
       "      <td>0.608000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>Central African Republic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>155</td>\n",
       "      <td>3.083</td>\n",
       "      <td>0.024000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>Burundi</td>\n",
       "      <td>NaN</td>\n",
       "      <td>156</td>\n",
       "      <td>2.905</td>\n",
       "      <td>0.091000</td>\n",
       "      <td>0.145000</td>\n",
       "      <td>0.627000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>Rwanda</td>\n",
       "      <td>NaN</td>\n",
       "      <td>152</td>\n",
       "      <td>3.334</td>\n",
       "      <td>0.359000</td>\n",
       "      <td>0.614000</td>\n",
       "      <td>0.711000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>Tanzania</td>\n",
       "      <td>NaN</td>\n",
       "      <td>153</td>\n",
       "      <td>3.231</td>\n",
       "      <td>0.476000</td>\n",
       "      <td>0.499000</td>\n",
       "      <td>0.885000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>154</td>\n",
       "      <td>3.203</td>\n",
       "      <td>0.350000</td>\n",
       "      <td>0.361000</td>\n",
       "      <td>0.517000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>Central African Republic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>155</td>\n",
       "      <td>3.083</td>\n",
       "      <td>0.026000</td>\n",
       "      <td>0.105000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>South Sudan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>156</td>\n",
       "      <td>2.853</td>\n",
       "      <td>0.306000</td>\n",
       "      <td>0.295000</td>\n",
       "      <td>0.575000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Country                           Region  \\\n",
       "153                    Rwanda               Sub-Saharan Africa   \n",
       "154                     Benin               Sub-Saharan Africa   \n",
       "155                     Syria  Middle East and Northern Africa   \n",
       "156                   Burundi               Sub-Saharan Africa   \n",
       "157                      Togo               Sub-Saharan Africa   \n",
       "152                     Benin               Sub-Saharan Africa   \n",
       "153               Afghanistan                    Southern Asia   \n",
       "154                      Togo               Sub-Saharan Africa   \n",
       "155                     Syria  Middle East and Northern Africa   \n",
       "156                   Burundi               Sub-Saharan Africa   \n",
       "150                    Rwanda                              NaN   \n",
       "151                     Syria                              NaN   \n",
       "152                  Tanzania                              NaN   \n",
       "153                   Burundi                              NaN   \n",
       "154  Central African Republic                              NaN   \n",
       "151                     Yemen                              NaN   \n",
       "152                  Tanzania                              NaN   \n",
       "153               South Sudan                              NaN   \n",
       "154  Central African Republic                              NaN   \n",
       "155                   Burundi                              NaN   \n",
       "151                    Rwanda                              NaN   \n",
       "152                  Tanzania                              NaN   \n",
       "153               Afghanistan                              NaN   \n",
       "154  Central African Republic                              NaN   \n",
       "155               South Sudan                              NaN   \n",
       "\n",
       "     Happiness Rank  Happiness Score  Economy (GDP per Capita)      HALE  \\\n",
       "153             154            3.465                  0.222080  0.428640   \n",
       "154             155            3.340                  0.286650  0.319100   \n",
       "155             156            3.006                  0.663200  0.721930   \n",
       "156             157            2.905                  0.015300  0.223960   \n",
       "157             158            2.839                  0.208680  0.284430   \n",
       "152             153            3.484                  0.394990  0.210280   \n",
       "153             154            3.360                  0.382270  0.173440   \n",
       "154             155            3.303                  0.281230  0.248110   \n",
       "155             156            3.069                  0.747190  0.629940   \n",
       "156             157            2.905                  0.068310  0.157470   \n",
       "150             151            3.471                  0.368746  0.326425   \n",
       "151             152            3.462                  0.777153  0.500533   \n",
       "152             153            3.349                  0.511136  0.364509   \n",
       "153             154            2.905                  0.091623  0.151611   \n",
       "154             155            2.693                  0.000000  0.018773   \n",
       "151             152            3.355                  0.442000  0.343000   \n",
       "152             153            3.303                  0.455000  0.381000   \n",
       "153             154            3.254                  0.337000  0.177000   \n",
       "154             155            3.083                  0.024000  0.010000   \n",
       "155             156            2.905                  0.091000  0.145000   \n",
       "151             152            3.334                  0.359000  0.614000   \n",
       "152             153            3.231                  0.476000  0.499000   \n",
       "153             154            3.203                  0.350000  0.361000   \n",
       "154             155            3.083                  0.026000  0.105000   \n",
       "155             156            2.853                  0.306000  0.295000   \n",
       "\n",
       "     Social Support  Dystopia Residual  Year  \n",
       "153        0.773700           0.670420  2015  \n",
       "154        0.353860           1.633280  2015  \n",
       "155        0.474890           0.328580  2015  \n",
       "156        0.415870           1.833020  2015  \n",
       "157        0.139950           1.567260  2015  \n",
       "152        0.104190           2.108120  2016  \n",
       "153        0.110370           2.145580  2016  \n",
       "154        0.000000           2.135400  2016  \n",
       "155        0.148660           0.817890  2016  \n",
       "156        0.234420           2.104040  2016  \n",
       "150        0.945707           0.540061  2017  \n",
       "151        0.396103           1.061574  2017  \n",
       "152        1.041990           0.621130  2017  \n",
       "153        0.629794           1.683024  2017  \n",
       "154        0.000000           2.066005  2017  \n",
       "151        1.073000                NaN  2018  \n",
       "152        0.991000                NaN  2018  \n",
       "153        0.608000                NaN  2018  \n",
       "154        0.000000                NaN  2018  \n",
       "155        0.627000                NaN  2018  \n",
       "151        0.711000                NaN  2019  \n",
       "152        0.885000                NaN  2019  \n",
       "153        0.517000                NaN  2019  \n",
       "154        0.000000                NaN  2019  \n",
       "155        0.575000                NaN  2019  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#put all years bottom 5 countries in one df\n",
    "bottom5_all = pd.concat([C2015_bottom5,C2016_bottom5,C2017_bottom5,C2018_bottom5,C2019_bottom5])\n",
    "bottom5_all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "8dd3932f-a68d-49d8-adaf-72ba8934a21d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Switzerland', 'Iceland', 'Denmark', 'Norway', 'Canada', 'Finland',\n",
       "       'Netherlands'], dtype=object)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top5_all['Country'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "d48adcd9-0454-4e7c-acda-b3b686cae3c6",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Rwanda', 'Benin', 'Syria', 'Burundi', 'Togo', 'Afghanistan',\n",
       "       'Tanzania', 'Central African Republic', 'Yemen', 'South Sudan'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bottom5_all['Country'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4ac62be-0343-4816-becb-82eb884c93c7",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Part 2: Eveluation on the HALE metric"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "00e47ad4-fae0-4346-86d3-b4413c0ba37f",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      0.94143\n",
       "1      0.94784\n",
       "2      0.87464\n",
       "3      0.88521\n",
       "4      0.90563\n",
       "        ...   \n",
       "752    0.49500\n",
       "753    0.46300\n",
       "754    0.49900\n",
       "755    0.36100\n",
       "756    0.10500\n",
       "Name: HALE, Length: 757, dtype: float64"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# grab the HALE score in all years\n",
    "all_HALE=pd.merge(pd.merge(pd.merge(pd.merge(C2015['HALE'],C2016['HALE'],how='outer',on='HALE'),C2017['HALE'],how='outer',on='HALE'),C2018['HALE'],how='outer',on='HALE'),C2019['HALE'],how='outer',on='HALE')\n",
    "all_list=all_HALE['HALE']\n",
    "all_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "1388d972-ddf5-4d7a-b841-123972397bca",
   "metadata": {},
   "outputs": [],
   "source": [
    "#find the lowerq and upperq of all HALE scores\n",
    "quartiles = all_list.quantile([.25,.5,.75])\n",
    "lowerq = quartiles[0.25]\n",
    "upperq = quartiles[0.75]\n",
    "lowerq \n",
    "iqr = upperq-lowerq\n",
    "lower_bound = lowerq - (1.5*iqr)\n",
    "upper_bound = upperq + (1.5*iqr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "a9b41539-c77f-45a7-ba52-13601a068de9",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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8HgAAwFxBn7mRzt6079FHH+3SCzc0NKi1tVUxMTF+6zExMaqrq+vwmP3792vHjh2KjIzUa6+9poaGBi1YsEBffvnlea+7KSoq0tKlS7s0KwAA6DsCPnPz5Zdf6vPPP/db++STT3TXXXdp1qxZeuWVVzo1gM1m89v2er3t1s5pa2uTzWbThg0bNHHiRE2fPl0rV67U+vXrz3v2pqCgQI2Njb7H4cOHOzUnAADoGwKOm4ULF2rlypW+7fr6eqWnp+sf//iHmpubNXfuXL300ksBv3B0dLTCw8PbnaWpr69vdzbnnFGjRmn06NFyOBy+tcTERHm93nbhdY7dbldUVJTfAwAAmCvguPnggw80c+ZM3/aLL76o4cOHq7q6Wm+88YYeffRRrVq1KuAXHjhwoJxOpyoqKvzWKyoqlJqa2uExaWlpOnLkiI4fP+5b27t3r8LCwnTppZcG/NoAAMBcAcdNXV2dEhISfNvvvPOOfvCDHygi4uxlOzNnztS+ffuCevH8/HytXbtWpaWlqqmpUV5enmpra5Wbmyvp7FtK2dnZvv1nz56tESNG6K677tLu3bu1bds23X///br77rs1aNCgoF4bAACYKeALiqOionTs2DHFx8dLkj788EPl5OT4fm6z2dTc3BzUi2dlZeno0aNatmyZ3G63kpKSVF5e7nsNt9vtd8+bIUOGqKKiQr/4xS+UkpKiESNGaNasWXrkkUeCel0AAGCugONm4sSJevrpp/XCCy9o8+bNampq0tSpU30/37t3r+Li4oIeYMGCBVqwYEGHP1u/fn27tfHjx7d7KwsAAOCcgOPmd7/7nW6++Wa9/PLLamlp0QMPPKBvfOMbvp//8Y9/1JQpU7plSAAAgEAFHDcTJkxQTU2NKisrNXLkSE2aNMnv57fffruuvvrqkA8IAAAQjKBu4nfxxRfre9/7Xoc/u/XWW0MyEAAAQFcE/fULAAAAvRlxAwAAjELcAAAAoxA3AADAKAHHzaZNm3TmzBnf9sGDB9Xa2urbPnnypJYvXx7a6QAAAIIUcNz8+Mc/1rFjx3zb1157rQ4dOuTbbmpqUkFBQUiHAwAACFbAceP1ei+4DQAA0BtwzQ0AADAKcQMAAIwS1B2Kt2zZIofDIUlqa2vT22+/rX/+85+S5Hc9DgAAgFWCips5c+b4bc+fPz+kwwAAAHRVwHHT1tbWnXMAAACERMiuufn3v/+tZcuWherpAAAAOiVkcVNXV6elS5eG6ukAAAA6hU9LAQAAoxA3AADAKMQNAAAwSsCflsrPz7/gz//zn/90eRgAAICuCjhuqqqqvnafG2+8sUvDAAAAdFXAcfO3v/2tO+cAAAAIiZBdc/Pxxx9r8eLFoXo6AACATulS3Hg8Hj333HOaOHGirrvuOr377rshGgsAAKBzOhU3W7duVXZ2tkaNGqUFCxZo6tSp2rt3r6qrq0M8HgAAQHACjhu3261HH31UV1xxhW6//XZFR0dr69atCgsLU3Z2tq644orunBMAACAgAV9QnJCQoB/96EdatWqVpk2bprAwbpEDAAB6n4ALJT4+Xjt27NC2bdu0d+/e7pwJAACg0wKOm08//VQvv/yy3G63vvWtb8npdOrJJ5+UJNlstm4bEAAAIBhBvbeUlpam0tJSud1u5ebmatOmTWptbdWCBQv0wgsvcJdiAABguU5dODNkyBDdc889ev/99/XJJ5/I6XTqN7/5jWJjY0M9HwAAQFC6fFVwYmKiVqxYoc8//1xlZWWhmAkAAKDTAv60lMfj+dp9br755i4NAwAA0FUBx82wYcMueOGw1+uVzWZTa2trSAYDAADojE59cabX69X06dO1du1ajR49ulsGAwAA6IyA42bKlCl+2+Hh4br++ut12WWXhXwoAACAzuI2wwAAwCgBn7kBcH4nz7RYPUKPO93SJtsAu063tPXL3z8Y/fnfavBA/syg59m8Xq+3MwcOHTpUH330kRISEkI9U7fyeDxyOBxqbGxUVFSU1ePAEGOX/K/VIwC90sHHbrV6BBgimL/fASf1D3/4Q7/t06dPKzc3VxdddJHf+ubNm4MYFQAAILQCjhuHw+G3fccdd4R8GKCv2r3sFqtH6HFHG47qL//zP7rqqqs0ePAgq8eRdPY78HJycrRu3TqNGzfO6nH8DBkyVFdccbnVYwD9QqffluqreFsKMNeuXbvkdDrlcrmUnJxs9TgAQiiYv998WgoAABiFuAEAAEYhbgAAgFGIGwAAYBTiBgAAGMXyuFm9erUSEhIUGRkpp9Op7du3B3Tce++9p4iICE2YMKF7BwQAAH2KpXFTVlamxYsXq7CwUFVVVUpPT1dmZqZqa2sveFxjY6Oys7P1ne98p4cmBQAAfYWlcbNy5Url5ORo3rx5SkxMVHFxseLi4lRSUnLB4+bPn6/Zs2dr8uTJPTQpAADoKyyLmzNnzsjlcikjI8NvPSMjQ5WVlec97g9/+IM+++wzPfTQQwG9TnNzszwej98DAACYy7K4aWhoUGtrq2JiYvzWY2JiVFdX1+Ex+/bt05IlS7RhwwZFRAT2zRFFRUVyOBy+R1xcXJdnBwAAvZflFxTbbDa/ba/X225NklpbWzV79mwtXbpUV111VcDPX1BQoMbGRt/j8OHDXZ4ZAAD0XgF/cWaoRUdHKzw8vN1Zmvr6+nZncySpqalJO3fuVFVVlX7+859Lktra2uT1ehUREaG33npLU6dObXec3W6X3W7vnl8CAAD0OpaduRk4cKCcTqcqKir81isqKpSamtpu/6ioKH388ceqrq72PXJzczVu3DhVV1dr0qRJPTU6AADoxSw7cyNJ+fn5uvPOO5WSkqLJkyfr+eefV21trXJzcyWdfUvpiy++0IsvvqiwsDAlJSX5HX/JJZcoMjKy3ToAAOi/LI2brKwsHT16VMuWLZPb7VZSUpLKy8sVHx8vSXK73V97zxsAAID/ZvN6vV6rh+hJHo9HDodDjY2NioqKsnocACG0a9cuOZ1OuVwuJScnWz0OgBAK5u+35Z+WAgAACCXiBgAAGIW4AQAARiFuAACAUYgbAABgFOIGAAAYhbgBAABGIW4AAIBRiBsAAGAU4gYAABiFuAEAAEYhbgAAgFGIGwAAYBTiBgAAGIW4AQAARiFuAACAUYgbAABgFOIGAAAYhbgBAABGIW4AAIBRiBsAAGAU4gYAABiFuAEAAEYhbgAAgFGIGwAAYBTiBgAAGIW4AQAARiFuAACAUYgbAABgFOIGAAAYhbgBAABGIW4AAIBRiBsAAGAU4gYAABiFuAEAAEYhbgAAgFGIGwAAYBTiBgAAGIW4AQAARiFuAACAUYgbAABgFOIGAAAYhbgBAABGIW4AAIBRiBsAAGAU4gYAABiFuAEAAEaxPG5Wr16thIQERUZGyul0avv27efdd/PmzZo2bZouvvhiRUVFafLkydqyZUsPTgsAAHo7S+OmrKxMixcvVmFhoaqqqpSenq7MzEzV1tZ2uP+2bds0bdo0lZeXy+Vy6aabbtKMGTNUVVXVw5MDAIDeyub1er1WvfikSZOUnJyskpIS31piYqK+//3vq6ioKKDnuOaaa5SVlaUHH3wwoP09Ho8cDocaGxsVFRXVqbkB9E67du2S0+mUy+VScnKy1eMACKFg/n5bdubmzJkzcrlcysjI8FvPyMhQZWVlQM/R1tampqYmDR8+vDtGBAAAfVCEVS/c0NCg1tZWxcTE+K3HxMSorq4uoOd44okndOLECc2aNeu8+zQ3N6u5udm37fF4OjcwAADoEyy/oNhms/lte73edmsd2bhxox5++GGVlZXpkksuOe9+RUVFcjgcvkdcXFyXZwYAAL2XZXETHR2t8PDwdmdp6uvr253N+b/KysqUk5OjTZs26eabb77gvgUFBWpsbPQ9Dh8+3OXZAQBA72VZ3AwcOFBOp1MVFRV+6xUVFUpNTT3vcRs3btTcuXP1yiuv6NZbb/3a17Hb7YqKivJ7AAAAc1l2zY0k5efn684771RKSoomT56s559/XrW1tcrNzZV09qzLF198oRdffFHS2bDJzs7WU089peuvv9531mfQoEFyOByW/R4AAKD3sDRusrKydPToUS1btkxut1tJSUkqLy9XfHy8JMntdvvd8+a5555TS0uLFi5cqIULF/rW58yZo/Xr1/f0+AAAoBey9D43VuA+N4C5uM8NYK4+cZ8bAACA7kDcAAAAoxA3AADAKMQNAAAwCnEDAACMQtwAAACjEDcAAMAoxA0AADAKcQMAAIxC3AAAAKMQNwAAwCjEDQAAMApxAwAAjELcAAAAoxA3AADAKMQNAAAwCnEDAACMQtwAAACjEDcAAMAoxA0AADAKcQMAAIxC3AAAAKMQNwAAwCjEDQAAMApxAwAAjELcAAAAoxA3AADAKMQNAAAwCnEDAACMQtwAAACjEDcAAMAoxA0AADAKcQMAAIxC3AAAAKMQNwAAwCjEDQAAMApxAwAAjELcAAAAoxA3AADAKMQNAAAwCnEDAACMQtwAAACjEDcAAMAoxA0AADAKcQMAAIxC3AAAAKMQNwAAwCjEDQAAMIrlcbN69WolJCQoMjJSTqdT27dvv+D+W7duldPpVGRkpC677DKtWbOmhyYFAAB9gaVxU1ZWpsWLF6uwsFBVVVVKT09XZmamamtrO9z/wIEDmj59utLT01VVVaUHHnhAixYt0quvvtrDkwMAgN7K0rhZuXKlcnJyNG/ePCUmJqq4uFhxcXEqKSnpcP81a9ZozJgxKi4uVmJioubNm6e7775bK1as6OHJAQBAbxVh1QufOXNGLpdLS5Ys8VvPyMhQZWVlh8e8//77ysjI8Fu75ZZbtG7dOn311VcaMGBAu2Oam5vV3Nzs2/Z4PCGYHkAonTx5Unv27Ony89TU1Pj9ZyiMHz9egwcPDtnzAeh+lsVNQ0ODWltbFRMT47ceExOjurq6Do+pq6vrcP+WlhY1NDRo1KhR7Y4pKirS0qVLQzc4gJDbs2ePnE5nyJ7vjjvuCNlzuVwuJScnh+z5AHQ/y+LmHJvN5rft9XrbrX3d/h2tn1NQUKD8/HzftsfjUVxcXGfHBdANxo8fL5fL1eXnOXXqlA4ePKixY8dq0KBBIZjs7GwA+hbL4iY6Olrh4eHtztLU19e3OztzzsiRIzvcPyIiQiNGjOjwGLvdLrvdHpqhAXSLwYMHh+zsSFpaWkieB0DfZdkFxQMHDpTT6VRFRYXfekVFhVJTUzs8ZvLkye32f+utt5SSktLh9TYAAKD/sfTTUvn5+Vq7dq1KS0tVU1OjvLw81dbWKjc3V9LZt5Sys7N9++fm5urQoUPKz89XTU2NSktLtW7dOt13331W/QoAAKCXsfSam6ysLB09elTLli2T2+1WUlKSysvLFR8fL0lyu91+97xJSEhQeXm58vLytGrVKsXGxurpp5/WbbfdZtWvAAAAehmb99wVuf2Ex+ORw+FQY2OjoqKirB4HAAAEIJi/35Z//QIAAEAoETcAAMAoxA0AADAKcQMAAIxC3AAAAKMQNwAAwCjEDQAAMApxAwAAjELcAAAAo1j69QtWOHdDZo/HY/EkAAAgUOf+bgfyxQr9Lm6ampokSXFxcRZPAgAAgtXU1CSHw3HBffrdd0u1tbXpyJEjGjp0qGw2m9XjAAghj8ejuLg4HT58mO+OAwzj9XrV1NSk2NhYhYVd+Kqafhc3AMzFF+MCkLigGAAAGIa4AQAARiFuABjDbrfroYcekt1ut3oUABbimhsAAGAUztwAAACjEDcAAMAoxA0AADAKcQMAAIxC3AAwwrZt2zRjxgzFxsbKZrPp9ddft3okABYhbgAY4cSJE7ruuuv07LPPWj0KAIv1uy/OBGCmzMxMZWZmWj0GgF6AMzcAAMAoxA0AADAKcQMAAIxC3AAAAKMQNwAAwCh8WgqAEY4fP65//etfvu0DBw6ourpaw4cP15gxYyycDEBP41vBARjh3Xff1U033dRufc6cOVq/fn3PDwTAMsQNAAAwCtfcAAAAoxA3AADAKMQNAAAwCnEDAACMQtwAAACjEDcAAMAoxA0AADAKcQMAAIxC3AAAAKMQNwAAwCjEDQAAMApxAwAAjPL/AEVWYQeCACGcAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot\n",
    "fig1, ax0 = plt.subplots()\n",
    "\n",
    "ax0.set_ylabel('HALE Score')\n",
    "ax0.boxplot(all_list)\n",
    "ax0.hlines(y=lowerq, xmin=0.8, xmax=1.2)\n",
    "ax0.hlines(y=upperq, xmin=0.8, xmax=1.2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "d85b1171-1c1f-4a9a-a84b-40f5a69b377c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save to image\n",
    "fig1.savefig('HALE Score Metric.png', dpi=300, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "b63e74f6-6e1b-498c-bc13-05c81364efa5",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.941430\n",
       "1    0.947840\n",
       "2    0.874640\n",
       "3    0.885210\n",
       "4    0.905630\n",
       "0    0.795040\n",
       "1    0.863030\n",
       "2    0.867330\n",
       "3    0.795790\n",
       "4    0.810910\n",
       "0    0.796667\n",
       "1    0.792566\n",
       "2    0.833552\n",
       "3    0.858131\n",
       "4    0.809158\n",
       "0    0.874000\n",
       "1    0.861000\n",
       "2    0.868000\n",
       "3    0.914000\n",
       "4    0.927000\n",
       "0    0.986000\n",
       "1    0.996000\n",
       "2    1.028000\n",
       "3    1.026000\n",
       "4    0.999000\n",
       "Name: HALE, dtype: float64"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top5_all['HALE'].round(6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c9f125f0-2b3f-4260-9e0b-8ecd064203c4",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true,
     "source_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.79504, 0.79579, 0.796667, 0.792566, 0.741, 0.763, 0.772, 0.753]\n"
     ]
    }
   ],
   "source": [
    "#Create a list for all the top countries' low HALE scores\n",
    "ou=[]\n",
    "for i in top5_all['HALE'].round(6):\n",
    "    if i < upperq:\n",
    "        ou.append(i)\n",
    "print(ou)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "9133d079-8c3f-4839-a186-b1d920cd52a5",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#for i in ou:\n",
    "#    out = top5_all.loc[top5_all['HALE']==i]\n",
    "#    print(out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "3f0a57b0-f473-4640-bdf3-36f5422c754b",
   "metadata": {
    "tags": []
   },
   "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>Economy (GDP per Capita)</th>\n",
       "      <th>HALE</th>\n",
       "      <th>Social Support</th>\n",
       "      <th>Dystopia Residual</th>\n",
       "      <th>Year</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>1.396510</td>\n",
       "      <td>0.941430</td>\n",
       "      <td>1.349510</td>\n",
       "      <td>2.517380</td>\n",
       "      <td>2015</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>1.302320</td>\n",
       "      <td>0.947840</td>\n",
       "      <td>1.402230</td>\n",
       "      <td>2.702010</td>\n",
       "      <td>2015</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>1.325480</td>\n",
       "      <td>0.874640</td>\n",
       "      <td>1.360580</td>\n",
       "      <td>2.492040</td>\n",
       "      <td>2015</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>1.459000</td>\n",
       "      <td>0.885210</td>\n",
       "      <td>1.330950</td>\n",
       "      <td>2.465310</td>\n",
       "      <td>2015</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>1.326290</td>\n",
       "      <td>0.905630</td>\n",
       "      <td>1.322610</td>\n",
       "      <td>2.451760</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Denmark</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>1</td>\n",
       "      <td>7.526</td>\n",
       "      <td>1.441780</td>\n",
       "      <td>0.795040</td>\n",
       "      <td>1.163740</td>\n",
       "      <td>2.739390</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Switzerland</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>2</td>\n",
       "      <td>7.509</td>\n",
       "      <td>1.527330</td>\n",
       "      <td>0.863030</td>\n",
       "      <td>1.145240</td>\n",
       "      <td>2.694630</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Iceland</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>3</td>\n",
       "      <td>7.501</td>\n",
       "      <td>1.426660</td>\n",
       "      <td>0.867330</td>\n",
       "      <td>1.183260</td>\n",
       "      <td>2.831370</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Norway</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>4</td>\n",
       "      <td>7.498</td>\n",
       "      <td>1.577440</td>\n",
       "      <td>0.795790</td>\n",
       "      <td>1.126900</td>\n",
       "      <td>2.664650</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Finland</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>5</td>\n",
       "      <td>7.413</td>\n",
       "      <td>1.405980</td>\n",
       "      <td>0.810910</td>\n",
       "      <td>1.134640</td>\n",
       "      <td>2.825960</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Norway</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>7.537</td>\n",
       "      <td>1.616463</td>\n",
       "      <td>0.796667</td>\n",
       "      <td>1.533524</td>\n",
       "      <td>2.277027</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Denmark</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>7.522</td>\n",
       "      <td>1.482383</td>\n",
       "      <td>0.792566</td>\n",
       "      <td>1.551122</td>\n",
       "      <td>2.313707</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Iceland</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>7.504</td>\n",
       "      <td>1.480633</td>\n",
       "      <td>0.833552</td>\n",
       "      <td>1.610574</td>\n",
       "      <td>2.322715</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Switzerland</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>7.494</td>\n",
       "      <td>1.564980</td>\n",
       "      <td>0.858131</td>\n",
       "      <td>1.516912</td>\n",
       "      <td>2.276716</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Finland</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>7.469</td>\n",
       "      <td>1.443572</td>\n",
       "      <td>0.809158</td>\n",
       "      <td>1.540247</td>\n",
       "      <td>2.430182</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Finland</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>7.632</td>\n",
       "      <td>1.305000</td>\n",
       "      <td>0.874000</td>\n",
       "      <td>1.592000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Norway</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>7.594</td>\n",
       "      <td>1.456000</td>\n",
       "      <td>0.861000</td>\n",
       "      <td>1.582000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Denmark</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>7.555</td>\n",
       "      <td>1.351000</td>\n",
       "      <td>0.868000</td>\n",
       "      <td>1.590000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Iceland</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>7.495</td>\n",
       "      <td>1.343000</td>\n",
       "      <td>0.914000</td>\n",
       "      <td>1.644000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Switzerland</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>7.487</td>\n",
       "      <td>1.420000</td>\n",
       "      <td>0.927000</td>\n",
       "      <td>1.549000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Finland</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>7.769</td>\n",
       "      <td>1.340000</td>\n",
       "      <td>0.986000</td>\n",
       "      <td>1.587000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Denmark</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>7.600</td>\n",
       "      <td>1.383000</td>\n",
       "      <td>0.996000</td>\n",
       "      <td>1.573000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Norway</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>7.554</td>\n",
       "      <td>1.488000</td>\n",
       "      <td>1.028000</td>\n",
       "      <td>1.582000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Iceland</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>7.494</td>\n",
       "      <td>1.380000</td>\n",
       "      <td>1.026000</td>\n",
       "      <td>1.624000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Netherlands</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>7.488</td>\n",
       "      <td>1.396000</td>\n",
       "      <td>0.999000</td>\n",
       "      <td>1.522000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Country          Region  Happiness Rank  Happiness Score  \\\n",
       "0  Switzerland  Western Europe               1            7.587   \n",
       "1      Iceland  Western Europe               2            7.561   \n",
       "2      Denmark  Western Europe               3            7.527   \n",
       "3       Norway  Western Europe               4            7.522   \n",
       "4       Canada   North America               5            7.427   \n",
       "0      Denmark  Western Europe               1            7.526   \n",
       "1  Switzerland  Western Europe               2            7.509   \n",
       "2      Iceland  Western Europe               3            7.501   \n",
       "3       Norway  Western Europe               4            7.498   \n",
       "4      Finland  Western Europe               5            7.413   \n",
       "0       Norway             NaN               1            7.537   \n",
       "1      Denmark             NaN               2            7.522   \n",
       "2      Iceland             NaN               3            7.504   \n",
       "3  Switzerland             NaN               4            7.494   \n",
       "4      Finland             NaN               5            7.469   \n",
       "0      Finland             NaN               1            7.632   \n",
       "1       Norway             NaN               2            7.594   \n",
       "2      Denmark             NaN               3            7.555   \n",
       "3      Iceland             NaN               4            7.495   \n",
       "4  Switzerland             NaN               5            7.487   \n",
       "0      Finland             NaN               1            7.769   \n",
       "1      Denmark             NaN               2            7.600   \n",
       "2       Norway             NaN               3            7.554   \n",
       "3      Iceland             NaN               4            7.494   \n",
       "4  Netherlands             NaN               5            7.488   \n",
       "\n",
       "   Economy (GDP per Capita)      HALE  Social Support  Dystopia Residual  Year  \n",
       "0                  1.396510  0.941430        1.349510           2.517380  2015  \n",
       "1                  1.302320  0.947840        1.402230           2.702010  2015  \n",
       "2                  1.325480  0.874640        1.360580           2.492040  2015  \n",
       "3                  1.459000  0.885210        1.330950           2.465310  2015  \n",
       "4                  1.326290  0.905630        1.322610           2.451760  2015  \n",
       "0                  1.441780  0.795040        1.163740           2.739390  2016  \n",
       "1                  1.527330  0.863030        1.145240           2.694630  2016  \n",
       "2                  1.426660  0.867330        1.183260           2.831370  2016  \n",
       "3                  1.577440  0.795790        1.126900           2.664650  2016  \n",
       "4                  1.405980  0.810910        1.134640           2.825960  2016  \n",
       "0                  1.616463  0.796667        1.533524           2.277027  2017  \n",
       "1                  1.482383  0.792566        1.551122           2.313707  2017  \n",
       "2                  1.480633  0.833552        1.610574           2.322715  2017  \n",
       "3                  1.564980  0.858131        1.516912           2.276716  2017  \n",
       "4                  1.443572  0.809158        1.540247           2.430182  2017  \n",
       "0                  1.305000  0.874000        1.592000                NaN  2018  \n",
       "1                  1.456000  0.861000        1.582000                NaN  2018  \n",
       "2                  1.351000  0.868000        1.590000                NaN  2018  \n",
       "3                  1.343000  0.914000        1.644000                NaN  2018  \n",
       "4                  1.420000  0.927000        1.549000                NaN  2018  \n",
       "0                  1.340000  0.986000        1.587000                NaN  2019  \n",
       "1                  1.383000  0.996000        1.573000                NaN  2019  \n",
       "2                  1.488000  1.028000        1.582000                NaN  2019  \n",
       "3                  1.380000  1.026000        1.624000                NaN  2019  \n",
       "4                  1.396000  0.999000        1.522000                NaN  2019  "
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top5_all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "d7404c79-5fbb-4c4d-a8dd-a0dd45bda897",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Get the top 5 country all variables\n",
    "top5_Switzerland = top5_all.loc[top5_all['Country']=='Switzerland']\n",
    "top5_Iceland = top5_all.loc[top5_all['Country']=='Iceland'] \n",
    "top5_Denmark = top5_all.loc[top5_all['Country']=='Denmark'] \n",
    "top5_Norway = top5_all.loc[top5_all['Country']=='Norway'] \n",
    "top5_Canada = top5_all.loc[top5_all['Country']=='Canada'] \n",
    "top5_Finland = top5_all.loc[top5_all['Country']=='Finland'] \n",
    "top5_Netherlands = top5_all.loc[top5_all['Country']=='Netherlands'] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "94bbe44b-b809-49cf-8cdd-d4e0af4b8f6a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Rwanda', 'Benin', 'Syria', 'Burundi', 'Togo', 'Afghanistan',\n",
       "       'Tanzania', 'Central African Republic', 'Yemen', 'South Sudan'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bottom5_all['Country'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "96a74914-20aa-4e07-9684-e9968404180d",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Get the bottom 5 country all variables\n",
    "bottom5_Rwanda = bottom5_all.loc[bottom5_all['Country']=='Rwanda']\n",
    "bottom5_Benin = bottom5_all.loc[bottom5_all['Country']=='Benin'] \n",
    "bottom5_Syria = bottom5_all.loc[bottom5_all['Country']=='Syria'] \n",
    "bottom5_Burundi = bottom5_all.loc[bottom5_all['Country']=='Burundi'] \n",
    "bottom5_Togo = bottom5_all.loc[bottom5_all['Country']=='Togo'] \n",
    "bottom5_Afghanistan = bottom5_all.loc[bottom5_all['Country']=='Afghanistan'] \n",
    "bottom5_Tanzania = bottom5_all.loc[bottom5_all['Country']=='Tanzania'] \n",
    "bottom5_CAR = bottom5_all.loc[bottom5_all['Country']=='Central African Republic'] \n",
    "bottom5_Yemen = bottom5_all.loc[bottom5_all['Country']=='Yemen'] \n",
    "bottom5_SS = bottom5_all.loc[bottom5_all['Country']=='South Sudan'] \n",
    "#bottom5_Zimbabwe = bottom5_all.loc[bottom5_all['Country']=='Zimbabwe'] \n",
    "#bottom5_Lesotho = bottom5_all.loc[bottom5_all['Country']=='Lesotho'] \n",
    "#bottom5_Botswana = bottom5_all.loc[bottom5_all['Country']=='Botswana'] \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "e5b5049f-0643-4d24-acfd-23f44bd0c0de",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generate a scatter plot that shows the HALE score for each top 5 countries for each year.\n",
    "top5_Switzerland_HALE = top5_Switzerland[['Country','HALE','Year']] \n",
    "top5_Iceland_HALE = top5_Iceland[['Country','HALE','Year']] \n",
    "top5_Denmark_HALE = top5_Denmark[['Country','HALE','Year']] \n",
    "top5_Norway_HALE = top5_Norway[['Country','HALE','Year']] \n",
    "top5_Canada_HALE = top5_Canada[['Country','HALE','Year']] \n",
    "top5_Finland_HALE = top5_Finland[['Country','HALE','Year']] \n",
    "top5_Netherlands_HALE = top5_Netherlands[['Country','HALE','Year']] \n",
    "\n",
    "ax_S=top5_Switzerland_HALE.plot.scatter(x='Year', y='HALE', c='red', s=80)\n",
    "ax_I=top5_Iceland_HALE.plot.scatter(x='Year', y='HALE', c='yellow',ax=ax_S, s=80)\n",
    "ax_D=top5_Denmark_HALE.plot.scatter(x='Year', y='HALE', c='green',ax=ax_S, s=80)\n",
    "ax_NO=top5_Norway_HALE.plot.scatter(x='Year', y='HALE', c='blue',ax=ax_S, s=80)\n",
    "ax_C=top5_Canada_HALE.plot.scatter(x='Year', y='HALE', c='orange',ax=ax_S, s=80)\n",
    "ax_F=top5_Finland_HALE.plot.scatter(x='Year', y='HALE', c='pink', ax=ax_S, s=80)\n",
    "ax_NE=top5_Netherlands_HALE.plot.scatter(x='Year', y='HALE', c='purple',ax=ax_S, s=80)\n",
    "\n",
    "print(ax_S == ax_I == ax_D == ax_C == ax_F == ax_NO == ax_NE)\n",
    "\n",
    "x_axis = np.arange(len(top5_all))\n",
    "plt.title ('Top 5 Countries Healthy Life Expectancy Score by Year') \n",
    "plt.ylabel('Healthy Life Expectancy Score')\n",
    "plt.hlines(y=upperq,xmin=-1, xmax=5, linewidth=1.5, color='red')\n",
    "#plt.legend(loc='upper center')\n",
    "#plt.xlim(-1,7)\n",
    "\n",
    "#plt.show()\n",
    "plt.tight_layout()\n",
    "\n",
    "# Save to image_no legend\n",
    "plt.savefig('Top 5 Countries Healthy Life Expectancy Score by Year_no legend.png', dpi=300, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d302f1b5-6400-4a05-889a-6655413393ec",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generate a scatter plot that shows the HALE score for each top 5 countries for each year.\n",
    "top5_Switzerland_HALE = top5_Switzerland[['Country','HALE','Year']] \n",
    "top5_Iceland_HALE = top5_Iceland[['Country','HALE','Year']] \n",
    "top5_Denmark_HALE = top5_Denmark[['Country','HALE','Year']] \n",
    "top5_Norway_HALE = top5_Norway[['Country','HALE','Year']] \n",
    "top5_Canada_HALE = top5_Canada[['Country','HALE','Year']] \n",
    "top5_Finland_HALE = top5_Finland[['Country','HALE','Year']] \n",
    "top5_Netherlands_HALE = top5_Netherlands[['Country','HALE','Year']] \n",
    "\n",
    "ax_S=top5_Switzerland_HALE.plot.scatter(x='Year', y='HALE', c='red', label='Switzerland', s=30)\n",
    "ax_I=top5_Iceland_HALE.plot.scatter(x='Year', y='HALE', c='yellow',label='Iceland', ax=ax_S, s=30)\n",
    "ax_D=top5_Denmark_HALE.plot.scatter(x='Year', y='HALE', c='green', label='Denmark',ax=ax_S, s=30)\n",
    "ax_NO=top5_Norway_HALE.plot.scatter(x='Year', y='HALE', c='blue', label='Norwayd',ax=ax_S, s=30)\n",
    "ax_C=top5_Canada_HALE.plot.scatter(x='Year', y='HALE', c='orange', label='Canada',ax=ax_S, s=30)\n",
    "ax_F=top5_Finland_HALE.plot.scatter(x='Year', y='HALE', c='pink',label='Finland', ax=ax_S, s=30)\n",
    "ax_NE=top5_Netherlands_HALE.plot.scatter(x='Year', y='HALE', c='purple', label='Netherlands',ax=ax_S, s=30)\n",
    "\n",
    "print(ax_S == ax_I == ax_D == ax_C == ax_F == ax_NO == ax_NE)\n",
    "\n",
    "x_axis = np.arange(len(top5_all))\n",
    "plt.title ('Top 5 Countries Healthy Life Expectancy Score by Year') \n",
    "plt.ylabel('Healthy Life Expectancy Score')\n",
    "plt.hlines(y=upperq,xmin=-1, xmax=5, linewidth=1.5, color='red')\n",
    "plt.legend(loc='upper center')\n",
    "#plt.xlim(-1,7)\n",
    "\n",
    "#plt.show()\n",
    "plt.tight_layout()\n",
    "\n",
    "# Save to image\n",
    "plt.savefig('Top 5 Countries Healthy Life Expectancy Score by Year.png', dpi=300, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "5b9a3608-ff81-4300-af77-cc11bfce0a48",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generate a scatter plot that shows the HALE score for each bottom 5 countries for each year.\n",
    "bottom5_Rwanda_HALE = bottom5_Rwanda[['Country','HALE','Year']] \n",
    "bottom5_Benin_HALE = bottom5_Benin[['Country','HALE','Year']] \n",
    "bottom5_Syria_HALE = bottom5_Syria[['Country','HALE','Year']] \n",
    "bottom5_Burundi_HALE = bottom5_Burundi[['Country','HALE','Year']] \n",
    "bottom5_Togo_HALE = bottom5_Togo[['Country','HALE','Year']] \n",
    "bottom5_Afghanistan_HALE = bottom5_Afghanistan[['Country','HALE','Year']] \n",
    "bottom5_Tanzania_HALE = bottom5_Tanzania[['Country','HALE','Year']] \n",
    "bottom5_CAR_HALE = bottom5_CAR[['Country','HALE','Year']] \n",
    "bottom5_Yemen_HALE = bottom5_Yemen[['Country','HALE','Year']] \n",
    "bottom5_SS_HALE = bottom5_SS[['Country','HALE','Year']] \n",
    "#bottom5_Zimbabwe_HALE = bottom5_Zimbabwe[['Country','HALE','Year']] \n",
    "#bottom5_Lesotho_HALE = bottom5_Lesotho[['Country','HALE','Year']] \n",
    "#bottom5_Botswana_HALE = bottom5_Botswana[['Country','HALE','Year']] \n",
    "\n",
    "ax_Sy=bottom5_Syria_HALE.plot.scatter(x='Year', y='HALE', c='green',label='Syria', s=30)\n",
    "ax_SS=bottom5_SS_HALE.plot.scatter(x='Year', y='HALE', c='black', ax=ax_Sy,label='South Sudan', s=30)\n",
    "ax_Rw=bottom5_Rwanda_HALE.plot.scatter(x='Year', y='HALE', c='red', ax=ax_Sy,label='Rwanda', s=30)\n",
    "ax_Be=bottom5_Benin_HALE.plot.scatter(x='Year', y='HALE', c='yellow', ax=ax_Sy,label='Benin', s=30)\n",
    "ax_Bu=bottom5_Burundi_HALE.plot.scatter(x='Year', y='HALE', c='blue', ax=ax_Sy,label='Burundi', s=30)\n",
    "ax_To=bottom5_Togo_HALE.plot.scatter(x='Year', y='HALE', c='orange', ax=ax_Sy,label='Togo', s=30)\n",
    "ax_Af=bottom5_Afghanistan_HALE.plot.scatter(x='Year', y='HALE', c='pink', ax=ax_Sy,label='Afghanistan', s=30)\n",
    "ax_Ta=bottom5_Tanzania_HALE.plot.scatter(x='Year', y='HALE', c='purple', ax=ax_Sy,label='Tanzania', s=30)\n",
    "ax_CAR=bottom5_CAR_HALE.plot.scatter(x='Year', y='HALE', c='cyan', ax=ax_Sy,label='Central African Republic', s=30)\n",
    "ax_Ye=bottom5_Yemen_HALE.plot.scatter(x='Year', y='HALE', c='magenta', ax=ax_Sy,label='Yemen', s=30)\n",
    "\n",
    "#ax_Zi=bottom5_Zimbabwe_HALE.plot.scatter(x='Year', y='HALE', c='purple', ax=ax_Rw)\n",
    "#ax_Le=bottom5_Lesotho_HALE.plot.scatter(x='Year', y='HALE', c='purple', ax=ax_Rw)\n",
    "#ax_Bo=bottom5_Botswana_HALE.plot.scatter(x='Year', y='HALE', c='purple', ax=ax_Rw)\n",
    "\n",
    "print(ax_Rw == ax_Be == ax_Sy == ax_Bu == ax_To == ax_Af == ax_Ta == ax_CAR == ax_Ye == ax_SS)\n",
    "\n",
    "x_axis = bottom5_all['Year']\n",
    "#plt.xlim(2015, len(x_axis))\n",
    "plt.title ('Bottom 5 Countries Healthy Life Expectancy Score by Year') \n",
    "plt.ylabel('Healthy Life Expectancy Score')\n",
    "plt.hlines(y=lowerq,xmin=-1, xmax=5,linewidth=1.5, color='red')\n",
    "plt.legend(loc='upper right')\n",
    "#plt.xlim(-2,20)\n",
    "\n",
    "#plt.show()\n",
    "plt.tight_layout()\n",
    "\n",
    "# Save to image\n",
    "plt.savefig('Bottom 5 Countries Healthy Life Expectancy Score by Year.png', dpi=300, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "5874b327-9bfd-4f96-b3d8-5c45ebca9b7c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generate a scatter plot that shows the HALE score for each bottom 5 countries for each year.\n",
    "bottom5_Rwanda_HALE = bottom5_Rwanda[['Country','HALE','Year']] \n",
    "bottom5_Benin_HALE = bottom5_Benin[['Country','HALE','Year']] \n",
    "bottom5_Syria_HALE = bottom5_Syria[['Country','HALE','Year']] \n",
    "bottom5_Burundi_HALE = bottom5_Burundi[['Country','HALE','Year']] \n",
    "bottom5_Togo_HALE = bottom5_Togo[['Country','HALE','Year']] \n",
    "bottom5_Afghanistan_HALE = bottom5_Afghanistan[['Country','HALE','Year']] \n",
    "bottom5_Tanzania_HALE = bottom5_Tanzania[['Country','HALE','Year']] \n",
    "bottom5_CAR_HALE = bottom5_CAR[['Country','HALE','Year']] \n",
    "bottom5_Yemen_HALE = bottom5_Yemen[['Country','HALE','Year']] \n",
    "bottom5_SS_HALE = bottom5_SS[['Country','HALE','Year']] \n",
    "#bottom5_Zimbabwe_HALE = bottom5_Zimbabwe[['Country','HALE','Year']] \n",
    "#bottom5_Lesotho_HALE = bottom5_Lesotho[['Country','HALE','Year']] \n",
    "#bottom5_Botswana_HALE = bottom5_Botswana[['Country','HALE','Year']] \n",
    "\n",
    "ax_Sy=bottom5_Syria_HALE.plot.scatter(x='Year', y='HALE', c='green', s=80)\n",
    "ax_SS=bottom5_SS_HALE.plot.scatter(x='Year', y='HALE', c='black', ax=ax_Sy, s=80)\n",
    "ax_Rw=bottom5_Rwanda_HALE.plot.scatter(x='Year', y='HALE', c='red', ax=ax_Sy,s=80)\n",
    "ax_Be=bottom5_Benin_HALE.plot.scatter(x='Year', y='HALE', c='yellow', ax=ax_Sy,s=80)\n",
    "ax_Bu=bottom5_Burundi_HALE.plot.scatter(x='Year', y='HALE', c='blue', ax=ax_Sy,s=80)\n",
    "ax_To=bottom5_Togo_HALE.plot.scatter(x='Year', y='HALE', c='orange', ax=ax_Sy,s=80)\n",
    "ax_Af=bottom5_Afghanistan_HALE.plot.scatter(x='Year', y='HALE', c='pink', ax=ax_Sy,s=80)\n",
    "ax_Ta=bottom5_Tanzania_HALE.plot.scatter(x='Year', y='HALE', c='purple', ax=ax_Sy, s=80)\n",
    "ax_CAR=bottom5_CAR_HALE.plot.scatter(x='Year', y='HALE', c='cyan', ax=ax_Sy,s=80)\n",
    "ax_Ye=bottom5_Yemen_HALE.plot.scatter(x='Year', y='HALE', c='magenta', ax=ax_Sy,s=80)\n",
    "\n",
    "#ax_Zi=bottom5_Zimbabwe_HALE.plot.scatter(x='Year', y='HALE', c='purple', ax=ax_Rw)\n",
    "#ax_Le=bottom5_Lesotho_HALE.plot.scatter(x='Year', y='HALE', c='purple', ax=ax_Rw)\n",
    "#ax_Bo=bottom5_Botswana_HALE.plot.scatter(x='Year', y='HALE', c='purple', ax=ax_Rw)\n",
    "\n",
    "print(ax_Rw == ax_Be == ax_Sy == ax_Bu == ax_To == ax_Af == ax_Ta == ax_CAR == ax_Ye == ax_SS)\n",
    "\n",
    "x_axis = bottom5_all['Year']\n",
    "#plt.xlim(2015, len(x_axis))\n",
    "plt.title ('Bottom 5 Countries Healthy Life Expectancy Score by Year') \n",
    "plt.ylabel('Healthy Life Expectancy Score')\n",
    "plt.hlines(y=lowerq,xmin=-1, xmax=5,linewidth=1.5, color='red')\n",
    "#plt.legend(loc='upper right')\n",
    "#plt.xlim(-2,20)\n",
    "\n",
    "#plt.show()\n",
    "plt.tight_layout()\n",
    "\n",
    "# Save to image\n",
    "plt.savefig('Bottom 5 Countries Healthy Life Expectancy Score by Year_no legend.png', dpi=300, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "09235306-9b78-4f8a-ba04-6a854ef34966",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bf0579f8-047d-4d33-91a5-37bb6685ba0d",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "outputs": [],
   "source": [
    "## Part 3: Correlations"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "082b7bce-cc7a-4292-8df9-44a328f4f0a2",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Part 3: Coorelations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "593838b3-9eae-4242-a0e4-813a2a2cfad9",
   "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>Happiness Rank</th>\n",
       "      <th>Country</th>\n",
       "      <th>Happiness Score</th>\n",
       "      <th>Economy (GDP per Capita)</th>\n",
       "      <th>Social Support</th>\n",
       "      <th>HALE</th>\n",
       "      <th>Freedom to make life choices</th>\n",
       "      <th>Generosity</th>\n",
       "      <th>Perceptions of corruption</th>\n",
       "      <th>Dystopia Residual</th>\n",
       "      <th>Unnamed: 10</th>\n",
       "      <th>Region</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Finland</td>\n",
       "      <td>7.769</td>\n",
       "      <td>1.340</td>\n",
       "      <td>1.587</td>\n",
       "      <td>0.986</td>\n",
       "      <td>0.596</td>\n",
       "      <td>0.153</td>\n",
       "      <td>0.393</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Denmark</td>\n",
       "      <td>7.600</td>\n",
       "      <td>1.383</td>\n",
       "      <td>1.573</td>\n",
       "      <td>0.996</td>\n",
       "      <td>0.592</td>\n",
       "      <td>0.252</td>\n",
       "      <td>0.410</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Norway</td>\n",
       "      <td>7.554</td>\n",
       "      <td>1.488</td>\n",
       "      <td>1.582</td>\n",
       "      <td>1.028</td>\n",
       "      <td>0.603</td>\n",
       "      <td>0.271</td>\n",
       "      <td>0.341</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Iceland</td>\n",
       "      <td>7.494</td>\n",
       "      <td>1.380</td>\n",
       "      <td>1.624</td>\n",
       "      <td>1.026</td>\n",
       "      <td>0.591</td>\n",
       "      <td>0.354</td>\n",
       "      <td>0.118</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>7.488</td>\n",
       "      <td>1.396</td>\n",
       "      <td>1.522</td>\n",
       "      <td>0.999</td>\n",
       "      <td>0.557</td>\n",
       "      <td>0.322</td>\n",
       "      <td>0.298</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>152</td>\n",
       "      <td>Rwanda</td>\n",
       "      <td>3.334</td>\n",
       "      <td>0.359</td>\n",
       "      <td>0.711</td>\n",
       "      <td>0.614</td>\n",
       "      <td>0.555</td>\n",
       "      <td>0.217</td>\n",
       "      <td>0.411</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>153</td>\n",
       "      <td>Tanzania</td>\n",
       "      <td>3.231</td>\n",
       "      <td>0.476</td>\n",
       "      <td>0.885</td>\n",
       "      <td>0.499</td>\n",
       "      <td>0.417</td>\n",
       "      <td>0.276</td>\n",
       "      <td>0.147</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>154</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>3.203</td>\n",
       "      <td>0.350</td>\n",
       "      <td>0.517</td>\n",
       "      <td>0.361</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.158</td>\n",
       "      <td>0.025</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>155</td>\n",
       "      <td>Central African Republic</td>\n",
       "      <td>3.083</td>\n",
       "      <td>0.026</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.105</td>\n",
       "      <td>0.225</td>\n",
       "      <td>0.235</td>\n",
       "      <td>0.035</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>156</td>\n",
       "      <td>South Sudan</td>\n",
       "      <td>2.853</td>\n",
       "      <td>0.306</td>\n",
       "      <td>0.575</td>\n",
       "      <td>0.295</td>\n",
       "      <td>0.010</td>\n",
       "      <td>0.202</td>\n",
       "      <td>0.091</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>156 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Happiness Rank                   Country  Happiness Score  \\\n",
       "0                 1                   Finland            7.769   \n",
       "1                 2                   Denmark            7.600   \n",
       "2                 3                    Norway            7.554   \n",
       "3                 4                   Iceland            7.494   \n",
       "4                 5               Netherlands            7.488   \n",
       "..              ...                       ...              ...   \n",
       "151             152                    Rwanda            3.334   \n",
       "152             153                  Tanzania            3.231   \n",
       "153             154               Afghanistan            3.203   \n",
       "154             155  Central African Republic            3.083   \n",
       "155             156               South Sudan            2.853   \n",
       "\n",
       "     Economy (GDP per Capita)  Social Support   HALE  \\\n",
       "0                       1.340           1.587  0.986   \n",
       "1                       1.383           1.573  0.996   \n",
       "2                       1.488           1.582  1.028   \n",
       "3                       1.380           1.624  1.026   \n",
       "4                       1.396           1.522  0.999   \n",
       "..                        ...             ...    ...   \n",
       "151                     0.359           0.711  0.614   \n",
       "152                     0.476           0.885  0.499   \n",
       "153                     0.350           0.517  0.361   \n",
       "154                     0.026           0.000  0.105   \n",
       "155                     0.306           0.575  0.295   \n",
       "\n",
       "     Freedom to make life choices  Generosity  Perceptions of corruption  \\\n",
       "0                           0.596       0.153                      0.393   \n",
       "1                           0.592       0.252                      0.410   \n",
       "2                           0.603       0.271                      0.341   \n",
       "3                           0.591       0.354                      0.118   \n",
       "4                           0.557       0.322                      0.298   \n",
       "..                            ...         ...                        ...   \n",
       "151                         0.555       0.217                      0.411   \n",
       "152                         0.417       0.276                      0.147   \n",
       "153                         0.000       0.158                      0.025   \n",
       "154                         0.225       0.235                      0.035   \n",
       "155                         0.010       0.202                      0.091   \n",
       "\n",
       "     Dystopia Residual  Unnamed: 10  Region  \n",
       "0                  NaN          NaN     NaN  \n",
       "1                  NaN          NaN     NaN  \n",
       "2                  NaN          NaN     NaN  \n",
       "3                  NaN          NaN     NaN  \n",
       "4                  NaN          NaN     NaN  \n",
       "..                 ...          ...     ...  \n",
       "151                NaN          NaN     NaN  \n",
       "152                NaN          NaN     NaN  \n",
       "153                NaN          NaN     NaN  \n",
       "154                NaN          NaN     NaN  \n",
       "155                NaN          NaN     NaN  \n",
       "\n",
       "[156 rows x 12 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "C2019"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "0d96b2fc-fc0a-420c-95ad-49b4c0ca6c71",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The correlation between HALE and GDP per Capita is 0.84\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Take year 2019 as example\n",
    "#Calculate the correlation coefficient for HALE and GDP per Capita\n",
    "\n",
    "# Identify x and y axises\n",
    "x_axis1 = C2019['HALE']\n",
    "y_axis1 = C2019['Economy (GDP per Capita)']\n",
    "\n",
    "#Plot the line\n",
    "plt.scatter(x_axis1, y_axis1 ,marker=\"o\")\n",
    "\n",
    "#set labels and title\n",
    "plt.xlabel('Healthy Life Expectancy Score')\n",
    "plt.ylabel('GDP per Capita Score')\n",
    "plt.title('Correlation of Health and Economy | 2019')\n",
    "\n",
    "correlation = st.pearsonr(x_axis1,y_axis1)\n",
    "print(f\"The correlation between HALE and GDP per Capita is {round(correlation[0],2)}\")\n",
    "\n",
    "# Calculate the linear regression model\n",
    "p_slope, p_int, p_r, p_p, p_std_err = st.linregress(x_axis1, y_axis1)\n",
    "p_fit = p_slope * x_axis1 + p_int\n",
    "\n",
    "#Plot the line\n",
    "plt.scatter(x_axis1, y_axis1 ,marker=\"o\")\n",
    "plt.plot(x_axis1,p_fit,\"--\",linewidth=3, color='blue')\n",
    "\n",
    "#plt.show()\n",
    "plt.savefig('Correlation of Health and Economy.png', dpi=300, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "265532cf-fab4-420f-a553-f1b0927b920e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The correlation between HALE and Family per Capita is 0.72\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Take year 2019 as example\n",
    "#Calculate the correlation coefficient for HALE and GDP per Capita\n",
    "\n",
    "# Identify x and y axises\n",
    "x_axis1 = C2019['HALE']\n",
    "y_axis1 = C2019['Social Support']\n",
    "\n",
    "#Plot the line\n",
    "plt.scatter(x_axis1, y_axis1 ,marker=\"o\", c= 'blue')\n",
    "\n",
    "#set labels and title\n",
    "plt.xlabel('Healthy Life Expectancy Score')\n",
    "plt.ylabel('Family Score')\n",
    "plt.title('Correlation of Health and Family | 2019')\n",
    "\n",
    "correlation = st.pearsonr(x_axis1,y_axis1)\n",
    "print(f\"The correlation between HALE and Family per Capita is {round(correlation[0],2)}\")\n",
    "\n",
    "# Calculate the linear regression model\n",
    "p_slope, p_int, p_r, p_p, p_std_err = st.linregress(x_axis1, y_axis1)\n",
    "p_fit = p_slope * x_axis1 + p_int\n",
    "\n",
    "#Plot the line\n",
    "plt.scatter(x_axis1, y_axis1 ,marker=\"o\")\n",
    "plt.plot(x_axis1,p_fit,\"--\",linewidth=3, color='red')\n",
    "\n",
    "#plt.show()\n",
    "plt.savefig('Correlation of Health and Family.png', dpi=300, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "6b174f24-0b28-4be7-b921-c9615b18dcf1",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The correlation between HALE and Family per Capita is 0.78\n",
      "The correlation between HALE and Family per Capita is 0.78\n",
      "The correlation between HALE and Family per Capita is 0.78\n",
      "The correlation between HALE and Family per Capita is 0.77\n",
      "The correlation between HALE and Family per Capita is 0.72\n"
     ]
    }
   ],
   "source": [
    "x_axis1 = C2019['HALE']\n",
    "y_axis1 = C2019['Happiness Score']\n",
    "correlation = st.pearsonr(x_axis1,y_axis1)\n",
    "print(f\"The correlation between HALE and Family per Capita is {round(correlation[0],2)}\")\n",
    "x_axis1 = C2018['HALE']\n",
    "y_axis1 = C2018['Happiness Score']\n",
    "correlation = st.pearsonr(x_axis1,y_axis1)\n",
    "print(f\"The correlation between HALE and Family per Capita is {round(correlation[0],2)}\")\n",
    "x_axis1 = C2017['HALE']\n",
    "y_axis1 = C2017['Happiness Score']\n",
    "correlation = st.pearsonr(x_axis1,y_axis1)\n",
    "print(f\"The correlation between HALE and Family per Capita is {round(correlation[0],2)}\")\n",
    "x_axis1 = C2016['HALE']\n",
    "y_axis1 = C2016['Happiness Score']\n",
    "correlation = st.pearsonr(x_axis1,y_axis1)\n",
    "print(f\"The correlation between HALE and Family per Capita is {round(correlation[0],2)}\")\n",
    "x_axis1 = C2015['HALE']\n",
    "y_axis1 = C2015['Happiness Score']\n",
    "correlation = st.pearsonr(x_axis1,y_axis1)\n",
    "print(f\"The correlation between HALE and Family per Capita is {round(correlation[0],2)}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6ad7b00c-3478-4ca4-953a-2e210620d67d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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