world-happiness-report-2022.ipynb

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    "# The dataset, its description + imports\n",
    "\n",
    "Column descriptions from [whr+22.pdf](https://happiness-report.s3.amazonaws.com/2022/WHR+22.pdf):\n",
    "\n",
    "**RANK**<br>\n",
    "Ranking based on happiness score\n",
    "\n",
    "**Country** <br>\n",
    "researched country.\n",
    "\n",
    "**Happiness score** <br>\n",
    "Sum of the six key variables relative to a hypothetical country called \"Distopia\".\n",
    "\n",
    "**Whisker-high and Whisker-low** <br>\n",
    "The quartile (real number between 0 and 10) from which the happiness score is calculated.\n",
    "\n",
    "**Dystopia (1.83) + residual**<br>\n",
    "The predicted Cantril ladder for a hypothetical country with the world’s lowest values for each of the six variables.\n",
    "\n",
    "> “Dystopia”—named because it has values equal to the world’s lowest national averages for 2019-2021 for each of the six key variables used in Table 2.1. We use Dystopia as a benchmark against which to compare contributions from each of the six factors. The choice of Dystopia as a benchmark permits every real country to have a positive (or at least zero) contribution from each of the six factors. Based on the estimates in the first column of Table 2.1, we calculate that Dystopia had a 2019-2021 life evaluation equal to 1.83 on the 0 to 10 scale. The final sub-bar is the sum of two components: the calculated average 2017-2019 life evaluation in Dystopia (=1.83) plus each country’s own prediction error, which measures the extent to which life evaluations are higher or lower than those predicted by our equation in the first column of Table 2.1. These residuals are as likely to be negative as positive\n",
    "\n",
    "**Explained by: GDP per capita**<br>\n",
    "Purchasing Power Parity (PPP) adjusted to constant 2017 international dollars, taken from the World Development Indicators (WDI) released by the World Bank on December 16, 1. See Statistical Appendix 1 for more details. GDP data for 2021 are not yet available, so we extend the GDP time series from 2020 to 2021 using country-specific forecasts of real GDP growth from the OECD Economic Outlook No. 110 (Edition December 1) or, if missing, the World Bank’s Global Economic Prospects (Last Updated: 01/11/2022), after adjustment for population growth. The equation uses the natural log of GDP per capita, as this form fits the data significantly better than GDP per capita.\n",
    "\n",
    "**Explained by: Social support**<br>\n",
    "The national average of the binary responses (0=no, 1=yes) to the Gallup World Poll (GWP) question “If you were in trouble, do you have relatives or friends you can count on to help you whenever you need them, or not?”\n",
    "\n",
    "**Explained by: Healthy life expectancy**<br>\n",
    "The time series for healthy life expectancy at birth is constructed based on data from the World Health Organization (WHO) Global Health Observatory data repository, with data available for 2000, 2010, 2015, and 2019. Interpolation and extrapolation are used to match this report’s sample period (2005-2021)\n",
    "\n",
    "**Explained by: Freedom to make life choices**<br>\n",
    "The national average of binary responses (0=no, 1=yes) to the GWP question “Are you satisfied or dissatisfied with your freedom to choose what you do with your life?”\n",
    "\n",
    "**Explained by: Generosity**<br>\n",
    "The residual of regressing the national average of GWP responses to the donation question “Have you donated money to a charity in the past month?” on log GDP per capita.\n",
    "\n",
    "**Explained by: Perceptions of corruption**<br>\n",
    "The average of binary answers to two GWP questions: “Is corruption widespread throughout the government in this country or not?” and “Is corruption widespread within businesses in this country or not?” Where data for government corruption are missing, the perception of business corruption is used as the overall corruption-perception measure.\n",
    "\n",
    "---\n",
    "\n",
    "**Additional information**<br>\n",
    "Countries marked with an astarisk do not have survey information in 2020 or 2021. Instead their averages are based on the 2019 survey\n",
    "\n",
    "**Positive affect** is defined as the average of previous-day affect measures for laughter, enjoyment, and doing or learning something interesting. This marks a change from recent years, where only laughter and enjoyment were included. The inclusion of interest gives us three components in each of positive and negative affect and slightly improves the equation fit in column 4. The general form for the affect questions is: Did you experience the following feelings during a lot of the day yesterday? Only the interest question is phrased differently: Did you learn or do something interesting yesterday? See Statistical Appendix 1 for more details.\n",
    "\n",
    "**Negative affect** is defined as the average of previous-day affect measures for worry, sadness, and anger.\n",
    "\n",
    "For more detailed information on each of the predictors, see page 20 & 21 of the pdf"
   ]
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   "source": [
    "## Imports"
   ]
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      "BokehDeprecationWarning: 'tile_providers module' was deprecated in Bokeh 3.0.0 and will be removed, use 'add_tile directly' instead.\n"
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   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "# import matplotlib.pyplot as plt\n",
    "import pandas_bokeh\n",
    "from bokeh.models import Whisker\n",
    "from bokeh.plotting import show, output_notebook, figure\n",
    "output_notebook()\n",
    "\n",
    "pd.set_option('plotting.backend', 'pandas_bokeh')\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Custom functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "DUPLICATE_COLS_TO_CHECK = [\"INSERT COLS\"] # Based on non-null cols gleaned from df.info()\n",
    "DUPLICATE_VALUE_COL = \"INSERT KEY\" # Which col to return values from\n",
    "\n",
    "## Summary, providing info on duplicates if asked\n",
    "def mySummary(dataframe: pd.DataFrame, duplicateCheck: bool = False) -> str:\n",
    "    if duplicateCheck:\n",
    "        num_of_dupes = dataframe[dataframe[DUPLICATE_COLS_TO_CHECK].duplicated()][DUPLICATE_VALUE_COL].count()\n",
    "        if num_of_dupes > 10:\n",
    "            DUPLICATES = (num_of_dupes, \"More than 10 duplicates, explore more\")\n",
    "        else:\n",
    "            DUPLICATES = (num_of_dupes, dataframe[dataframe[DUPLICATE_COLS_TO_CHECK].duplicated()][DUPLICATE_VALUE_COL].values)\n",
    "\n",
    "        info_string = \"\"\"\n",
    "            Rows: {} | Cols: {} | Duplicates: {}\n",
    "            Duplicates from '{}' column: {}\n",
    "            \"\"\".format(\n",
    "                df.shape[0],          # rows\n",
    "                df.shape[1],          # cols\n",
    "                DUPLICATES[0],\n",
    "                DUPLICATE_VALUE_COL,\n",
    "                DUPLICATES[1],\n",
    "                )\n",
    "    else:\n",
    "        info_string = \"\"\"\n",
    "            Rows: {} | Cols: {}\n",
    "            \"\"\".format(\n",
    "                df.shape[0],          # rows\n",
    "                df.shape[1],          # cols\n",
    "                )\n",
    "    return info_string"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def myInfo(dataframe: pd.DataFrame) -> pd.DataFrame:\n",
    "    # Custom info dataframe\n",
    "    my_info_df = pd.DataFrame()\n",
    "    my_info_df[\"columns\"] = dataframe.keys()\n",
    "\n",
    "    # Custom info columns\n",
    "    my_info_df[\"missing_data\"] = dataframe.isna().sum().values\n",
    "    my_info_df[\"col_dtype\"] = dataframe.dtypes.values\n",
    "\n",
    "    my_info_df[\"bytes\"] = dataframe.memory_usage(index=False, deep=True).values\n",
    "    my_info_df[\"nunique\"] = dataframe.nunique().values\n",
    "    return my_info_df"
   ]
  },
  {
   "attachments": {},
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   "metadata": {},
   "source": [
    "# Exploration"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Main Dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RANK</th>\n",
       "      <th>Country</th>\n",
       "      <th>Happiness score</th>\n",
       "      <th>Whisker-high</th>\n",
       "      <th>Whisker-low</th>\n",
       "      <th>Dystopia (1.83) + residual</th>\n",
       "      <th>Explained by: GDP per capita</th>\n",
       "      <th>Explained by: Social support</th>\n",
       "      <th>Explained by: Healthy life expectancy</th>\n",
       "      <th>Explained by: Freedom to make life choices</th>\n",
       "      <th>Explained by: Generosity</th>\n",
       "      <th>Explained by: Perceptions of corruption</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Finland</td>\n",
       "      <td>7.821</td>\n",
       "      <td>7.886</td>\n",
       "      <td>7.756</td>\n",
       "      <td>2.518</td>\n",
       "      <td>1.892</td>\n",
       "      <td>1.258</td>\n",
       "      <td>0.775</td>\n",
       "      <td>0.736</td>\n",
       "      <td>0.109</td>\n",
       "      <td>0.534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Denmark</td>\n",
       "      <td>7.636</td>\n",
       "      <td>7.710</td>\n",
       "      <td>7.563</td>\n",
       "      <td>2.226</td>\n",
       "      <td>1.953</td>\n",
       "      <td>1.243</td>\n",
       "      <td>0.777</td>\n",
       "      <td>0.719</td>\n",
       "      <td>0.188</td>\n",
       "      <td>0.532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Iceland</td>\n",
       "      <td>7.557</td>\n",
       "      <td>7.651</td>\n",
       "      <td>7.464</td>\n",
       "      <td>2.320</td>\n",
       "      <td>1.936</td>\n",
       "      <td>1.320</td>\n",
       "      <td>0.803</td>\n",
       "      <td>0.718</td>\n",
       "      <td>0.270</td>\n",
       "      <td>0.191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Switzerland</td>\n",
       "      <td>7.512</td>\n",
       "      <td>7.586</td>\n",
       "      <td>7.437</td>\n",
       "      <td>2.153</td>\n",
       "      <td>2.026</td>\n",
       "      <td>1.226</td>\n",
       "      <td>0.822</td>\n",
       "      <td>0.677</td>\n",
       "      <td>0.147</td>\n",
       "      <td>0.461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>7.415</td>\n",
       "      <td>7.471</td>\n",
       "      <td>7.359</td>\n",
       "      <td>2.137</td>\n",
       "      <td>1.945</td>\n",
       "      <td>1.206</td>\n",
       "      <td>0.787</td>\n",
       "      <td>0.651</td>\n",
       "      <td>0.271</td>\n",
       "      <td>0.419</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   RANK      Country  Happiness score  Whisker-high  Whisker-low  \\\n",
       "0     1      Finland            7.821         7.886        7.756   \n",
       "1     2      Denmark            7.636         7.710        7.563   \n",
       "2     3      Iceland            7.557         7.651        7.464   \n",
       "3     4  Switzerland            7.512         7.586        7.437   \n",
       "4     5  Netherlands            7.415         7.471        7.359   \n",
       "\n",
       "   Dystopia (1.83) + residual  Explained by: GDP per capita  \\\n",
       "0                       2.518                         1.892   \n",
       "1                       2.226                         1.953   \n",
       "2                       2.320                         1.936   \n",
       "3                       2.153                         2.026   \n",
       "4                       2.137                         1.945   \n",
       "\n",
       "   Explained by: Social support  Explained by: Healthy life expectancy  \\\n",
       "0                         1.258                                  0.775   \n",
       "1                         1.243                                  0.777   \n",
       "2                         1.320                                  0.803   \n",
       "3                         1.226                                  0.822   \n",
       "4                         1.206                                  0.787   \n",
       "\n",
       "   Explained by: Freedom to make life choices  Explained by: Generosity  \\\n",
       "0                                       0.736                     0.109   \n",
       "1                                       0.719                     0.188   \n",
       "2                                       0.718                     0.270   \n",
       "3                                       0.677                     0.147   \n",
       "4                                       0.651                     0.271   \n",
       "\n",
       "   Explained by: Perceptions of corruption  \n",
       "0                                    0.534  \n",
       "1                                    0.532  \n",
       "2                                    0.191  \n",
       "3                                    0.461  \n",
       "4                                    0.419  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Data\n",
    "df = pd.read_csv(\"data/World Happiness Report 2022.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>happiness_score</th>\n",
       "      <th>whisker_high</th>\n",
       "      <th>whisker_low</th>\n",
       "      <th>dystopia</th>\n",
       "      <th>gdp_per_capita</th>\n",
       "      <th>social_support</th>\n",
       "      <th>life_expectancy</th>\n",
       "      <th>free_life_choices</th>\n",
       "      <th>generosity</th>\n",
       "      <th>corruption_perception</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Finland</td>\n",
       "      <td>7.821</td>\n",
       "      <td>7.886</td>\n",
       "      <td>7.756</td>\n",
       "      <td>2.518</td>\n",
       "      <td>1.892</td>\n",
       "      <td>1.258</td>\n",
       "      <td>0.775</td>\n",
       "      <td>0.736</td>\n",
       "      <td>0.109</td>\n",
       "      <td>0.534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Denmark</td>\n",
       "      <td>7.636</td>\n",
       "      <td>7.710</td>\n",
       "      <td>7.563</td>\n",
       "      <td>2.226</td>\n",
       "      <td>1.953</td>\n",
       "      <td>1.243</td>\n",
       "      <td>0.777</td>\n",
       "      <td>0.719</td>\n",
       "      <td>0.188</td>\n",
       "      <td>0.532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Iceland</td>\n",
       "      <td>7.557</td>\n",
       "      <td>7.651</td>\n",
       "      <td>7.464</td>\n",
       "      <td>2.320</td>\n",
       "      <td>1.936</td>\n",
       "      <td>1.320</td>\n",
       "      <td>0.803</td>\n",
       "      <td>0.718</td>\n",
       "      <td>0.270</td>\n",
       "      <td>0.191</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   country  happiness_score  whisker_high  whisker_low  dystopia  \\\n",
       "0  Finland            7.821         7.886        7.756     2.518   \n",
       "1  Denmark            7.636         7.710        7.563     2.226   \n",
       "2  Iceland            7.557         7.651        7.464     2.320   \n",
       "\n",
       "   gdp_per_capita  social_support  life_expectancy  free_life_choices  \\\n",
       "0           1.892           1.258            0.775              0.736   \n",
       "1           1.953           1.243            0.777              0.719   \n",
       "2           1.936           1.320            0.803              0.718   \n",
       "\n",
       "   generosity  corruption_perception  \n",
       "0       0.109                  0.534  \n",
       "1       0.188                  0.532  \n",
       "2       0.270                  0.191  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"data/World Happiness Report 2022.csv\")\n",
    "# Conclusion:\n",
    "# 1 - Don't need rank as it's just a copy of index+1\n",
    "df.drop(\"RANK\",axis=1,inplace=True)\n",
    "# 2 - I don't like column names with mixed cases or spaces:\n",
    "rename_to = {\n",
    "    \"Country\": \"country\",\n",
    "    \"Happiness score\": \"happiness_score\",\n",
    "    \"Whisker-high\": \"whisker_high\",\n",
    "    \"Whisker-low\": \"whisker_low\",\n",
    "    \"Dystopia (1.83) + residual\": \"dystopia\",\n",
    "    \"Explained by: GDP per capita\": \"gdp_per_capita\",\n",
    "    \"Explained by: Social support\": \"social_support\",\n",
    "    \"Explained by: Healthy life expectancy\": \"life_expectancy\",\n",
    "    \"Explained by: Freedom to make life choices\": \"free_life_choices\",\n",
    "    \"Explained by: Generosity\": \"generosity\",\n",
    "    \"Explained by: Perceptions of corruption\": \"corruption_perception\"}\n",
    "df.rename(columns=rename_to, inplace=True)\n",
    "df.head(3)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "            Rows: 146 | Cols: 11\n",
      "            \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>columns</th>\n",
       "      <th>missing_data</th>\n",
       "      <th>col_dtype</th>\n",
       "      <th>bytes</th>\n",
       "      <th>nunique</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>country</td>\n",
       "      <td>0</td>\n",
       "      <td>string</td>\n",
       "      <td>9545</td>\n",
       "      <td>146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>happiness_score</td>\n",
       "      <td>0</td>\n",
       "      <td>float32</td>\n",
       "      <td>584</td>\n",
       "      <td>141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>whisker_high</td>\n",
       "      <td>0</td>\n",
       "      <td>float32</td>\n",
       "      <td>584</td>\n",
       "      <td>144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>whisker_low</td>\n",
       "      <td>0</td>\n",
       "      <td>float32</td>\n",
       "      <td>584</td>\n",
       "      <td>141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>dystopia</td>\n",
       "      <td>0</td>\n",
       "      <td>float32</td>\n",
       "      <td>584</td>\n",
       "      <td>138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>gdp_per_capita</td>\n",
       "      <td>0</td>\n",
       "      <td>float32</td>\n",
       "      <td>584</td>\n",
       "      <td>141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>social_support</td>\n",
       "      <td>0</td>\n",
       "      <td>float32</td>\n",
       "      <td>584</td>\n",
       "      <td>133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>life_expectancy</td>\n",
       "      <td>0</td>\n",
       "      <td>float32</td>\n",
       "      <td>584</td>\n",
       "      <td>134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>free_life_choices</td>\n",
       "      <td>0</td>\n",
       "      <td>float32</td>\n",
       "      <td>584</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>generosity</td>\n",
       "      <td>0</td>\n",
       "      <td>float32</td>\n",
       "      <td>584</td>\n",
       "      <td>116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>corruption_perception</td>\n",
       "      <td>0</td>\n",
       "      <td>float32</td>\n",
       "      <td>584</td>\n",
       "      <td>116</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  columns  missing_data col_dtype  bytes  nunique\n",
       "0                 country             0    string   9545      146\n",
       "1         happiness_score             0   float32    584      141\n",
       "2            whisker_high             0   float32    584      144\n",
       "3             whisker_low             0   float32    584      141\n",
       "4                dystopia             0   float32    584      138\n",
       "5          gdp_per_capita             0   float32    584      141\n",
       "6          social_support             0   float32    584      133\n",
       "7         life_expectancy             0   float32    584      134\n",
       "8       free_life_choices             0   float32    584      128\n",
       "9              generosity             0   float32    584      116\n",
       "10  corruption_perception             0   float32    584      116"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dataframe optimisation\n",
    "df[\"country\"] = df[\"country\"].astype(\"string\")\n",
    "for col in df.columns[1:]:\n",
    "    df[col] = df[col].astype('float32')\n",
    "\n",
    "# Summary & info\n",
    "print(mySummary(df))\n",
    "myInfo(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "  background-color: #fbf3f9;\n",
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       "}\n",
       "#T_e70ed_row1_col1, #T_e70ed_row5_col1, #T_e70ed_row6_col2 {\n",
       "  background-color: #faf3f9;\n",
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       "}\n",
       "#T_e70ed_row1_col3, #T_e70ed_row1_col4, #T_e70ed_row3_col0, #T_e70ed_row3_col1, #T_e70ed_row3_col2, #T_e70ed_row4_col3, #T_e70ed_row5_col3, #T_e70ed_row5_col4, #T_e70ed_row7_col5 {\n",
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       "  background-color: #fef6fb;\n",
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       "}\n",
       "#T_e70ed_row1_col7, #T_e70ed_row1_col8, #T_e70ed_row1_col9, #T_e70ed_row2_col0, #T_e70ed_row2_col1, #T_e70ed_row2_col2, #T_e70ed_row2_col3, #T_e70ed_row2_col4, #T_e70ed_row2_col5, #T_e70ed_row2_col6, #T_e70ed_row2_col7, #T_e70ed_row2_col8, #T_e70ed_row2_col9, #T_e70ed_row3_col3, #T_e70ed_row3_col4, #T_e70ed_row3_col5, #T_e70ed_row3_col6, #T_e70ed_row3_col7, #T_e70ed_row3_col8, #T_e70ed_row3_col9, #T_e70ed_row4_col6, #T_e70ed_row4_col7, #T_e70ed_row4_col8, #T_e70ed_row4_col9, #T_e70ed_row5_col7, #T_e70ed_row5_col8, #T_e70ed_row5_col9, #T_e70ed_row6_col8, #T_e70ed_row6_col9, #T_e70ed_row7_col8 {\n",
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       "#T_e70ed_row6_col0, #T_e70ed_row6_col1 {\n",
       "  background-color: #faf2f8;\n",
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       "}\n",
       "#T_e70ed_row6_col3, #T_e70ed_row6_col4, #T_e70ed_row7_col3, #T_e70ed_row7_col4 {\n",
       "  background-color: #fdf5fa;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_e70ed_row7_col0, #T_e70ed_row7_col1, #T_e70ed_row7_col2 {\n",
       "  background-color: #f8f1f8;\n",
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       "</style>\n",
       "<table id=\"T_e70ed\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_e70ed_level0_col0\" class=\"col_heading level0 col0\" >happiness_score</th>\n",
       "      <th id=\"T_e70ed_level0_col1\" class=\"col_heading level0 col1\" >whisker_high</th>\n",
       "      <th id=\"T_e70ed_level0_col2\" class=\"col_heading level0 col2\" >whisker_low</th>\n",
       "      <th id=\"T_e70ed_level0_col3\" class=\"col_heading level0 col3\" >dystopia</th>\n",
       "      <th id=\"T_e70ed_level0_col4\" class=\"col_heading level0 col4\" >gdp_per_capita</th>\n",
       "      <th id=\"T_e70ed_level0_col5\" class=\"col_heading level0 col5\" >social_support</th>\n",
       "      <th id=\"T_e70ed_level0_col6\" class=\"col_heading level0 col6\" >life_expectancy</th>\n",
       "      <th id=\"T_e70ed_level0_col7\" class=\"col_heading level0 col7\" >free_life_choices</th>\n",
       "      <th id=\"T_e70ed_level0_col8\" class=\"col_heading level0 col8\" >generosity</th>\n",
       "      <th id=\"T_e70ed_level0_col9\" class=\"col_heading level0 col9\" >corruption_perception</th>\n",
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       "  </thead>\n",
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       "      <td id=\"T_e70ed_row0_col1\" class=\"data row0 col1\" >146.000000</td>\n",
       "      <td id=\"T_e70ed_row0_col2\" class=\"data row0 col2\" >146.000000</td>\n",
       "      <td id=\"T_e70ed_row0_col3\" class=\"data row0 col3\" >146.000000</td>\n",
       "      <td id=\"T_e70ed_row0_col4\" class=\"data row0 col4\" >146.000000</td>\n",
       "      <td id=\"T_e70ed_row0_col5\" class=\"data row0 col5\" >146.000000</td>\n",
       "      <td id=\"T_e70ed_row0_col6\" class=\"data row0 col6\" >146.000000</td>\n",
       "      <td id=\"T_e70ed_row0_col7\" class=\"data row0 col7\" >146.000000</td>\n",
       "      <td id=\"T_e70ed_row0_col8\" class=\"data row0 col8\" >146.000000</td>\n",
       "      <td id=\"T_e70ed_row0_col9\" class=\"data row0 col9\" >146.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e70ed_level0_row1\" class=\"row_heading level0 row1\" >mean</th>\n",
       "      <td id=\"T_e70ed_row1_col0\" class=\"data row1 col0\" >5.553575</td>\n",
       "      <td id=\"T_e70ed_row1_col1\" class=\"data row1 col1\" >5.673589</td>\n",
       "      <td id=\"T_e70ed_row1_col2\" class=\"data row1 col2\" >5.433568</td>\n",
       "      <td id=\"T_e70ed_row1_col3\" class=\"data row1 col3\" >1.831808</td>\n",
       "      <td id=\"T_e70ed_row1_col4\" class=\"data row1 col4\" >1.410445</td>\n",
       "      <td id=\"T_e70ed_row1_col5\" class=\"data row1 col5\" >0.905863</td>\n",
       "      <td id=\"T_e70ed_row1_col6\" class=\"data row1 col6\" >0.586171</td>\n",
       "      <td id=\"T_e70ed_row1_col7\" class=\"data row1 col7\" >0.517226</td>\n",
       "      <td id=\"T_e70ed_row1_col8\" class=\"data row1 col8\" >0.147377</td>\n",
       "      <td id=\"T_e70ed_row1_col9\" class=\"data row1 col9\" >0.154781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e70ed_level0_row2\" class=\"row_heading level0 row2\" >std</th>\n",
       "      <td id=\"T_e70ed_row2_col0\" class=\"data row2 col0\" >1.086843</td>\n",
       "      <td id=\"T_e70ed_row2_col1\" class=\"data row2 col1\" >1.065621</td>\n",
       "      <td id=\"T_e70ed_row2_col2\" class=\"data row2 col2\" >1.109380</td>\n",
       "      <td id=\"T_e70ed_row2_col3\" class=\"data row2 col3\" >0.534994</td>\n",
       "      <td id=\"T_e70ed_row2_col4\" class=\"data row2 col4\" >0.421663</td>\n",
       "      <td id=\"T_e70ed_row2_col5\" class=\"data row2 col5\" >0.280122</td>\n",
       "      <td id=\"T_e70ed_row2_col6\" class=\"data row2 col6\" >0.176336</td>\n",
       "      <td id=\"T_e70ed_row2_col7\" class=\"data row2 col7\" >0.145859</td>\n",
       "      <td id=\"T_e70ed_row2_col8\" class=\"data row2 col8\" >0.082799</td>\n",
       "      <td id=\"T_e70ed_row2_col9\" class=\"data row2 col9\" >0.127514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e70ed_level0_row3\" class=\"row_heading level0 row3\" >min</th>\n",
       "      <td id=\"T_e70ed_row3_col0\" class=\"data row3 col0\" >2.404000</td>\n",
       "      <td id=\"T_e70ed_row3_col1\" class=\"data row3 col1\" >2.469000</td>\n",
       "      <td id=\"T_e70ed_row3_col2\" class=\"data row3 col2\" >2.339000</td>\n",
       "      <td id=\"T_e70ed_row3_col3\" class=\"data row3 col3\" >0.187000</td>\n",
       "      <td id=\"T_e70ed_row3_col4\" class=\"data row3 col4\" >0.000000</td>\n",
       "      <td id=\"T_e70ed_row3_col5\" class=\"data row3 col5\" >0.000000</td>\n",
       "      <td id=\"T_e70ed_row3_col6\" class=\"data row3 col6\" >0.000000</td>\n",
       "      <td id=\"T_e70ed_row3_col7\" class=\"data row3 col7\" >0.000000</td>\n",
       "      <td id=\"T_e70ed_row3_col8\" class=\"data row3 col8\" >0.000000</td>\n",
       "      <td id=\"T_e70ed_row3_col9\" class=\"data row3 col9\" >0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e70ed_level0_row4\" class=\"row_heading level0 row4\" >25%</th>\n",
       "      <td id=\"T_e70ed_row4_col0\" class=\"data row4 col0\" >4.888750</td>\n",
       "      <td id=\"T_e70ed_row4_col1\" class=\"data row4 col1\" >5.006250</td>\n",
       "      <td id=\"T_e70ed_row4_col2\" class=\"data row4 col2\" >4.754750</td>\n",
       "      <td id=\"T_e70ed_row4_col3\" class=\"data row4 col3\" >1.555250</td>\n",
       "      <td id=\"T_e70ed_row4_col4\" class=\"data row4 col4\" >1.095500</td>\n",
       "      <td id=\"T_e70ed_row4_col5\" class=\"data row4 col5\" >0.732000</td>\n",
       "      <td id=\"T_e70ed_row4_col6\" class=\"data row4 col6\" >0.463250</td>\n",
       "      <td id=\"T_e70ed_row4_col7\" class=\"data row4 col7\" >0.440500</td>\n",
       "      <td id=\"T_e70ed_row4_col8\" class=\"data row4 col8\" >0.089000</td>\n",
       "      <td id=\"T_e70ed_row4_col9\" class=\"data row4 col9\" >0.068250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e70ed_level0_row5\" class=\"row_heading level0 row5\" >50%</th>\n",
       "      <td id=\"T_e70ed_row5_col0\" class=\"data row5 col0\" >5.568500</td>\n",
       "      <td id=\"T_e70ed_row5_col1\" class=\"data row5 col1\" >5.680000</td>\n",
       "      <td id=\"T_e70ed_row5_col2\" class=\"data row5 col2\" >5.453000</td>\n",
       "      <td id=\"T_e70ed_row5_col3\" class=\"data row5 col3\" >1.894500</td>\n",
       "      <td id=\"T_e70ed_row5_col4\" class=\"data row5 col4\" >1.445500</td>\n",
       "      <td id=\"T_e70ed_row5_col5\" class=\"data row5 col5\" >0.957500</td>\n",
       "      <td id=\"T_e70ed_row5_col6\" class=\"data row5 col6\" >0.621500</td>\n",
       "      <td id=\"T_e70ed_row5_col7\" class=\"data row5 col7\" >0.543500</td>\n",
       "      <td id=\"T_e70ed_row5_col8\" class=\"data row5 col8\" >0.132500</td>\n",
       "      <td id=\"T_e70ed_row5_col9\" class=\"data row5 col9\" >0.119500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e70ed_level0_row6\" class=\"row_heading level0 row6\" >75%</th>\n",
       "      <td id=\"T_e70ed_row6_col0\" class=\"data row6 col0\" >6.305000</td>\n",
       "      <td id=\"T_e70ed_row6_col1\" class=\"data row6 col1\" >6.448750</td>\n",
       "      <td id=\"T_e70ed_row6_col2\" class=\"data row6 col2\" >6.190000</td>\n",
       "      <td id=\"T_e70ed_row6_col3\" class=\"data row6 col3\" >2.153000</td>\n",
       "      <td id=\"T_e70ed_row6_col4\" class=\"data row6 col4\" >1.784750</td>\n",
       "      <td id=\"T_e70ed_row6_col5\" class=\"data row6 col5\" >1.114250</td>\n",
       "      <td id=\"T_e70ed_row6_col6\" class=\"data row6 col6\" >0.719750</td>\n",
       "      <td id=\"T_e70ed_row6_col7\" class=\"data row6 col7\" >0.626000</td>\n",
       "      <td id=\"T_e70ed_row6_col8\" class=\"data row6 col8\" >0.197750</td>\n",
       "      <td id=\"T_e70ed_row6_col9\" class=\"data row6 col9\" >0.198500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e70ed_level0_row7\" class=\"row_heading level0 row7\" >max</th>\n",
       "      <td id=\"T_e70ed_row7_col0\" class=\"data row7 col0\" >7.821000</td>\n",
       "      <td id=\"T_e70ed_row7_col1\" class=\"data row7 col1\" >7.886000</td>\n",
       "      <td id=\"T_e70ed_row7_col2\" class=\"data row7 col2\" >7.756000</td>\n",
       "      <td id=\"T_e70ed_row7_col3\" class=\"data row7 col3\" >2.844000</td>\n",
       "      <td id=\"T_e70ed_row7_col4\" class=\"data row7 col4\" >2.209000</td>\n",
       "      <td id=\"T_e70ed_row7_col5\" class=\"data row7 col5\" >1.320000</td>\n",
       "      <td id=\"T_e70ed_row7_col6\" class=\"data row7 col6\" >0.942000</td>\n",
       "      <td id=\"T_e70ed_row7_col7\" class=\"data row7 col7\" >0.740000</td>\n",
       "      <td id=\"T_e70ed_row7_col8\" class=\"data row7 col8\" >0.468000</td>\n",
       "      <td id=\"T_e70ed_row7_col9\" class=\"data row7 col9\" >0.587000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x21b92c76d10>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Outlier inspection 1\n",
    "df.describe().style.background_gradient(cmap = \"PuBu\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inspection of additional information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>happiness_score</th>\n",
       "      <th>whisker_high</th>\n",
       "      <th>whisker_low</th>\n",
       "      <th>dystopia</th>\n",
       "      <th>gdp_per_capita</th>\n",
       "      <th>social_support</th>\n",
       "      <th>life_expectancy</th>\n",
       "      <th>free_life_choices</th>\n",
       "      <th>generosity</th>\n",
       "      <th>corruption_perception</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Luxembourg*</td>\n",
       "      <td>7.404</td>\n",
       "      <td>7.501</td>\n",
       "      <td>7.307</td>\n",
       "      <td>2.042</td>\n",
       "      <td>2.209</td>\n",
       "      <td>1.155</td>\n",
       "      <td>0.790</td>\n",
       "      <td>0.700</td>\n",
       "      <td>0.120</td>\n",
       "      <td>0.388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Guatemala*</td>\n",
       "      <td>6.262</td>\n",
       "      <td>6.460</td>\n",
       "      <td>6.064</td>\n",
       "      <td>2.746</td>\n",
       "      <td>1.274</td>\n",
       "      <td>0.831</td>\n",
       "      <td>0.522</td>\n",
       "      <td>0.662</td>\n",
       "      <td>0.112</td>\n",
       "      <td>0.115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>Kuwait*</td>\n",
       "      <td>6.106</td>\n",
       "      <td>6.235</td>\n",
       "      <td>5.977</td>\n",
       "      <td>1.621</td>\n",
       "      <td>1.904</td>\n",
       "      <td>0.983</td>\n",
       "      <td>0.747</td>\n",
       "      <td>0.617</td>\n",
       "      <td>0.087</td>\n",
       "      <td>0.147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>Belarus*</td>\n",
       "      <td>5.821</td>\n",
       "      <td>5.950</td>\n",
       "      <td>5.693</td>\n",
       "      <td>1.811</td>\n",
       "      <td>1.562</td>\n",
       "      <td>1.157</td>\n",
       "      <td>0.629</td>\n",
       "      <td>0.342</td>\n",
       "      <td>0.040</td>\n",
       "      <td>0.282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>Turkmenistan*</td>\n",
       "      <td>5.474</td>\n",
       "      <td>5.578</td>\n",
       "      <td>5.371</td>\n",
       "      <td>1.160</td>\n",
       "      <td>1.484</td>\n",
       "      <td>1.319</td>\n",
       "      <td>0.516</td>\n",
       "      <td>0.649</td>\n",
       "      <td>0.314</td>\n",
       "      <td>0.032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>North Cyprus*</td>\n",
       "      <td>5.467</td>\n",
       "      <td>5.609</td>\n",
       "      <td>5.325</td>\n",
       "      <td>1.078</td>\n",
       "      <td>1.815</td>\n",
       "      <td>0.888</td>\n",
       "      <td>0.819</td>\n",
       "      <td>0.523</td>\n",
       "      <td>0.130</td>\n",
       "      <td>0.213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>Libya*</td>\n",
       "      <td>5.330</td>\n",
       "      <td>5.543</td>\n",
       "      <td>5.118</td>\n",
       "      <td>1.544</td>\n",
       "      <td>1.476</td>\n",
       "      <td>0.943</td>\n",
       "      <td>0.606</td>\n",
       "      <td>0.477</td>\n",
       "      <td>0.106</td>\n",
       "      <td>0.179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>Azerbaijan*</td>\n",
       "      <td>5.173</td>\n",
       "      <td>5.265</td>\n",
       "      <td>5.082</td>\n",
       "      <td>1.098</td>\n",
       "      <td>1.458</td>\n",
       "      <td>1.093</td>\n",
       "      <td>0.560</td>\n",
       "      <td>0.601</td>\n",
       "      <td>0.023</td>\n",
       "      <td>0.341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>Gambia*</td>\n",
       "      <td>5.164</td>\n",
       "      <td>5.409</td>\n",
       "      <td>4.918</td>\n",
       "      <td>2.531</td>\n",
       "      <td>0.785</td>\n",
       "      <td>0.621</td>\n",
       "      <td>0.369</td>\n",
       "      <td>0.367</td>\n",
       "      <td>0.388</td>\n",
       "      <td>0.103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>Liberia*</td>\n",
       "      <td>5.122</td>\n",
       "      <td>5.428</td>\n",
       "      <td>4.815</td>\n",
       "      <td>2.844</td>\n",
       "      <td>0.636</td>\n",
       "      <td>0.670</td>\n",
       "      <td>0.309</td>\n",
       "      <td>0.405</td>\n",
       "      <td>0.178</td>\n",
       "      <td>0.080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>Niger*</td>\n",
       "      <td>5.003</td>\n",
       "      <td>5.247</td>\n",
       "      <td>4.760</td>\n",
       "      <td>2.667</td>\n",
       "      <td>0.570</td>\n",
       "      <td>0.560</td>\n",
       "      <td>0.326</td>\n",
       "      <td>0.571</td>\n",
       "      <td>0.165</td>\n",
       "      <td>0.145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>Comoros*</td>\n",
       "      <td>4.609</td>\n",
       "      <td>4.849</td>\n",
       "      <td>4.368</td>\n",
       "      <td>2.304</td>\n",
       "      <td>0.899</td>\n",
       "      <td>0.476</td>\n",
       "      <td>0.424</td>\n",
       "      <td>0.185</td>\n",
       "      <td>0.195</td>\n",
       "      <td>0.125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>Palestinian Territories*</td>\n",
       "      <td>4.483</td>\n",
       "      <td>4.665</td>\n",
       "      <td>4.300</td>\n",
       "      <td>1.368</td>\n",
       "      <td>1.148</td>\n",
       "      <td>0.957</td>\n",
       "      <td>0.521</td>\n",
       "      <td>0.336</td>\n",
       "      <td>0.073</td>\n",
       "      <td>0.079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>Eswatini, Kingdom of*</td>\n",
       "      <td>4.396</td>\n",
       "      <td>4.607</td>\n",
       "      <td>4.185</td>\n",
       "      <td>1.688</td>\n",
       "      <td>1.274</td>\n",
       "      <td>0.786</td>\n",
       "      <td>0.197</td>\n",
       "      <td>0.259</td>\n",
       "      <td>0.038</td>\n",
       "      <td>0.154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>Madagascar*</td>\n",
       "      <td>4.339</td>\n",
       "      <td>4.530</td>\n",
       "      <td>4.148</td>\n",
       "      <td>2.148</td>\n",
       "      <td>0.670</td>\n",
       "      <td>0.645</td>\n",
       "      <td>0.378</td>\n",
       "      <td>0.202</td>\n",
       "      <td>0.143</td>\n",
       "      <td>0.154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>Chad*</td>\n",
       "      <td>4.251</td>\n",
       "      <td>4.503</td>\n",
       "      <td>3.999</td>\n",
       "      <td>2.419</td>\n",
       "      <td>0.662</td>\n",
       "      <td>0.506</td>\n",
       "      <td>0.225</td>\n",
       "      <td>0.180</td>\n",
       "      <td>0.182</td>\n",
       "      <td>0.077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>Yemen*</td>\n",
       "      <td>4.197</td>\n",
       "      <td>4.384</td>\n",
       "      <td>4.010</td>\n",
       "      <td>1.561</td>\n",
       "      <td>0.691</td>\n",
       "      <td>1.043</td>\n",
       "      <td>0.384</td>\n",
       "      <td>0.330</td>\n",
       "      <td>0.090</td>\n",
       "      <td>0.098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>Mauritania*</td>\n",
       "      <td>4.153</td>\n",
       "      <td>4.356</td>\n",
       "      <td>3.950</td>\n",
       "      <td>1.207</td>\n",
       "      <td>1.100</td>\n",
       "      <td>0.865</td>\n",
       "      <td>0.450</td>\n",
       "      <td>0.304</td>\n",
       "      <td>0.088</td>\n",
       "      <td>0.138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>Lesotho*</td>\n",
       "      <td>3.512</td>\n",
       "      <td>3.748</td>\n",
       "      <td>3.276</td>\n",
       "      <td>1.312</td>\n",
       "      <td>0.839</td>\n",
       "      <td>0.848</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.419</td>\n",
       "      <td>0.076</td>\n",
       "      <td>0.018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>Botswana*</td>\n",
       "      <td>3.471</td>\n",
       "      <td>3.667</td>\n",
       "      <td>3.275</td>\n",
       "      <td>0.187</td>\n",
       "      <td>1.503</td>\n",
       "      <td>0.815</td>\n",
       "      <td>0.280</td>\n",
       "      <td>0.571</td>\n",
       "      <td>0.012</td>\n",
       "      <td>0.102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>Rwanda*</td>\n",
       "      <td>3.268</td>\n",
       "      <td>3.462</td>\n",
       "      <td>3.074</td>\n",
       "      <td>0.536</td>\n",
       "      <td>0.785</td>\n",
       "      <td>0.133</td>\n",
       "      <td>0.462</td>\n",
       "      <td>0.621</td>\n",
       "      <td>0.187</td>\n",
       "      <td>0.544</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      country  happiness_score  whisker_high  whisker_low  \\\n",
       "5                 Luxembourg*            7.404         7.501        7.307   \n",
       "38                 Guatemala*            6.262         6.460        6.064   \n",
       "49                    Kuwait*            6.106         6.235        5.977   \n",
       "64                   Belarus*            5.821         5.950        5.693   \n",
       "77              Turkmenistan*            5.474         5.578        5.371   \n",
       "78              North Cyprus*            5.467         5.609        5.325   \n",
       "85                     Libya*            5.330         5.543        5.118   \n",
       "91                Azerbaijan*            5.173         5.265        5.082   \n",
       "92                    Gambia*            5.164         5.409        4.918   \n",
       "96                   Liberia*            5.122         5.428        4.815   \n",
       "103                    Niger*            5.003         5.247        4.760   \n",
       "115                  Comoros*            4.609         4.849        4.368   \n",
       "121  Palestinian Territories*            4.483         4.665        4.300   \n",
       "124     Eswatini, Kingdom of*            4.396         4.607        4.185   \n",
       "127               Madagascar*            4.339         4.530        4.148   \n",
       "129                     Chad*            4.251         4.503        3.999   \n",
       "131                    Yemen*            4.197         4.384        4.010   \n",
       "132               Mauritania*            4.153         4.356        3.950   \n",
       "140                  Lesotho*            3.512         3.748        3.276   \n",
       "141                 Botswana*            3.471         3.667        3.275   \n",
       "142                   Rwanda*            3.268         3.462        3.074   \n",
       "\n",
       "     dystopia  gdp_per_capita  social_support  life_expectancy  \\\n",
       "5       2.042           2.209           1.155            0.790   \n",
       "38      2.746           1.274           0.831            0.522   \n",
       "49      1.621           1.904           0.983            0.747   \n",
       "64      1.811           1.562           1.157            0.629   \n",
       "77      1.160           1.484           1.319            0.516   \n",
       "78      1.078           1.815           0.888            0.819   \n",
       "85      1.544           1.476           0.943            0.606   \n",
       "91      1.098           1.458           1.093            0.560   \n",
       "92      2.531           0.785           0.621            0.369   \n",
       "96      2.844           0.636           0.670            0.309   \n",
       "103     2.667           0.570           0.560            0.326   \n",
       "115     2.304           0.899           0.476            0.424   \n",
       "121     1.368           1.148           0.957            0.521   \n",
       "124     1.688           1.274           0.786            0.197   \n",
       "127     2.148           0.670           0.645            0.378   \n",
       "129     2.419           0.662           0.506            0.225   \n",
       "131     1.561           0.691           1.043            0.384   \n",
       "132     1.207           1.100           0.865            0.450   \n",
       "140     1.312           0.839           0.848            0.000   \n",
       "141     0.187           1.503           0.815            0.280   \n",
       "142     0.536           0.785           0.133            0.462   \n",
       "\n",
       "     free_life_choices  generosity  corruption_perception  \n",
       "5                0.700       0.120                  0.388  \n",
       "38               0.662       0.112                  0.115  \n",
       "49               0.617       0.087                  0.147  \n",
       "64               0.342       0.040                  0.282  \n",
       "77               0.649       0.314                  0.032  \n",
       "78               0.523       0.130                  0.213  \n",
       "85               0.477       0.106                  0.179  \n",
       "91               0.601       0.023                  0.341  \n",
       "92               0.367       0.388                  0.103  \n",
       "96               0.405       0.178                  0.080  \n",
       "103              0.571       0.165                  0.145  \n",
       "115              0.185       0.195                  0.125  \n",
       "121              0.336       0.073                  0.079  \n",
       "124              0.259       0.038                  0.154  \n",
       "127              0.202       0.143                  0.154  \n",
       "129              0.180       0.182                  0.077  \n",
       "131              0.330       0.090                  0.098  \n",
       "132              0.304       0.088                  0.138  \n",
       "140              0.419       0.076                  0.018  \n",
       "141              0.571       0.012                  0.102  \n",
       "142              0.621       0.187                  0.544  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Countries who's average are based on 2019 data\n",
    "df[df[\"country\"].str.contains(\"\\*\") == True]\n",
    "# TODO: Explore the 'fairness' of including these countries in the updated statistics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>happiness_score</th>\n",
       "      <th>whisker_high</th>\n",
       "      <th>whisker_low</th>\n",
       "      <th>dystopia</th>\n",
       "      <th>gdp_per_capita</th>\n",
       "      <th>social_support</th>\n",
       "      <th>life_expectancy</th>\n",
       "      <th>free_life_choices</th>\n",
       "      <th>generosity</th>\n",
       "      <th>corruption_perception</th>\n",
       "      <th>confidence</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>India</td>\n",
       "      <td>3.777</td>\n",
       "      <td>3.828</td>\n",
       "      <td>3.726</td>\n",
       "      <td>0.795</td>\n",
       "      <td>1.167</td>\n",
       "      <td>0.376</td>\n",
       "      <td>0.471</td>\n",
       "      <td>0.647</td>\n",
       "      <td>0.198</td>\n",
       "      <td>0.123</td>\n",
       "      <td>0.102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Netherlands</td>\n",
       "      <td>7.415</td>\n",
       "      <td>7.471</td>\n",
       "      <td>7.359</td>\n",
       "      <td>2.137</td>\n",
       "      <td>1.945</td>\n",
       "      <td>1.206</td>\n",
       "      <td>0.787</td>\n",
       "      <td>0.651</td>\n",
       "      <td>0.271</td>\n",
       "      <td>0.419</td>\n",
       "      <td>0.112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Israel</td>\n",
       "      <td>7.364</td>\n",
       "      <td>7.426</td>\n",
       "      <td>7.301</td>\n",
       "      <td>2.634</td>\n",
       "      <td>1.826</td>\n",
       "      <td>1.221</td>\n",
       "      <td>0.818</td>\n",
       "      <td>0.568</td>\n",
       "      <td>0.155</td>\n",
       "      <td>0.143</td>\n",
       "      <td>0.125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Finland</td>\n",
       "      <td>7.821</td>\n",
       "      <td>7.886</td>\n",
       "      <td>7.756</td>\n",
       "      <td>2.518</td>\n",
       "      <td>1.892</td>\n",
       "      <td>1.258</td>\n",
       "      <td>0.775</td>\n",
       "      <td>0.736</td>\n",
       "      <td>0.109</td>\n",
       "      <td>0.534</td>\n",
       "      <td>0.130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>China</td>\n",
       "      <td>5.585</td>\n",
       "      <td>5.650</td>\n",
       "      <td>5.520</td>\n",
       "      <td>1.516</td>\n",
       "      <td>1.508</td>\n",
       "      <td>0.958</td>\n",
       "      <td>0.705</td>\n",
       "      <td>0.656</td>\n",
       "      <td>0.099</td>\n",
       "      <td>0.142</td>\n",
       "      <td>0.130</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>115</th>\n",
       "      <td>Comoros*</td>\n",
       "      <td>4.609</td>\n",
       "      <td>4.849</td>\n",
       "      <td>4.368</td>\n",
       "      <td>2.304</td>\n",
       "      <td>0.899</td>\n",
       "      <td>0.476</td>\n",
       "      <td>0.424</td>\n",
       "      <td>0.185</td>\n",
       "      <td>0.195</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>Niger*</td>\n",
       "      <td>5.003</td>\n",
       "      <td>5.247</td>\n",
       "      <td>4.760</td>\n",
       "      <td>2.667</td>\n",
       "      <td>0.570</td>\n",
       "      <td>0.560</td>\n",
       "      <td>0.326</td>\n",
       "      <td>0.571</td>\n",
       "      <td>0.165</td>\n",
       "      <td>0.145</td>\n",
       "      <td>0.487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>Gambia*</td>\n",
       "      <td>5.164</td>\n",
       "      <td>5.409</td>\n",
       "      <td>4.918</td>\n",
       "      <td>2.531</td>\n",
       "      <td>0.785</td>\n",
       "      <td>0.621</td>\n",
       "      <td>0.369</td>\n",
       "      <td>0.367</td>\n",
       "      <td>0.388</td>\n",
       "      <td>0.103</td>\n",
       "      <td>0.491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>Chad*</td>\n",
       "      <td>4.251</td>\n",
       "      <td>4.503</td>\n",
       "      <td>3.999</td>\n",
       "      <td>2.419</td>\n",
       "      <td>0.662</td>\n",
       "      <td>0.506</td>\n",
       "      <td>0.225</td>\n",
       "      <td>0.180</td>\n",
       "      <td>0.182</td>\n",
       "      <td>0.077</td>\n",
       "      <td>0.504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>Liberia*</td>\n",
       "      <td>5.122</td>\n",
       "      <td>5.428</td>\n",
       "      <td>4.815</td>\n",
       "      <td>2.844</td>\n",
       "      <td>0.636</td>\n",
       "      <td>0.670</td>\n",
       "      <td>0.309</td>\n",
       "      <td>0.405</td>\n",
       "      <td>0.178</td>\n",
       "      <td>0.080</td>\n",
       "      <td>0.613</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>146 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         country  happiness_score  whisker_high  whisker_low  dystopia  \\\n",
       "135        India            3.777         3.828        3.726     0.795   \n",
       "4    Netherlands            7.415         7.471        7.359     2.137   \n",
       "8         Israel            7.364         7.426        7.301     2.634   \n",
       "0        Finland            7.821         7.886        7.756     2.518   \n",
       "71         China            5.585         5.650        5.520     1.516   \n",
       "..           ...              ...           ...          ...       ...   \n",
       "115     Comoros*            4.609         4.849        4.368     2.304   \n",
       "103       Niger*            5.003         5.247        4.760     2.667   \n",
       "92       Gambia*            5.164         5.409        4.918     2.531   \n",
       "129        Chad*            4.251         4.503        3.999     2.419   \n",
       "96      Liberia*            5.122         5.428        4.815     2.844   \n",
       "\n",
       "     gdp_per_capita  social_support  life_expectancy  free_life_choices  \\\n",
       "135           1.167           0.376            0.471              0.647   \n",
       "4             1.945           1.206            0.787              0.651   \n",
       "8             1.826           1.221            0.818              0.568   \n",
       "0             1.892           1.258            0.775              0.736   \n",
       "71            1.508           0.958            0.705              0.656   \n",
       "..              ...             ...              ...                ...   \n",
       "115           0.899           0.476            0.424              0.185   \n",
       "103           0.570           0.560            0.326              0.571   \n",
       "92            0.785           0.621            0.369              0.367   \n",
       "129           0.662           0.506            0.225              0.180   \n",
       "96            0.636           0.670            0.309              0.405   \n",
       "\n",
       "     generosity  corruption_perception  confidence  \n",
       "135       0.198                  0.123       0.102  \n",
       "4         0.271                  0.419       0.112  \n",
       "8         0.155                  0.143       0.125  \n",
       "0         0.109                  0.534       0.130  \n",
       "71        0.099                  0.142       0.130  \n",
       "..          ...                    ...         ...  \n",
       "115       0.195                  0.125       0.481  \n",
       "103       0.165                  0.145       0.487  \n",
       "92        0.388                  0.103       0.491  \n",
       "129       0.182                  0.077       0.504  \n",
       "96        0.178                  0.080       0.613  \n",
       "\n",
       "[146 rows x 12 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Adding confidence column\n",
    "df[\"confidence\"] = df[\"whisker_high\"] - df[\"whisker_low\"]\n",
    "df.sort_values(\"confidence\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hypothesis\n",
    "- No missing data\n",
    "- No outliers"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualisation"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Stacked barplot with confidence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>happiness_score</th>\n",
       "      <th>whisker_high</th>\n",
       "      <th>whisker_low</th>\n",
       "      <th>dystopia</th>\n",
       "      <th>gdp_per_capita</th>\n",
       "      <th>social_support</th>\n",
       "      <th>life_expectancy</th>\n",
       "      <th>free_life_choices</th>\n",
       "      <th>generosity</th>\n",
       "      <th>corruption_perception</th>\n",
       "      <th>confidence</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Finland</td>\n",
       "      <td>7.821</td>\n",
       "      <td>7.886</td>\n",
       "      <td>7.756</td>\n",
       "      <td>2.518</td>\n",
       "      <td>1.892</td>\n",
       "      <td>1.258</td>\n",
       "      <td>0.775</td>\n",
       "      <td>0.736</td>\n",
       "      <td>0.109</td>\n",
       "      <td>0.534</td>\n",
       "      <td>0.130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Denmark</td>\n",
       "      <td>7.636</td>\n",
       "      <td>7.710</td>\n",
       "      <td>7.563</td>\n",
       "      <td>2.226</td>\n",
       "      <td>1.953</td>\n",
       "      <td>1.243</td>\n",
       "      <td>0.777</td>\n",
       "      <td>0.719</td>\n",
       "      <td>0.188</td>\n",
       "      <td>0.532</td>\n",
       "      <td>0.147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Iceland</td>\n",
       "      <td>7.557</td>\n",
       "      <td>7.651</td>\n",
       "      <td>7.464</td>\n",
       "      <td>2.320</td>\n",
       "      <td>1.936</td>\n",
       "      <td>1.320</td>\n",
       "      <td>0.803</td>\n",
       "      <td>0.718</td>\n",
       "      <td>0.270</td>\n",
       "      <td>0.191</td>\n",
       "      <td>0.187</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   country  happiness_score  whisker_high  whisker_low  dystopia  \\\n",
       "0  Finland            7.821         7.886        7.756     2.518   \n",
       "1  Denmark            7.636         7.710        7.563     2.226   \n",
       "2  Iceland            7.557         7.651        7.464     2.320   \n",
       "\n",
       "   gdp_per_capita  social_support  life_expectancy  free_life_choices  \\\n",
       "0           1.892           1.258            0.775              0.736   \n",
       "1           1.953           1.243            0.777              0.719   \n",
       "2           1.936           1.320            0.803              0.718   \n",
       "\n",
       "   generosity  corruption_perception  confidence  \n",
       "0       0.109                  0.534       0.130  \n",
       "1       0.188                  0.532       0.147  \n",
       "2       0.270                  0.191       0.187  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>gdp_per_capita</th>\n",
       "      <th>social_support</th>\n",
       "      <th>life_expectancy</th>\n",
       "      <th>free_life_choices</th>\n",
       "      <th>generosity</th>\n",
       "      <th>corruption_perception</th>\n",
       "      <th>dystopia</th>\n",
       "      <th>happiness_score</th>\n",
       "      <th>lower</th>\n",
       "      <th>upper</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Afghanistan</th>\n",
       "      <td>0.758</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.289</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.089</td>\n",
       "      <td>0.005</td>\n",
       "      <td>1.263</td>\n",
       "      <td>2.404</td>\n",
       "      <td>2.3390</td>\n",
       "      <td>2.4690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lebanon</th>\n",
       "      <td>1.392</td>\n",
       "      <td>0.498</td>\n",
       "      <td>0.631</td>\n",
       "      <td>0.103</td>\n",
       "      <td>0.082</td>\n",
       "      <td>0.034</td>\n",
       "      <td>0.216</td>\n",
       "      <td>2.955</td>\n",
       "      <td>2.8615</td>\n",
       "      <td>3.0485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zimbabwe</th>\n",
       "      <td>0.947</td>\n",
       "      <td>0.690</td>\n",
       "      <td>0.270</td>\n",
       "      <td>0.329</td>\n",
       "      <td>0.106</td>\n",
       "      <td>0.105</td>\n",
       "      <td>0.548</td>\n",
       "      <td>2.995</td>\n",
       "      <td>2.8800</td>\n",
       "      <td>3.1100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             gdp_per_capita  social_support  life_expectancy  \\\n",
       "country                                                        \n",
       "Afghanistan           0.758           0.000            0.289   \n",
       "Lebanon               1.392           0.498            0.631   \n",
       "Zimbabwe              0.947           0.690            0.270   \n",
       "\n",
       "             free_life_choices  generosity  corruption_perception  dystopia  \\\n",
       "country                                                                       \n",
       "Afghanistan              0.000       0.089                  0.005     1.263   \n",
       "Lebanon                  0.103       0.082                  0.034     0.216   \n",
       "Zimbabwe                 0.329       0.106                  0.105     0.548   \n",
       "\n",
       "             happiness_score   lower   upper  \n",
       "country                                       \n",
       "Afghanistan            2.404  2.3390  2.4690  \n",
       "Lebanon                2.955  2.8615  3.0485  \n",
       "Zimbabwe               2.995  2.8800  3.1100  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_factors = df.copy()\n",
    "df_factors.drop(axis=1, columns=[\"dystopia\",\"whisker_high\",\"whisker_low\",\"happiness_score\",\"confidence\"], inplace=True)\n",
    "df_factors[\"dystopia\"] = df[\"dystopia\"]\n",
    "df_factors[\"happiness_score\"] = df[\"happiness_score\"]\n",
    "df_factors[\"lower\"] = df[\"happiness_score\"] - df[\"confidence\"]/2\n",
    "df_factors[\"upper\"] = df[\"happiness_score\"] + df[\"confidence\"]/2\n",
    "df_factors.sort_values(\"happiness_score\",inplace=True)\n",
    "df_i = df_factors.set_index(\"country\").copy()\n",
    "df_i.head(3)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_factors.to_csv(\"data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
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     "metadata": {},
     "output_type": "display_data"
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 const render_items = [{\"docid\":\"d2a9d73d-f4cb-41ba-aa02-17ea71f9eab4\",\"roots\":{\"p1611\":\"f7af6f8e-ac28-4be6-aa24-f49e4c77451f\"},\"root_ids\":[\"p1611\"]}];\n  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n  }\n  if (root.Bokeh !== undefined) {\n    embed_document(root);\n  } else {\n    let attempts = 0;\n    const timer = setInterval(function(root) {\n      if (root.Bokeh !== undefined) {\n        clearInterval(timer);\n        embed_document(root);\n      } else {\n        attempts++;\n        if (attempts > 100) {\n          clearInterval(timer);\n          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n        }\n      }\n    }, 10, root)\n  }\n})(window);",
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "p1611"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from bokeh.models import ColumnDataSource,LabelSet,Whisker\n",
    "from bokeh.palettes import Category10_7\n",
    "from bokeh.io import curdoc\n",
    "\n",
    "# apply theme to current document\n",
    "curdoc().theme = \"dark_minimal\"\n",
    "\n",
    "# TODO: Make the legend dynamic, aka filter by factor.\n",
    "# TODO: Make the label dynamic for above todo & also figure out how to format to 3sf\n",
    "\n",
    "# Vars\n",
    "WIDTH,HEIGHT = 1300,2300\n",
    "TOOLS = \"crosshair,pan,wheel_zoom,box_zoom,reset,hover,save\"\n",
    "TOOLTIPS = [\n",
    "    (\"country:\",\"@country\"),\n",
    "    (\"happiness:\",\"@happiness_score\"),\n",
    "    (\"GDP per Capita:\", \"@gdp_per_capita\")\n",
    "]\n",
    "\n",
    "factors = list(df_i.columns)[:-3]\n",
    "country = list(df_i.index)\n",
    "source = ColumnDataSource(df_i)\n",
    "\n",
    "# Plot\n",
    "p = figure(\n",
    "    width=WIDTH,\n",
    "    height=HEIGHT,\n",
    "    y_range = country,\n",
    "    title = \"Happiness score\",\n",
    "    tools=TOOLS,\n",
    "    tooltips=TOOLTIPS\n",
    ");\n",
    "\n",
    "p.y_range.range_padding = 0.05\n",
    "p.x_range.range_padding = 0\n",
    "\n",
    "\n",
    "# Stacked horizontal bar\n",
    "p.hbar_stack(\n",
    "    factors,\n",
    "    source=source,\n",
    "    y=\"country\",\n",
    "    color=Category10_7,\n",
    "    legend_label=factors,\n",
    "    alpha=0.6,\n",
    "    height=0.9\n",
    ")\n",
    "\n",
    "# Add label\n",
    "labels = LabelSet(\n",
    "    x=0.1, y='country',\n",
    "    text='happiness_score',\n",
    "    x_offset=0, y_offset=-3,\n",
    "    source=source,\n",
    "    text_font_size = \"10px\",\n",
    "    text_color=\"white\"\n",
    ")\n",
    "p.add_layout(labels);\n",
    "\n",
    "p.legend.orientation = \"horizontal\"\n",
    "p.legend.location = \"Wtop_center\"\n",
    "show(p)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) {window.MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-2.17.1.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "linkText": "Export to plot.ly",
        "plotlyServerURL": "https://plot.ly",
        "showLink": false
       },
       "data": [
        {
         "colorbar": {
          "title": {
           "text": "Happiness scale"
          }
         },
         "colorscale": [
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           0,
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          ],
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          ],
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         ],
         "locationmode": "country names",
         "locations": [
          "Finland",
          "Denmark",
          "Iceland",
          "Switzerland",
          "Netherlands",
          "Luxembourg*",
          "Sweden",
          "Norway",
          "Israel",
          "New Zealand",
          "Austria",
          "Australia",
          "Ireland",
          "Germany",
          "Canada",
          "United States",
          "United Kingdom",
          "Czechia",
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          "Slovenia",
          "Costa Rica",
          "United Arab Emirates",
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          "Taiwan Province of China",
          "Singapore",
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          "Panama",
          "Brazil",
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          "Kazakhstan",
          "Cyprus",
          "Latvia",
          "Serbia",
          "Chile",
          "Nicaragua",
          "Mexico",
          "Croatia",
          "Poland",
          "El Salvador",
          "Kuwait*",
          "Hungary",
          "Mauritius",
          "Uzbekistan",
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          "Honduras",
          "Portugal",
          "Argentina",
          "Greece",
          "South Korea",
          "Philippines",
          "Thailand",
          "Moldova",
          "Jamaica",
          "Kyrgyzstan",
          "Belarus*",
          "Colombia",
          "Bosnia and Herzegovina",
          "Mongolia",
          "Dominican Republic",
          "Malaysia",
          "Bolivia",
          "China",
          "Paraguay",
          "Peru",
          "Montenegro",
          "Ecuador",
          "Vietnam",
          "Turkmenistan*",
          "North Cyprus*",
          "Russia",
          "Hong Kong S.A.R. of China",
          "Armenia",
          "Tajikistan",
          "Nepal",
          "Bulgaria",
          "Libya*",
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          "Rwanda*",
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         ],
         "reversescale": false,
         "text": [
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Macedonia\",\"Albania\",\"South Africa\",\"Azerbaijan*\",\"Gambia*\",\"Bangladesh\",\"Laos\",\"Algeria\",\"Liberia*\",\"Ukraine\",\"Congo\",\"Morocco\",\"Mozambique\",\"Cameroon\",\"Senegal\",\"Niger*\",\"Georgia\",\"Gabon\",\"Iraq\",\"Venezuela\",\"Guinea\",\"Iran\",\"Ghana\",\"Turkey\",\"Burkina Faso\",\"Cambodia\",\"Benin\",\"Comoros*\",\"Uganda\",\"Nigeria\",\"Kenya\",\"Tunisia\",\"Pakistan\",\"Palestinian Territories*\",\"Mali\",\"Namibia\",\"Eswatini, Kingdom of*\",\"Myanmar\",\"Sri Lanka\",\"Madagascar*\",\"Egypt\",\"Chad*\",\"Ethiopia\",\"Yemen*\",\"Mauritania*\",\"Jordan\",\"Togo\",\"India\",\"Zambia\",\"Malawi\",\"Tanzania\",\"Sierra Leone\",\"Lesotho*\",\"Botswana*\",\"Rwanda*\",\"Zimbabwe\",\"Lebanon\",\"Afghanistan\"],\"z\":[7.821000099182129,7.636000156402588,7.557000160217285,7.51200008392334,7.414999961853027,7.4039998054504395,7.383999824523926,7.364999771118164,7.363999843597412,7.199999809265137,7.163000106811523,7.1620001792907715,7.040999889373779,7.033999919891357,7.025000095367432,6.9770002365112305,6.942999839782715,6.920000076293945,6.804999828338623,6.686999797821045,6.646999835968018,6.630000114440918,6.581999778747559,6.576000213623047,6.5229997634887695,6.51200008392334,6.480000019073486,6.4770002365112305,6.47599983215332,6.473999977111816,6.4670000076293945,6.454999923706055,6.447000026702881,6.446000099182129,6.390999794006348,6.341000080108643,6.309000015258789,6.293000221252441,6.26200008392334,6.234000205993652,6.2210001945495605,6.179999828338623,6.177999973297119,6.171999931335449,6.164999961853027,6.127999782562256,6.125,6.123000144958496,6.119999885559082,6.105999946594238,6.085999965667725,6.071000099182129,6.063000202178955,6.039000034332275,6.021999835968018,6.015999794006348,5.9670000076293945,5.947999954223633,5.934999942779541,5.9039998054504395,5.890999794006348,5.85699987411499,5.849999904632568,5.828000068664551,5.821000099182129,5.781000137329102,5.76800012588501,5.761000156402588,5.736999988555908,5.710999965667725,5.599999904632568,5.585000038146973,5.578000068664551,5.559000015258789,5.546999931335449,5.5329999923706055,5.485000133514404,5.473999977111816,5.4670000076293945,5.459000110626221,5.425000190734863,5.39900016784668,5.376999855041504,5.376999855041504,5.370999813079834,5.329999923706055,5.239999771118164,5.235000133514404,5.198999881744385,5.198999881744385,5.193999767303467,5.172999858856201,5.164000034332275,5.15500020980835,5.139999866485596,5.122000217437744,5.122000217437744,5.084000110626221,5.074999809265137,5.059999942779541,5.047999858856201,5.047999858856201,5.046000003814697,5.002999782562256,4.9730000495910645,4.958000183105469,4.940999984741211,4.925000190734863,4.890999794006348,4.888000011444092,4.872000217437744,4.74399995803833,4.670000076293945,4.639999866485596,4.623000144958496,4.609000205993652,4.603000164031982,4.552000045776367,4.543000221252441,4.515999794006348,4.515999794006348,4.482999801635742,4.479000091552734,4.459000110626221,4.395999908447266,4.394000053405762,4.361999988555908,4.339000225067139,4.288000106811523,4.250999927520752,4.241000175476074,4.197000026702881,4.1529998779296875,4.1519999504089355,4.111999988555908,3.7769999504089355,3.759999990463257,3.75,3.7019999027252197,3.5739998817443848,3.51200008392334,3.4709999561309814,3.2679998874664307,2.994999885559082,2.9549999237060547,2.4040000438690186],\"type\":\"choropleth\"}],                        {\"geo\":{\"projection\":{\"type\":\"natural earth\"},\"showframe\":false},\"title\":{\"text\":\"Global Happiness of countries\"},\"template\":{\"data\":{\"barpolar\":[{\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"barpolar\"}],\"bar\":[{\"error_x\":{\"color\":\"#2a3f5f\"},\"error_y\":{\"color\":\"#2a3f5f\"},\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"bar\"}],\"carpet\":[{\"aaxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"baxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"type\":\"carpet\"}],\"choropleth\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"choropleth\"}],\"contourcarpet\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"contourcarpet\"}],\"contour\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"contour\"}],\"heatmapgl\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"heatmapgl\"}],\"heatmap\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"heatmap\"}],\"histogram2dcontour\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"histogram2dcontour\"}],\"histogram2d\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"histogram2d\"}],\"histogram\":[{\"marker\":{\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"histogram\"}],\"mesh3d\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"mesh3d\"}],\"parcoords\":[{\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"parcoords\"}],\"pie\":[{\"automargin\":true,\"type\":\"pie\"}],\"scatter3d\":[{\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatter3d\"}],\"scattercarpet\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattercarpet\"}],\"scattergeo\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattergeo\"}],\"scattergl\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattergl\"}],\"scattermapbox\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattermapbox\"}],\"scatterpolargl\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterpolargl\"}],\"scatterpolar\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterpolar\"}],\"scatter\":[{\"fillpattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2},\"type\":\"scatter\"}],\"scatterternary\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterternary\"}],\"surface\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"surface\"}],\"table\":[{\"cells\":{\"fill\":{\"color\":\"#EBF0F8\"},\"line\":{\"color\":\"white\"}},\"header\":{\"fill\":{\"color\":\"#C8D4E3\"},\"line\":{\"color\":\"white\"}},\"type\":\"table\"}]},\"layout\":{\"annotationdefaults\":{\"arrowcolor\":\"#2a3f5f\",\"arrowhead\":0,\"arrowwidth\":1},\"autotypenumbers\":\"strict\",\"coloraxis\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"colorscale\":{\"diverging\":[[0,\"#8e0152\"],[0.1,\"#c51b7d\"],[0.2,\"#de77ae\"],[0.3,\"#f1b6da\"],[0.4,\"#fde0ef\"],[0.5,\"#f7f7f7\"],[0.6,\"#e6f5d0\"],[0.7,\"#b8e186\"],[0.8,\"#7fbc41\"],[0.9,\"#4d9221\"],[1,\"#276419\"]],\"sequential\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"sequentialminus\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]},\"colorway\":[\"#636efa\",\"#EF553B\",\"#00cc96\",\"#ab63fa\",\"#FFA15A\",\"#19d3f3\",\"#FF6692\",\"#B6E880\",\"#FF97FF\",\"#FECB52\"],\"font\":{\"color\":\"#2a3f5f\"},\"geo\":{\"bgcolor\":\"white\",\"lakecolor\":\"white\",\"landcolor\":\"#E5ECF6\",\"showlakes\":true,\"showland\":true,\"subunitcolor\":\"white\"},\"hoverlabel\":{\"align\":\"left\"},\"hovermode\":\"closest\",\"mapbox\":{\"style\":\"light\"},\"paper_bgcolor\":\"rgba(0,0,0,0)\",\"plot_bgcolor\":\"rgba(0,0,0,0)\",\"polar\":{\"angularaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"bgcolor\":\"#E5ECF6\",\"radialaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"scene\":{\"xaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"},\"yaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"},\"zaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"}},\"shapedefaults\":{\"line\":{\"color\":\"#2a3f5f\"}},\"ternary\":{\"aaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"baxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"bgcolor\":\"#E5ECF6\",\"caxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"title\":{\"x\":0.05},\"xaxis\":{\"automargin\":true,\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"zerolinewidth\":2},\"yaxis\":{\"automargin\":true,\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"zerolinewidth\":2}}},\"margin\":{\"r\":0,\"t\":0,\"l\":0,\"b\":0},\"width\":1300,\"height\":650,\"autosize\":true,\"plot_bgcolor\":\"rgb(17,17,17)\",\"paper_bgcolor\":\"rgb(10,10,10)\"},                        {\"responsive\": true}                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('3d25182e-3b39-41ae-aaca-0569c56346e3');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
       "        var display = window.getComputedStyle(gd).display;\n",
       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
       "}}\n",
       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
       "}}\n",
       "\n",
       "                        })                };                });            </script>        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import plotly.graph_objects as go\n",
    "from plotly.offline import init_notebook_mode, iplot, plot\n",
    "init_notebook_mode(connected=True)\n",
    "\n",
    "# TODO: Figure out how to make the api map dark theme\n",
    "# TODO: Create widget that allows user to switch factors.\n",
    "\n",
    "import plotly.io as pio\n",
    "pio.templates['custom'] = go.layout.Template(\n",
    "    layout_paper_bgcolor='rgba(0,0,0,0)',\n",
    "    layout_plot_bgcolor='rgba(0,0,0,0)'\n",
    "    )\n",
    "pio.templates.default = 'plotly+custom'\n",
    "\n",
    "MY_COLORSCALE = [\n",
    "    [0.0, \"rgb(165,0,38)\"],\n",
    "    [0.1111111111111111, \"rgb(215,48,39)\"],\n",
    "    [0.2222222222222222, \"rgb(244,109,67)\"],\n",
    "    [0.3333333333333333, \"rgb(253,174,97)\"],\n",
    "    [0.4444444444444444, \"rgb(254,224,144)\"],\n",
    "    [0.5555555555555556, \"rgb(224,243,248)\"],\n",
    "    [0.6666666666666666, \"rgb(171,217,233)\"],\n",
    "    [0.7777777777777778, \"rgb(116,173,209)\"],\n",
    "    [0.8888888888888888, \"rgb(69,117,180)\"],\n",
    "    [1.0, \"rgb(49,54,149)\"]\n",
    "]\n",
    "\n",
    "data=dict(\n",
    "    type=\"choropleth\",\n",
    "    colorscale=MY_COLORSCALE,\n",
    "    reversescale=True,\n",
    "    locations=df['country'],\n",
    "    locationmode=\"country names\",\n",
    "    z=df[\"happiness_score\"],\n",
    "    text=df[\"country\"],\n",
    "    colorbar={'title':'Happiness scale'}\n",
    ")\n",
    "\n",
    "layout=dict(\n",
    "    title='Global Happiness of countries',\n",
    "    geo=dict(showframe=False,projection={'type':'natural earth'}),\n",
    ")\n",
    "\n",
    "fig=go.Figure(data=data,layout=layout)\n",
    "\n",
    "fig.update_layout(\n",
    "    width = 1300, height=650,\n",
    "    autosize=True,\n",
    "    margin = {\"r\":0,\"t\":0,\"l\":0,\"b\":0},\n",
    "    plot_bgcolor='rgb(17,17,17)',\n",
    "    paper_bgcolor ='rgb(10,10,10)'\n",
    ")\n",
    "\n",
    "fig.update_traces(reversescale=False)\n",
    "iplot(fig, validate=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create Bokeh-Table with DataFrame:\n",
    "from bokeh.models.widgets import DataTable, TableColumn\n",
    "from bokeh.models import ColumnDataSource\n",
    "\n",
    "data_table = DataTable(\n",
    "    columns=[TableColumn(field=Ci, title=Ci) for Ci in df_i.columns],\n",
    "    source=ColumnDataSource(df_i),\n",
    "    height=300,width=50\n",
    ")\n",
    "\n",
    "# Combine Table and Scatterplot via grid layout:\n",
    "# pandas_bokeh.plot_grid([[data_table, p]], width=600, height=1700)\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ideas from other contributers"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Notebook layout design & styling:\n",
    "https://www.kaggle.com/code/georgyzubkov/world-happy-exploratory-data-analysis-with-plotly#list-tab"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <h1 style='background:#FF69B4; border:2; border-radius: 10px; font-size:250%; font-weight: bold; color:black'><center>World happiness report 2022</center></h1>\n",
    "\n",
    "The Happy Planet Index (HPI) is an index of human well-being and environmental impact that was introduced by the New Economics Foundation in 2006. Each country's HPI value is a function of its average subjective life satisfaction, life expectancy at birth, and ecological footprint per capita. The exact function is a little more complex, but conceptually it approximates multiplying life satisfaction and life expectancy and dividing that by the ecological footprint. The index is weighted to give progressively higher scores to nations with lower ecological footprints.\n",
    "\n",
    "The index is designed to challenge well-established indices of countries’ development, such as the gross domestic product (GDP) and the Human Development Index (HDI), which are seen as not taking sustainability into account. In particular, GDP is seen as inappropriate, as the usual ultimate aim of most people is not to be rich, but to be happy and healthy. Furthermore, it is believed that the notion of sustainable development requires a measure of the environmental costs of pursuing those goals.\n",
    "\n",
    "Out of the 178 countries surveyed in 2006, the best scoring countries were Vanuatu, Colombia, Costa Rica, Dominica, and Panama. In 2009, Costa Rica was the best scoring country among the 143 analyzed, followed by the Dominican Republic, Jamaica, Guatemala and Vietnam. Tanzania, Botswana and Zimbabwe were featured at the bottom of the list.\n",
    "\n",
    "For the 2012 ranking, 151 countries were compared, and the best scoring country for the second time in a row was Costa Rica, followed by Vietnam, Colombia, Belize and El Salvador. The lowest ranking countries in 2012 were Botswana, Chad and Qatar. In 2016, out of 140 countries, Costa Rica topped the index for the third time in a row. It was followed by Mexico, Colombia, Vanuatu and Vietnam. At the bottom were Chad, Luxembourg and Togo.\n",
    "\n",
    "<a id='top'></a>\n",
    "<div class=\"list-group\" id=\"list-tab\" role=\"tablist\">\n",
    "\n",
    "<h1 style='background:#FF69B4; border:0; border-radius: 10px; color:black'><center> TABLE OF CONTENTS </center></h1>\n",
    "\n",
    "### [**1. IMPORTING LIBRARIES AND LOADING DATA**](#title-one)\n",
    "\n",
    "### [**2. DATA INFORMATION**](#title-two)\n",
    "\n",
    "### [**3. EXPLORATORY DATA ANALYSIS**](#title-three)\n",
    "\n",
    "### [**4. STATISTICAL TESTS**](#title-four) \n",
    "\n",
    "### [**5. MACHINE LEARNING**](#title-five)\n",
    "\n",
    "### [**6. RECOMENDATIONS**](#title-six)\n",
    "\n",
    "<a id=\"title-one\"></a>\n",
    "<h1 style='background:#FF69B4; border:2; border-radius: 10px; color:black'><center>IMPORTING LIBRARIES AND LOADING DATA</center></h1>"
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