main.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualização da Informação\n",
    "## Escola de Matemática Aplicada - Fundação Getúlio Vargas\n",
    "## Mestrado em Modelagem Matemática\n",
    "\n",
    "Aluno: Gianlucca Devigili<br>\n",
    "Github do projeto: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import plotly\n",
    "import plotly.express as px\n",
    "from source.utils import *\n",
    "import pycountry\n",
    "\n",
    "df = pd.read_csv('data/world-happiness-report.csv')\n",
    "df2021 = pd.read_csv('data/world-happiness-report-2021.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Preparation\n",
    "\n",
    "#### Renaming columns and 2021 data to the original dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['Country name', 'year', 'Ladder score', 'Log GDP per capita',\n",
      "       'Social support', 'Healthy life expectancy at birth',\n",
      "       'Freedom to make life choices', 'Generosity',\n",
      "       'Perceptions of corruption'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "# rename columns to make the dfs match\n",
    "df.rename(columns = {\n",
    "    'Life Ladder': 'Ladder score'\n",
    "}, inplace=True)\n",
    "\n",
    "df2021.rename(columns = {\n",
    "    'Healthy life expectancy': 'Healthy life expectancy at birth',\n",
    "    'Logged GDP per capita': 'Log GDP per capita'\n",
    "}, inplace=True)\n",
    "\n",
    "# set year of 2021's df\n",
    "df2021['year'] = 2021\n",
    "\n",
    "# drop columns that aren't in 2021 data\n",
    "df = df.drop(['Positive affect', 'Negative affect'], axis=1)\n",
    "\n",
    "# append 2021 data to df\n",
    "df = df.append(df2021[df.columns]);\n",
    "\n",
    "# defines new names for the columns\n",
    "print(df.columns)\n",
    "df.columns = ['country', 'year', 'happiness_score', 'gdp_per_capita', 'social_support', 'life_expectancy', 'freedom_of_choiche', 'generosity', 'corruption_perception']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Add highlights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "highlight = []\n",
    "\n",
    "# iterate over the df rows\n",
    "for index, row in df.iterrows():\n",
    "\n",
    "    # highlight the datapoint if it's the 2020's or 2021's datapoint\n",
    "    if row.year in [2020, 2021]:\n",
    "        highlight.append(row['year'])\n",
    "    else:\n",
    "        highlight.append('Other years')\n",
    "\n",
    "df['highlight'] = highlight\n",
    "\n",
    "df.to_pickle('data/df_happiness.pkl')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Adding the mean by country"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_mean = pd.DataFrame(columns=df.columns)\n",
    "\n",
    "for country in df['country'].unique():\n",
    "    new_col = df[df['country'] == country].groupby('country').mean()\n",
    "    new_col['year'] = None\n",
    "    new_col['country'] = country\n",
    "    new_col['highlight'] = 'Mean score'\n",
    "    df_mean = df_mean.append(new_col, ignore_index=True)\n",
    "\n",
    "df = df.append(df_mean, ignore_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Adding the Regional Indicator column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2021.rename(columns = {'Country name':'country'}, inplace=True)\n",
    "\n",
    "# create a dataframe with the region for each country\n",
    "df_region = {}\n",
    "for country in df2021['country'].unique():\n",
    "    df_region[country] = df2021[df2021['country'] == country]['Regional indicator'].values[0]\n",
    "\n",
    "# create a new column in the dataframe with the region for each country\n",
    "lst_region = []\n",
    "for index, row in df.iterrows():\n",
    "    try:\n",
    "        lst_region.append(df_region[row['country']])\n",
    "    except KeyError:\n",
    "        lst_region.append(None) \n",
    "\n",
    "# concat the series to the dataframe\n",
    "df['regional_indicator'] = lst_region\n",
    "\n",
    "# fill NA values with 'Other'\n",
    "df['regional_indicator'] = df['regional_indicator'].fillna('Other')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Dataframe Info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2264 entries, 0 to 2263\n",
      "Data columns (total 11 columns):\n",
      " #   Column                 Non-Null Count  Dtype  \n",
      "---  ------                 --------------  -----  \n",
      " 0   country                2264 non-null   object \n",
      " 1   year                   2098 non-null   object \n",
      " 2   happiness_score        2264 non-null   float64\n",
      " 3   gdp_per_capita         2224 non-null   float64\n",
      " 4   social_support         2250 non-null   float64\n",
      " 5   life_expectancy        2208 non-null   float64\n",
      " 6   freedom_of_choiche     2232 non-null   float64\n",
      " 7   generosity             2171 non-null   float64\n",
      " 8   corruption_perception  2152 non-null   float64\n",
      " 9   highlight              2264 non-null   object \n",
      " 10  regional_indicator     2264 non-null   object \n",
      "dtypes: float64(7), object(4)\n",
      "memory usage: 194.7+ KB\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th>happiness_score</th>\n",
       "      <th>gdp_per_capita</th>\n",
       "      <th>social_support</th>\n",
       "      <th>life_expectancy</th>\n",
       "      <th>freedom_of_choiche</th>\n",
       "      <th>generosity</th>\n",
       "      <th>corruption_perception</th>\n",
       "      <th>highlight</th>\n",
       "      <th>regional_indicator</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2008</td>\n",
       "      <td>3.724</td>\n",
       "      <td>7.370</td>\n",
       "      <td>0.451</td>\n",
       "      <td>50.80</td>\n",
       "      <td>0.718</td>\n",
       "      <td>0.168</td>\n",
       "      <td>0.882</td>\n",
       "      <td>Other years</td>\n",
       "      <td>South Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2009</td>\n",
       "      <td>4.402</td>\n",
       "      <td>7.540</td>\n",
       "      <td>0.552</td>\n",
       "      <td>51.20</td>\n",
       "      <td>0.679</td>\n",
       "      <td>0.190</td>\n",
       "      <td>0.850</td>\n",
       "      <td>Other years</td>\n",
       "      <td>South Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2010</td>\n",
       "      <td>4.758</td>\n",
       "      <td>7.647</td>\n",
       "      <td>0.539</td>\n",
       "      <td>51.60</td>\n",
       "      <td>0.600</td>\n",
       "      <td>0.121</td>\n",
       "      <td>0.707</td>\n",
       "      <td>Other years</td>\n",
       "      <td>South Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2011</td>\n",
       "      <td>3.832</td>\n",
       "      <td>7.620</td>\n",
       "      <td>0.521</td>\n",
       "      <td>51.92</td>\n",
       "      <td>0.496</td>\n",
       "      <td>0.162</td>\n",
       "      <td>0.731</td>\n",
       "      <td>Other years</td>\n",
       "      <td>South Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2012</td>\n",
       "      <td>3.783</td>\n",
       "      <td>7.705</td>\n",
       "      <td>0.521</td>\n",
       "      <td>52.24</td>\n",
       "      <td>0.531</td>\n",
       "      <td>0.236</td>\n",
       "      <td>0.776</td>\n",
       "      <td>Other years</td>\n",
       "      <td>South Asia</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       country  year  happiness_score  gdp_per_capita  social_support  \\\n",
       "0  Afghanistan  2008            3.724           7.370           0.451   \n",
       "1  Afghanistan  2009            4.402           7.540           0.552   \n",
       "2  Afghanistan  2010            4.758           7.647           0.539   \n",
       "3  Afghanistan  2011            3.832           7.620           0.521   \n",
       "4  Afghanistan  2012            3.783           7.705           0.521   \n",
       "\n",
       "   life_expectancy  freedom_of_choiche  generosity  corruption_perception  \\\n",
       "0            50.80               0.718       0.168                  0.882   \n",
       "1            51.20               0.679       0.190                  0.850   \n",
       "2            51.60               0.600       0.121                  0.707   \n",
       "3            51.92               0.496       0.162                  0.731   \n",
       "4            52.24               0.531       0.236                  0.776   \n",
       "\n",
       "     highlight regional_indicator  \n",
       "0  Other years         South Asia  \n",
       "1  Other years         South Asia  \n",
       "2  Other years         South Asia  \n",
       "3  Other years         South Asia  \n",
       "4  Other years         South Asia  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.info()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_pickle('data/df_happiness.pkl')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### EDA Charts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x540 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.hist(figsize=(20, 7.5), layout=(2, 4));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x540 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.loc[:, df.columns != 'year'].plot(kind = 'box', figsize = (20, 7.5), subplots = True, layout = (2, 4));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
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         "hovertemplate": "x=%{x}<br>y=%{y}<extra></extra>",
         "legendgroup": "",
         "marker": {
          "color": "#636efa",
          "symbol": "circle"
         },
         "mode": "markers",
         "name": "",
         "showlegend": false,
         "type": "scattergl",
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   "source": [
    "px.scatter(x = df['year'], y = df['happiness_score'])"
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Main Vis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### First Draft"
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  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
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          "South Africa",
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          },
          "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
          }
         }
        },
        "xaxis": {
         "anchor": "y",
         "domain": [
          0,
          1
         ],
         "range": [
          1,
          8.2
         ],
         "tickwidth": 1,
         "title": {
          "text": "happiness_score"
         }
        },
        "yaxis": {
         "anchor": "x",
         "domain": [
          0,
          1
         ],
         "tickvals": [
          "Afghanistan",
          "Albania",
          "Algeria",
          "Angola",
          "Argentina",
          "Armenia",
          "Australia",
          "Austria",
          "Azerbaijan",
          "Bahrain",
          "Bangladesh",
          "Belarus",
          "Belgium",
          "Belize",
          "Benin",
          "Bhutan",
          "Bolivia",
          "Bosnia and Herzegovina",
          "Botswana",
          "Brazil",
          "Bulgaria",
          "Burkina Faso",
          "Burundi",
          "Cambodia",
          "Cameroon",
          "Canada",
          "Central African Republic",
          "Chad",
          "Chile",
          "China",
          "Colombia",
          "Comoros",
          "Congo (Brazzaville)",
          "Congo (Kinshasa)",
          "Costa Rica",
          "Croatia",
          "Cuba",
          "Cyprus",
          "Czech Republic",
          "Denmark",
          "Djibouti",
          "Dominican Republic",
          "Ecuador",
          "Egypt",
          "El Salvador",
          "Estonia",
          "Ethiopia",
          "Finland",
          "France",
          "Gabon",
          "Gambia",
          "Georgia",
          "Germany",
          "Ghana",
          "Greece",
          "Guatemala",
          "Guinea",
          "Guyana",
          "Haiti",
          "Honduras",
          "Hong Kong S.A.R. of China",
          "Hungary",
          "Iceland",
          "India",
          "Indonesia",
          "Iran",
          "Iraq",
          "Ireland",
          "Israel",
          "Italy",
          "Ivory Coast",
          "Jamaica",
          "Japan",
          "Jordan",
          "Kazakhstan",
          "Kenya",
          "Kosovo",
          "Kuwait",
          "Kyrgyzstan",
          "Laos",
          "Latvia",
          "Lebanon",
          "Lesotho",
          "Liberia",
          "Libya",
          "Lithuania",
          "Luxembourg",
          "Madagascar",
          "Malawi",
          "Malaysia",
          "Maldives",
          "Mali",
          "Malta",
          "Mauritania",
          "Mauritius",
          "Mexico",
          "Moldova",
          "Mongolia",
          "Montenegro",
          "Morocco",
          "Mozambique",
          "Myanmar",
          "Namibia",
          "Nepal",
          "Netherlands",
          "New Zealand",
          "Nicaragua",
          "Niger",
          "Nigeria",
          "North Cyprus",
          "North Macedonia",
          "Norway",
          "Oman",
          "Pakistan",
          "Palestinian Territories",
          "Panama",
          "Paraguay",
          "Peru",
          "Philippines",
          "Poland",
          "Portugal",
          "Qatar",
          "Romania",
          "Russia",
          "Rwanda",
          "Saudi Arabia",
          "Senegal",
          "Serbia",
          "Sierra Leone",
          "Singapore",
          "Slovakia",
          "Slovenia",
          "Somalia",
          "Somaliland region",
          "South Africa",
          "South Korea",
          "South Sudan",
          "Spain",
          "Sri Lanka",
          "Sudan",
          "Suriname",
          "Swaziland",
          "Sweden",
          "Switzerland",
          "Syria",
          "Taiwan Province of China",
          "Tajikistan",
          "Tanzania",
          "Thailand",
          "Togo",
          "Trinidad and Tobago",
          "Tunisia",
          "Turkey",
          "Turkmenistan",
          "Uganda",
          "Ukraine",
          "United Arab Emirates",
          "United Kingdom",
          "United States",
          "Uruguay",
          "Uzbekistan",
          "Venezuela",
          "Vietnam",
          "Yemen",
          "Zambia",
          "Zimbabwe"
         ],
         "title": {
          "text": "country"
         }
        }
       }
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = px.scatter(\n",
    "    df, \n",
    "    x = 'happiness_score', \n",
    "    y = 'country',\n",
    "    height=4000,\n",
    "    hover_data = ['country', 'year', 'happiness_score']\n",
    "    )\n",
    "\n",
    "fig.update_yaxes(\n",
    "    tickvals = df.country.unique()          # make a line for each country\n",
    ")            \n",
    "\n",
    "fig.update_xaxes(\n",
    "    tickwidth = 1,\n",
    "    range = (1, 8.2)\n",
    ")\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th>happiness_score</th>\n",
       "      <th>gdp_per_capita</th>\n",
       "      <th>social_support</th>\n",
       "      <th>life_expectancy</th>\n",
       "      <th>freedom_of_choiche</th>\n",
       "      <th>generosity</th>\n",
       "      <th>corruption_perception</th>\n",
       "      <th>highlight</th>\n",
       "      <th>regional_indicator</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2008</td>\n",
       "      <td>3.724000</td>\n",
       "      <td>7.370000</td>\n",
       "      <td>0.451000</td>\n",
       "      <td>50.800000</td>\n",
       "      <td>0.718000</td>\n",
       "      <td>0.168000</td>\n",
       "      <td>0.882000</td>\n",
       "      <td>Other years</td>\n",
       "      <td>South Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2009</td>\n",
       "      <td>4.402000</td>\n",
       "      <td>7.540000</td>\n",
       "      <td>0.552000</td>\n",
       "      <td>51.200000</td>\n",
       "      <td>0.679000</td>\n",
       "      <td>0.190000</td>\n",
       "      <td>0.850000</td>\n",
       "      <td>Other years</td>\n",
       "      <td>South Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2010</td>\n",
       "      <td>4.758000</td>\n",
       "      <td>7.647000</td>\n",
       "      <td>0.539000</td>\n",
       "      <td>51.600000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.121000</td>\n",
       "      <td>0.707000</td>\n",
       "      <td>Other years</td>\n",
       "      <td>South Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2011</td>\n",
       "      <td>3.832000</td>\n",
       "      <td>7.620000</td>\n",
       "      <td>0.521000</td>\n",
       "      <td>51.920000</td>\n",
       "      <td>0.496000</td>\n",
       "      <td>0.162000</td>\n",
       "      <td>0.731000</td>\n",
       "      <td>Other years</td>\n",
       "      <td>South Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2012</td>\n",
       "      <td>3.783000</td>\n",
       "      <td>7.705000</td>\n",
       "      <td>0.521000</td>\n",
       "      <td>52.240000</td>\n",
       "      <td>0.531000</td>\n",
       "      <td>0.236000</td>\n",
       "      <td>0.776000</td>\n",
       "      <td>Other years</td>\n",
       "      <td>South Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2259</th>\n",
       "      <td>Venezuela</td>\n",
       "      <td>None</td>\n",
       "      <td>5.949375</td>\n",
       "      <td>9.448769</td>\n",
       "      <td>0.907062</td>\n",
       "      <td>66.005000</td>\n",
       "      <td>0.663188</td>\n",
       "      <td>-0.164583</td>\n",
       "      <td>0.797187</td>\n",
       "      <td>Mean score</td>\n",
       "      <td>Latin America and Caribbean</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2260</th>\n",
       "      <td>Vietnam</td>\n",
       "      <td>None</td>\n",
       "      <td>5.312867</td>\n",
       "      <td>8.676467</td>\n",
       "      <td>0.830714</td>\n",
       "      <td>67.002267</td>\n",
       "      <td>0.887250</td>\n",
       "      <td>-0.004571</td>\n",
       "      <td>0.785727</td>\n",
       "      <td>Mean score</td>\n",
       "      <td>Southeast Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2261</th>\n",
       "      <td>Yemen</td>\n",
       "      <td>None</td>\n",
       "      <td>3.892692</td>\n",
       "      <td>8.055000</td>\n",
       "      <td>0.746923</td>\n",
       "      <td>55.040154</td>\n",
       "      <td>0.620846</td>\n",
       "      <td>-0.128091</td>\n",
       "      <td>0.822200</td>\n",
       "      <td>Mean score</td>\n",
       "      <td>Middle East and North Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2262</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>None</td>\n",
       "      <td>4.519800</td>\n",
       "      <td>8.072067</td>\n",
       "      <td>0.735733</td>\n",
       "      <td>51.980600</td>\n",
       "      <td>0.758467</td>\n",
       "      <td>0.016533</td>\n",
       "      <td>0.828467</td>\n",
       "      <td>Mean score</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2263</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>None</td>\n",
       "      <td>3.836500</td>\n",
       "      <td>7.856125</td>\n",
       "      <td>0.796312</td>\n",
       "      <td>50.606313</td>\n",
       "      <td>0.593500</td>\n",
       "      <td>-0.080750</td>\n",
       "      <td>0.842750</td>\n",
       "      <td>Mean score</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2264 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          country  year  happiness_score  gdp_per_capita  social_support  \\\n",
       "0     Afghanistan  2008         3.724000        7.370000        0.451000   \n",
       "1     Afghanistan  2009         4.402000        7.540000        0.552000   \n",
       "2     Afghanistan  2010         4.758000        7.647000        0.539000   \n",
       "3     Afghanistan  2011         3.832000        7.620000        0.521000   \n",
       "4     Afghanistan  2012         3.783000        7.705000        0.521000   \n",
       "...           ...   ...              ...             ...             ...   \n",
       "2259    Venezuela  None         5.949375        9.448769        0.907062   \n",
       "2260      Vietnam  None         5.312867        8.676467        0.830714   \n",
       "2261        Yemen  None         3.892692        8.055000        0.746923   \n",
       "2262       Zambia  None         4.519800        8.072067        0.735733   \n",
       "2263     Zimbabwe  None         3.836500        7.856125        0.796312   \n",
       "\n",
       "      life_expectancy  freedom_of_choiche  generosity  corruption_perception  \\\n",
       "0           50.800000            0.718000    0.168000               0.882000   \n",
       "1           51.200000            0.679000    0.190000               0.850000   \n",
       "2           51.600000            0.600000    0.121000               0.707000   \n",
       "3           51.920000            0.496000    0.162000               0.731000   \n",
       "4           52.240000            0.531000    0.236000               0.776000   \n",
       "...               ...                 ...         ...                    ...   \n",
       "2259        66.005000            0.663188   -0.164583               0.797187   \n",
       "2260        67.002267            0.887250   -0.004571               0.785727   \n",
       "2261        55.040154            0.620846   -0.128091               0.822200   \n",
       "2262        51.980600            0.758467    0.016533               0.828467   \n",
       "2263        50.606313            0.593500   -0.080750               0.842750   \n",
       "\n",
       "        highlight            regional_indicator  \n",
       "0     Other years                    South Asia  \n",
       "1     Other years                    South Asia  \n",
       "2     Other years                    South Asia  \n",
       "3     Other years                    South Asia  \n",
       "4     Other years                    South Asia  \n",
       "...           ...                           ...  \n",
       "2259   Mean score   Latin America and Caribbean  \n",
       "2260   Mean score                Southeast Asia  \n",
       "2261   Mean score  Middle East and North Africa  \n",
       "2262   Mean score            Sub-Saharan Africa  \n",
       "2263   Mean score            Sub-Saharan Africa  \n",
       "\n",
       "[2264 rows x 11 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Graph with highlights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
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          "El Salvador",
          "Guatemala",
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          "Italy",
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          "Moldova",
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          "Bolivia",
          "Malaysia",
          "South Korea",
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          "Moldova",
          "Libya",
          "Serbia",
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          "Uzbekistan",
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          "Azerbaijan",
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          "Kyrgyzstan",
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          "Malawi",
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          "Mauritania",
          "Mauritius",
          "Mexico",
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          "Montenegro",
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          "Mozambique",
          "Myanmar",
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          "Nepal",
          "Netherlands",
          "New Zealand",
          "Nicaragua",
          "Niger",
          "Nigeria",
          "North Cyprus",
          "North Macedonia",
          "Norway",
          "Oman",
          "Pakistan",
          "Palestinian Territories",
          "Panama",
          "Paraguay",
          "Peru",
          "Philippines",
          "Poland",
          "Portugal",
          "Qatar",
          "Romania",
          "Russia",
          "Rwanda",
          "Saudi Arabia",
          "Senegal",
          "Serbia",
          "Sierra Leone",
          "Singapore",
          "Slovakia",
          "Slovenia",
          "Somalia",
          "Somaliland region",
          "South Africa",
          "South Korea",
          "South Sudan",
          "Spain",
          "Sri Lanka",
          "Sudan",
          "Suriname",
          "Swaziland",
          "Sweden",
          "Switzerland",
          "Syria",
          "Taiwan Province of China",
          "Tajikistan",
          "Tanzania",
          "Thailand",
          "Togo",
          "Trinidad and Tobago",
          "Tunisia",
          "Turkey",
          "Turkmenistan",
          "Uganda",
          "Ukraine",
          "United Arab Emirates",
          "United Kingdom",
          "United States",
          "Uruguay",
          "Uzbekistan",
          "Venezuela",
          "Vietnam",
          "Yemen",
          "Zambia",
          "Zimbabwe"
         ],
         "title": {
          "text": "country"
         }
        }
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     },
     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "ascending = False\n",
    "\n",
    "fig = px.scatter(\n",
    "    df.sort_values(['happiness_score'], ascending = ascending), \n",
    "    x = 'happiness_score', \n",
    "    y = 'country',\n",
    "    color = 'highlight',\n",
    "    height = 200 + len(df.country.unique()) * 20,\n",
    "    hover_data = ['country', 'year', 'happiness_score'],\n",
    "    color_discrete_map={\n",
    "            'Other years': '#E0E0E0',\n",
    "            2020: '#3177CD',\n",
    "            2021: '#051B33'\n",
    "        },\n",
    "    labels = {\n",
    "        'x': 'Happiness Index Score',\n",
    "        'y': None\n",
    "    }\n",
    ")\n",
    "\n",
    "fig.update_yaxes(\n",
    "    tickvals = df.country.unique(),              # make a line for each country\n",
    "    range=[-.5,len(df.country.unique())+.5]\n",
    ")            \n",
    "\n",
    "fig.update_xaxes(\n",
    "    tickwidth = 1,\n",
    "    range = (2, 8.2)\n",
    ")\n",
    "\n",
    "fig.update_traces(\n",
    "    marker = {\n",
    "        'size': 12,\n",
    "        'opacity':0.9,\n",
    "        'line':{'width':1, 'color': 'DarkSlateGrey'},\n",
    "    }\n",
    "    \n",
    ")\n",
    "\n",
    "fig.update_layout({\n",
    "    'plot_bgcolor': 'rgba(0, 0, 0, 0)'       # make the background transparent\n",
    "    # 'paper_bgcolor': 'rgba(0, 0, 0, 0)'\n",
    "}\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       False\n",
       "1       False\n",
       "2       False\n",
       "3       False\n",
       "4       False\n",
       "        ...  \n",
       "2259    False\n",
       "2260    False\n",
       "2261    False\n",
       "2262    False\n",
       "2263    False\n",
       "Name: country, Length: 2264, dtype: bool"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.country.isin(['Brazil', 'Argentina'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
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   "source": [
    "# get the mean df\n",
    "df_mean = df_mean_by_country(df)\n",
    "\n",
    "# transform the country names into ISO\n",
    "countries = {}\n",
    "\n",
    "for country in pycountry.countries:\n",
    "    countries [country.name] = country.alpha_3\n",
    "\n",
    "df_mean['country_iso'] = [countries.get(country, 'Unknown code') for country in df_mean['country'].unique()]\n",
    "\n",
    "fig = px.choropleth(\n",
    "    df_mean,\n",
    "    locations = 'country_iso', \n",
    "    color = 'happiness_score'\n",
    ")\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2264 entries, 0 to 2263\n",
      "Data columns (total 11 columns):\n",
      " #   Column                 Non-Null Count  Dtype  \n",
      "---  ------                 --------------  -----  \n",
      " 0   country                2264 non-null   object \n",
      " 1   year                   2098 non-null   object \n",
      " 2   happiness_score        2264 non-null   float64\n",
      " 3   gdp_per_capita         2224 non-null   float64\n",
      " 4   social_support         2250 non-null   float64\n",
      " 5   life_expectancy        2208 non-null   float64\n",
      " 6   freedom_of_choiche     2232 non-null   float64\n",
      " 7   generosity             2171 non-null   float64\n",
      " 8   corruption_perception  2152 non-null   float64\n",
      " 9   highlight              2264 non-null   object \n",
      " 10  regional_indicator     2264 non-null   object \n",
      "dtypes: float64(7), object(4)\n",
      "memory usage: 194.7+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 11 entries, South Asia to East Asia\n",
      "Data columns (total 2 columns):\n",
      " #   Column              Non-Null Count  Dtype  \n",
      "---  ------              --------------  -----  \n",
      " 0   regional_indicator  11 non-null     object \n",
      " 1   happiness_score     11 non-null     float64\n",
      "dtypes: float64(1), object(1)\n",
      "memory usage: 264.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "df_region = df_mean_by_region(df)\n",
    "df_region.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "ename": "IndexError",
     "evalue": "index 0 is out of bounds for axis 0 with size 0",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-18-9802735fc3c3>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     10\u001b[0m     \u001b[1;32mfor\u001b[0m \u001b[0mcol\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mgroup_col\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0munique\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     11\u001b[0m         \u001b[0mnew_col\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mgroup_col\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mcol\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m&\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'year'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0myear\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mgroup_col\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'year'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mvalue_col\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 12\u001b[1;33m         \u001b[0mmean_list\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnew_col\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     13\u001b[0m         \u001b[0myear_list\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0myear\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     14\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\series.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m    937\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    938\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_should_fallback_to_positional\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 939\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_values\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    940\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    941\u001b[0m         \u001b[1;32melif\u001b[0m \u001b[0mkey_is_scalar\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mIndexError\u001b[0m: index 0 is out of bounds for axis 0 with size 0"
     ]
    }
   ],
   "source": [
    "group_col = 'regional_indicator'\n",
    "value_col = 'happiness_score'\n",
    "\n",
    "mean_list = []\n",
    "year_list = []\n",
    "df_mean = pd.DataFrame(columns = [group_col, value_col])\n",
    "for year in df['year'].unique():\n",
    "    if year is None:\n",
    "        break\n",
    "    for col in df[group_col].unique():\n",
    "        new_col = df.loc[(df[group_col] == col) & df['year'] == year].groupby([group_col, 'year'])[value_col].mean()\n",
    "        mean_list.append(new_col[0])\n",
    "        year_list.append(year)\n",
    "\n",
    "df_mean[group_col] = df[group_col].unique()\n",
    "df_mean['year'] = year_list\n",
    "df_mean[value_col] = mean_list\n",
    "\n",
    "df_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Afghanistan</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>3.512231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>Albania</td>\n",
       "      <td>5.026357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algeria</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>5.334000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Angola</th>\n",
       "      <td>Angola</td>\n",
       "      <td>4.420250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Argentina</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>6.286313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Venezuela</th>\n",
       "      <td>Venezuela</td>\n",
       "      <td>5.949375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vietnam</th>\n",
       "      <td>Vietnam</td>\n",
       "      <td>5.312867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yemen</th>\n",
       "      <td>Yemen</td>\n",
       "      <td>3.892692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zambia</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>4.519800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zimbabwe</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>3.836500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>166 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 country  happiness_score\n",
       "Afghanistan  Afghanistan         3.512231\n",
       "Albania          Albania         5.026357\n",
       "Algeria          Algeria         5.334000\n",
       "Angola            Angola         4.420250\n",
       "Argentina      Argentina         6.286313\n",
       "...                  ...              ...\n",
       "Venezuela      Venezuela         5.949375\n",
       "Vietnam          Vietnam         5.312867\n",
       "Yemen              Yemen         3.892692\n",
       "Zambia            Zambia         4.519800\n",
       "Zimbabwe        Zimbabwe         3.836500\n",
       "\n",
       "[166 rows x 2 columns]"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_mean_by_country(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Finland', 'Denmark', 'Switzerland', 'Iceland', 'Netherlands']"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(df.loc[df['year'] == 2021].nlargest(5, 'happiness_score')['country'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['country', 'year', 'happiness_score', 'gdp_per_capita',\n",
       "       'social_support', 'life_expectancy', 'freedom_of_choiche', 'generosity',\n",
       "       'corruption_perception', 'highlight', 'regional_indicator'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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