Data Visualization: World Choropleth Map of Happiness

Background Information

The dataset (obtained from Kaggle) used in this article contains a list of countries around the world, their happiness rankings and scores, as well as other national scoring measures.

Fields include:

  • Overall rank
  • Country or region
  • GDP per capita
  • Social support
  • Healthy life expectancy
  • Freedom to make life choices
  • Generosity
  • Perceptions of corruption

There are 156 records. Since there are ~195 countries in the world, we can see that around 40 countries will be missing from this dataset.

Install Packages

As always, run the install command for all packages needed to perform analysis.

!pip install folium geopandas matplotlib numpy pandas

Import the Data

We only need a couple packges to create a choropleth map. We will use Folium, which provides map visualizations in Python. We will also use geopandas and pandas to wrangle our data before we put it on a map.

# Import the necessary Python packages
import folium
import geopandas as gpd
import pandas as pd

To get anything to show up on a map, we need a file that will specify the boundaries of each country. Luckily, these GeoJSON files exists for free on the internet. To get the boundaries of every country in the world, we will use the GeoJSON link shown below.

GeoPandas will take this data and load it into a dataframe so that we can easily match it to the data we're trying to analyze. Let's look at the GeoJSON dataframe:

# Load the GeoJSON data with geopandas
geo_data = gpd.read_file('')
the geo json dataframe
Fig. 1 - GeoJSON Dataframe

Next, let's load the data from the Kaggle dataset. I've downloaded this file, so update the file path if you have it somewhere else. After loading, let's take a look at this dataframe:

# Load the world happiness data with pandas
happy_data = pd.read_csv(r'~/Downloads/world_happiness_data_2019.csv')
the happiness dataframe
Fig. 2 - Happiness Dataframe

Clean the Data

Some countries need to be renamed or they will be lost when you merge the happiness and GeoJSON dataframes. This is something I discovered when the map below showed empty countries. I searched both data frames for the missing countries to see the naming differences. Any countries that do not have records in the happy_data df will not show up on the map.

# Rename some countries to match our GeoJSON data

# Rename USA
usa_index = happy_data.index[happy_data['Country or region'] == 'United States'][usa_index, 'Country or region'] = 'United States of America'

# Rename Tanzania
tanzania_index = happy_data.index[happy_data['Country or region'] == 'Tanzania'][tanzania_index, 'Country or region'] = 'United Republic of Tanzania'

# Rename the Congo
republic_congo_index = happy_data.index[happy_data['Country or region'] == 'Congo (Brazzaville)'][republic_congo_index, 'Country or region'] = 'Republic of Congo'

# Rename the DRC
democratic_congo_index = happy_data.index[happy_data['Country or region'] == 'Congo (Kinshasa)'][democratic_congo_index, 'Country or region'] = 'Democratic Republic of the Congo'

Merge the Data

Now that we have clean data, we need to merge the GeoJSON data with the happiness data. Since we've stored them both in dataframes, we just need to call the .merge() function.

We will also rename a couple columns, just so that they're a little easier to use when we create the map.

# Merge the two previous dataframes into a single geopandas dataframe
merged_df = geo_data.merge(happy_data,left_on='ADMIN', right_on='Country or region')

# Rename columns for ease of use
merged_df = merged_df.rename(columns = {'ADMIN':'GeoJSON_Country'})
merged_df = merged_df.rename(columns = {'Country or region':'Country'})
the merged dataframe
Fig. 3 - Merged Dataframe

Create the Map

The data is finally ready to be added to a map. The code below shows the simplest way to find the center of the map and create a Folium map object. The important part is to remember to reference the merged dataframe for our GeoJSON data and value data. The columns specify which geo data and value data to use.

# Assign centroids to map
x_map = merged_df.centroid.x.mean()
y_map = merged_df.centroid.y.mean()
# Creating a map object
world_map = folium.Map(location=[y_map, x_map], zoom_start=2,tiles=None)
folium.TileLayer('CartoDB positron',name='Dark Map',control=False).add_to(world_map)
# Creating choropleth map
    columns=['Country','Overall rank'], 
    legend_name='Overall happiness rank', 

Let's look at the resulting map.

the final world choropleth map
Fig. 4 - Choropleth Map

Create a Tooltip on Hover

Now that we have a map set up, we could stop. However, I want to add a tooltip so that I can see more information about each country. The tooltip_data code below will show a popup on hover with all the data fields shown.

# Adding labels to map
style_function = lambda x: {'fillColor': '#ffffff', 
                            'fillOpacity': 0.1, 
                            'weight': 0.1}

tooltip_data = folium.features.GeoJson(
                ,'Overall rank'
                ,'GDP per capita'
                ,'Social support'
                ,'Healthy life expectancy'
                ,'Freedom to make life choices'
                ,'Perceptions of corruption'
        aliases=['Country: '
                ,'Happiness rank: '
                ,'Happiness score: '
                ,'GDP per capita: '
                ,'Social support: '
                ,'Healthy life expectancy: '
                ,'Freedom to make life choices: '
                ,'Generosity: '
                ,'Perceptions of corruption: '
        style=('background-color: white; color: #333333; font-family: arial; font-size: 12px; padding: 10px;')

# Display the map

The final image below will show you what the tooltip looks like whenever you hover on a country.

the tooltip on the map
Fig. 5 - Choropleth Map Tooltip