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-+++
-date = 2020-09-25
-title = "Data Visualization: World Choropleth Map of Happiness"
-description = ""
-draft = false
-+++
-
-# Background Information
-
-The dataset (obtained from
-[Kaggle](https://www.kaggle.com/unsdsn/world-happiness)) 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.
-
-```python
-!pip install folium geopandas matplotlib numpy pandas
-```
-
-# Import the Data
-
-We only need a couple packages to create a choropleth map. We will use
-[Folium](https://python-visualization.github.io/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.
-
-```python
-# 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, GeoJSON files exist (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:
-
-```python
-# Load the GeoJSON data with geopandas
-geo_data = gpd.read_file('https://raw.githubusercontent.com/datasets/geo-countries/master/data/countries.geojson')
-geo_data.head()
-```
-
-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:
-
-```python
-# Load the world happiness data with pandas
-happy_data = pd.read_csv(r'~/Downloads/world_happiness_data_2019.csv')
-happy_data.head()
-```
-
-# 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.
-
-```python
-# Rename some countries to match our GeoJSON data
-
-# Rename USA
-usa_index = happy_data.index[happy_data['Country or region'] == 'United States']
-happy_data.at[usa_index, 'Country or region'] = 'United States of America'
-
-# Rename Tanzania
-tanzania_index = happy_data.index[happy_data['Country or region'] == 'Tanzania']
-happy_data.at[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)']
-happy_data.at[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)']
-happy_data.at[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.
-
-```python
-# 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'})
-```
-
-# 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.
-
-```python
-# Assign centroids to map
-x_map = merged_df.centroid.x.mean()
-y_map = merged_df.centroid.y.mean()
-print(x_map,y_map)
-
-# 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
-folium.Choropleth(
-    geo_data=merged_df,
-    name='Choropleth',
-    data=merged_df,
-    columns=['Country','Overall rank'],
-    key_on='feature.properties.Country',
-    fill_color='YlOrRd',
-    fill_opacity=0.6,
-    line_opacity=0.8,
-    legend_name='Overall happiness rank',
-    smooth_factor=0,
-    highlight=True
-).add_to(world_map)
-```
-
-Let's look at the resulting 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.
-
-```python
-    # Adding labels to map
-    style_function = lambda x: {'fillColor': '#ffffff',
-                                'color':'#000000',
-                                'fillOpacity': 0.1,
-                            'weight': 0.1}
-
-tooltip_data = folium.features.GeoJson(
-    merged_df,
-    style_function=style_function,
-    control=False,
-    tooltip=folium.features.GeoJsonTooltip(
-        fields=['Country'
-                ,'Overall rank'
-                ,'Score'
-                ,'GDP per capita'
-                ,'Social support'
-                ,'Healthy life expectancy'
-                ,'Freedom to make life choices'
-                ,'Generosity'
-                ,'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;')
-    )
-)
-world_map.add_child(tooltip_data)
-world_map.keep_in_front(tooltip_data)
-folium.LayerControl().add_to(world_map)
-
-# Display the map
-world_map
-```
-
-The tooltip will now appear whenever you hover over a country.