📍 The Data
This example considers a hierarchical dataset. The world is split by continents. Continents are split by country. Each country has a value (population size). Our goal is to represent each country as a circle, its size being proportional to its population.
Let's create such a dataset:
data = [{'id': 'World', 'datum': 6964195249, 'children' : [
{'id' : "North America", 'datum': 450448697,
'children' : [
{'id' : "United States", 'datum' : 308865000},
{'id' : "Mexico", 'datum' : 107550697},
{'id' : "Canada", 'datum' : 34033000}
]},
{'id' : "South America", 'datum' : 278095425,
'children' : [
{'id' : "Brazil", 'datum' : 192612000},
{'id' : "Colombia", 'datum' : 45349000},
{'id' : "Argentina", 'datum' : 40134425}
]},
{'id' : "Europe", 'datum' : 209246682,
'children' : [
{'id' : "Germany", 'datum' : 81757600},
{'id' : "France", 'datum' : 65447374},
{'id' : "United Kingdom", 'datum' : 62041708}
]},
{'id' : "Africa", 'datum' : 311929000,
'children' : [
{'id' : "Nigeria", 'datum' : 154729000},
{'id' : "Ethiopia", 'datum' : 79221000},
{'id' : "Egypt", 'datum' : 77979000}
]},
{'id' : "Asia", 'datum' : 2745929500,
'children' : [
{'id' : "China", 'datum' : 1336335000},
{'id' : "India", 'datum' : 1178225000},
{'id' : "Indonesia", 'datum' : 231369500}
]}
]}]🙇♂️ Compute circle position
We need an algorythm that computes the position of each country and continent circles, together with their radius. Fortunately, the circlize library is here. It's circlify() function does exactly that 😍
# import the circlify library
import circlify
# Compute circle positions thanks to the circlify() function
circles = circlify.circlify(
data,
show_enclosure=False,
target_enclosure=circlify.Circle(x=0, y=0, r=1)
)
Have a look to the circles object, it provides exactly that 🎉.
🔨 Build the viz
Let's be honnest, that's quite a bit of code to get a decent graph 😞. The circlize library has a bubble() function that allows to do a simple circle pack with one line of code, but it does not allow to customize the chart.
So once more matplotlib is our best friend for the rendering part. Here I'm printing the layers from the bottom to the top of the figure: first the cirles for the highest level of hierarchy (continent), then circle and labels for countries, then continent labels.
# import libraries
import circlify
import matplotlib.pyplot as plt
# Create just a figure and only one subplot
fig, ax = plt.subplots(figsize=(14,14))
# Title
ax.set_title('Repartition of the world population')
# Remove axes
ax.axis('off')
# Find axis boundaries
lim = max(
max(
abs(circle.x) + circle.r,
abs(circle.y) + circle.r,
)
for circle in circles
)
plt.xlim(-lim, lim)
plt.ylim(-lim, lim)
# Print circle the highest level (continents):
for circle in circles:
if circle.level != 2:
continue
x, y, r = circle
ax.add_patch( plt.Circle((x, y), r, alpha=0.5, linewidth=2, color="lightblue"))
# Print circle and labels for the highest level:
for circle in circles:
if circle.level != 3:
continue
x, y, r = circle
label = circle.ex["id"]
ax.add_patch( plt.Circle((x, y), r, alpha=0.5, linewidth=2, color="#69b3a2"))
plt.annotate(label, (x,y ), ha='center', color="white")
# Print labels for the continents
for circle in circles:
if circle.level != 2:
continue
x, y, r = circle
label = circle.ex["id"]
plt.annotate(label, (x,y ) ,va='center', ha='center', bbox=dict(facecolor='white', edgecolor='black', boxstyle='round', pad=.5))
