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Using Python Altair for Data Storytelling

Exercise: Bubble Chart and Hex Bin Chart

Explore how to represent data using bubble and hex bin charts with Python Altair. Understand techniques for encoding variables like size, opacity, and shape to visualize average latency and speed effectively. This lesson helps you apply these visualization methods to analyze network speed data across European countries.

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Consider the following dataset describing the average download/upload speed in European countries from 2019 to 2022:

Python 3.10.4
import pandas as pd
df = pd.read_csv('data/average_internet_speed.csv',parse_dates=['quarter'])
print(df)

In the following terminal, represent the data through a bubble chart:

Python 3.10.4
import pandas as pd
import altair as alt
import os
df = pd.read_csv('data/average_internet_speed.csv',parse_dates=['quarter'])
# build the chart
# use mark_point() to draw the points
# encode the average upload and download speeds in the x and y channels
# encode the country using the color channel. 
# highlight the color of a single country (e.g. IT), using alt.condition()
# chart = alt.Chart()df
# uncomment the following lines to show the chart
#chart.save('chart.html')
#os.system('cat chart.html')

Then, represent the dataset through a hex bin chart in the following code widget:

Python 3.10.4
import pandas as pd
import altair as alt
import os
df = pd.read_csv('data/average_internet_speed.csv',parse_dates=['quarter'])
# split upload and download speed in 5 bins
bins = [1, 51, 102, 152, 203,254]
labels = [50, 101, 152, 203,254]
df['binned upload speed'] = pd.cut(x = df['average upload speed'], bins = bins, labels = labels, include_lowest = True)
df['binned download speed'] = pd.cut(x = df['average download speed'], bins = bins, labels = labels, include_lowest = True)
# drop null values
df.dropna(inplace=True)
# the shape of a hexagon
hexagon = "M0,-2.3094010768L2,-1.1547005384 2,1.1547005384 0,2.3094010768 -2,1.1547005384 -2,-1.1547005384Z"
size = 25
# Count of distinct x features
xFeaturesCount = 4
# Count of distinct y features
yFeaturesCount = 5
# use mark_point(shape=hexagon) to draw hexagons
# encode binned download speed in the y channel
# calculate the position on the x axis through transform_calculate()
#chart = alt.Chart(df)
# uncomment the following lines to show the chart
#chart.save('chart.html')
#os.system('cat chart.html')
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