Vega-Altair Pie Chart

Introduction

Vega-Altair package is a powerful tool for data visualization, particularly within Jupyter Notebooks. It has a wide range of charts and plots to explore and one of them is Pie Chart. We've prepared several examples to demonstrate how Vega-Altair works:

• simple pie chart
• pie chart with pandas data
• an interactive pie chart

If you need any information about Vega-Altair check their docs: Vega-Altair Docs (opens in a new tab).

All of code examples are availabe as Jupyter Notebooks in our GitHub repositiory:

• Vega-Altair Pie Chart (opens in a new tab)
• Vega-Altair Interactive Pie Chart (opens in a new tab)

Pie Chart

Visualization with only vega-altair package:

# import packages 
import altair as alt 
 
# create data 
data = alt.Data(values=[{'name': 'A', 'value': 7},
                        {'name': 'B', 'value': 9},
                        {'name': 'C', 'value': 3},
                        {'name': 'D', 'value': 5},
                       ])
# plot 
alt.Chart(data).mark_arc().encode(
    theta="value:Q",
    color="name:N"
)

Pie Chart with Pandas Data

Using vega-altair, create a pie chart with pandas data:

# import packages 
import altair as alt 
import pandas as pd 
 
# create data 
df = pd.DataFrame({
    'name': ['Apples','Bananas','Oranges','Grapes'],
    'count': [10,6,8,2]
})
 
# plot 
alt.Chart(df).mark_arc().encode(
    theta='count',
    color='name'
)

Interactive Pie Chart

Regular charts are not very exciting. What about creating an interactive pie chart with vega-altair and mercury packages? You can create donut chart with changing size hole using mercury widgets. In this example we used Slider (opens in a new tab).

# import packages 
import altair as alt 
import mercury as mr 
import pandas as pd 

# mercury widget 
slider = mr.Slider(value=0, min=0, max=100, step=10, label="How big the hole should be?")

# create data 
df = pd.DataFrame({
    'name': ['Apples','Bananas','Oranges','Grapes','Dragon Fruits'],
    'count': [5,6,6,8,10]
})
 
# plot
alt.Chart(df).mark_arc(innerRadius=slider.value).encode(
    theta='count',
    color='name'
)

Now, you can easily convert your Jupyter Notebook into a Web App! Watch the video to see what it will look like.

Deploying Web App is very easy that you can do it in 3 steps:

Congrats! You just created your own Web App and you can share your Jupyter Notebooks with nontechnical users. If you need more information about deploying the Web App check Mercury Cloud Documentation (opens in a new tab).