How to make Density Plot in Python with Altair?

Altair is a statistical visualization library in Python based on Vega-Lite grammar. Density plots are useful for visualizing data distribution, comparing groups, and detecting outliers. This article demonstrates how to create density plots using Altair with a practical example.

What is a Density Plot?

A density plot shows the distribution of a continuous variable by estimating the probability density function. It's similar to a histogram but uses a smooth curve instead of bars.

Required Libraries

First, let's import the necessary libraries ?

import altair as alt
import pandas as pd

Loading Sample Data

We'll use the Titanic dataset to create a density plot showing age distribution by survival status ?

import altair as alt
import pandas as pd

# Load Titanic dataset
titanic_data = pd.read_csv("https://web.stanford.edu/class/archive/cs/cs109/cs109.1166/stuff/titanic.csv")

# Display first few rows
print(titanic_data.head())

   Survived  Pclass                                               Name     Sex   Age  Siblings/Spouses Aboard  Parents/Children Aboard     Fare
0         0       3                            Braund, Mr. Owen Harris    male  22.0                        1                        0   7.2500
1         1       1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0                        1                        0  71.2833
2         1       3                             Heikkinen, Miss. Laina  female  26.0                        0                        0   7.9250
3         1       1       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0                        1                        0  53.1000
4         0       3                           Allen, Mr. William Henry    male  35.0                        0                        0   8.0500

Creating a Density Plot

Now let's create a density plot showing age distribution grouped by survival status ?

import altair as alt
import pandas as pd

# Load data
titanic_data = pd.read_csv("https://web.stanford.edu/class/archive/cs/cs109/cs109.1166/stuff/titanic.csv")

# Create density plot
chart = alt.Chart(titanic_data).transform_density(
    density='Age',
    as_=['Age', 'density'],
    groupby=['Survived'],
    extent=[0, 100]
).mark_area(
    opacity=0.6
).encode(
    x=alt.X('Age:Q', title='Age'),
    y=alt.Y('density:Q', title='Density'),
    color=alt.Color('Survived:N', 
                   scale=alt.Scale(domain=[0, 1], range=['red', 'blue']),
                   title='Survived'),
    tooltip=['Survived:N', 'Age:Q', 'density:Q']
).properties(
    title='Age Distribution by Survival Status - Titanic Dataset',
    width=600,
    height=300
)

chart

Understanding the Code Components

transform_density() Parameters

The transform_density() method computes kernel density estimation with these key parameters ?

• density: Variable for density computation (here 'Age')

• as_: Output variable names ['Age', 'density']

• groupby: Variables to group by ['Survived']

• extent: Range for density computation [0, 100]

Encoding Properties

The encoding maps data to visual properties ?

• x-axis: Age values (quantitative)

• y-axis: Density values

• color: Survival status (red for died, blue for survived)

• tooltip: Interactive information on hover

Customizing the Plot

You can customize colors, transparency, and styling ?

import altair as alt
import pandas as pd

titanic_data = pd.read_csv("https://web.stanford.edu/class/archive/cs/cs109/cs109.1166/stuff/titanic.csv")

# Custom styled density plot
chart = alt.Chart(titanic_data).transform_density(
    density='Age',
    as_=['Age', 'density'],
    groupby=['Survived']
).mark_area(
    opacity=0.4,
    stroke='white',
    strokeWidth=2
).encode(
    x=alt.X('Age:Q', title='Age (years)', scale=alt.Scale(domain=[0, 80])),
    y=alt.Y('density:Q', title='Probability Density'),
    color=alt.Color('Survived:N', 
                   scale=alt.Scale(domain=[0, 1], range=['#ff6b6b', '#4ecdc4']),
                   legend=alt.Legend(title="Survival Status", 
                                   labelExpr="datum.value == 1 ? 'Survived' : 'Died'"))
).properties(
    title=alt.TitleParams(text='Titanic Passenger Age Distribution', fontSize=16),
    width=500,
    height=250
)

chart

Key Features

Conclusion

Altair makes creating density plots straightforward with transform_density(). These plots effectively visualize continuous data distributions and enable easy comparison between different groups in your dataset.