Introduction to Altair: A Declarative Python Library for Data Visualization

Altair is a simple, friendly Python library for statistical data visualization that builds on the powerful Vega‑Lite JSON specification, allowing users to generate attractive visualizations with minimal code.

Altair is a statistical visualization library with over 3000 stars on GitHub; it lets users focus on understanding data rather than low‑level chart construction, using a declarative language that describes visual appearance and interaction in JSON.

Key advantages include support for aggregation, data transformation, interaction, and composition; charts can be displayed in Jupyter Notebook, JupyterLab, or nteract, and exported as PNG, SVG, or standalone HTML, or viewed in the online Vega‑Lite editor.

Altair works best with tidy data, typically loaded as a Pandas DataFrame. Example code demonstrates importing Altair and Pandas, reading an Excel file, and creating a chart:

<code>import altair as alt
import pandas as pd

data = pd.read_excel("Index_Chart_Altair.xlsx", sheet_name="Sales", parse_dates=["Year"])
alt.Chart(data)
</code>

A simple bar chart can be created by mapping a quantitative variable to the x‑axis and a nominal variable to the y‑axis using mark_bar() :

<code>chart = alt.Chart(df).mark_bar().encode(x="profit:Q", y="product:N")
</code>

Altair supports various variable type combinations such as nominal + quantitative, temporal + quantitative, temporal + nominal, and quantitative + quantitative, enabling flexible chart designs.

For more complex visualizations, an area chart with faceting demonstrates Seattle’s monthly precipitation from 2012 to 2015. The code uses mark_area() , aggregates with mean() , and facets by year:

<code>chart = alt.Chart(df).mark_area(color="lightblue", interpolate="step", line=True, opacity=0.8)
    .encode(
        alt.X("month(date):T", axis=alt.Axis(format="%b", formatType="time", labelAngle=-15, labelBaseline="top", labelPadding=5, title="month")),
        y="mean(precipitation):Q",
        facet=alt.Facet("year(date):Q", columns=4,
            header=alt.Header(labelColor="red", labelFontSize=15,
                title="Seattle Monthly Precipitation from 2012 to 2015",
                titleFont="Calibri", titleFontSize=25, titlePadding=15))
    )
</code>

The Header wrapper customizes facet labels and titles with parameters such as labelColor , labelFontSize , title , titleFont , titleFontSize , and titlePadding .

Images illustrating the bar chart, area chart, and faceted precipitation charts are included throughout the article.

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