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Altair

Data & AnalyticsData SciencePython
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What it is

Altair is a declarative statistical visualization library for Python. It allows users to create interactive and concise charts based on the Vega and Vega-Lite visualization grammars.

Altair leverages a declarative API where you specify 'what' to plot rather than 'how' to plot it. It supports bar charts, line charts, scatter plots, heatmaps, and more, with built-in interactivity like selections, tooltips, and filtering.

Installation

pip install altair

Getting started

The smallest useful thing you can do with it, and what each part means.

Simple Bar Chart
import altair as alt
import pandas as pd
df = pd.DataFrame({'category': ['A', 'B', 'C'], 'value': [4, 7, 1]})
chart = alt.Chart(df).mark_bar().encode(x='category', y='value')
chart.show()
Creates a simple bar chart using a Pandas DataFrame and displays it.
Line Chart with Tooltips
import altair as alt
import pandas as pd
df = pd.DataFrame({'x': [1, 2, 3, 4], 'y': [10, 15, 13, 17]})
chart = alt.Chart(df).mark_line(point=True).encode(x='x', y='y', tooltip=['x','y'])
chart.show()
Generates a line chart with points and interactive tooltips showing x and y values.

Advanced usage

Where the library earns its place over a simpler alternative.

Scatter Plot with Selection
import altair as alt
import pandas as pd
df = pd.DataFrame({'x':[1,2,3,4],'y':[10,20,25,30],'category':['A','A','B','B']})
selector = alt.selection_multi(fields=['category'])
chart = alt.Chart(df).mark_circle(size=100).encode(x='x', y='y', color='category').add_selection(selector)
chart.show()
Creates an interactive scatter plot where users can select points by category using a selection object.
Faceted Charts
import altair as alt
import pandas as pd
df = pd.DataFrame({'x':[1,2,3,4],'y':[10,20,25,30],'group':['A','A','B','B']})
chart = alt.Chart(df).mark_line().encode(x='x', y='y').facet('group')
chart.show()
Facets data into multiple small charts based on the 'group' column.
Interactive Filtering
import altair as alt
import pandas as pd
df = pd.DataFrame({'x':[1,2,3,4],'y':[10,20,25,30],'group':['A','A','B','B']})
input_dropdown = alt.binding_select(options=['A','B'], name='Select Group:')
selection = alt.selection_single(fields=['group'], bind=input_dropdown)
chart = alt.Chart(df).mark_bar().encode(x='x', y='y', color='group').add_selection(selection).transform_filter(selection)
chart.show()
Demonstrates interactive filtering using a dropdown selection to display only selected group data.

Errors and fixes

The failures you are most likely to hit, and what actually resolves them.

ValueError: Data format not recognized
Ensure the input data is a Pandas DataFrame or a compatible data format.
AltairError: chart has no encodings
Make sure to specify at least one encoding (x, y, color, etc.) for the chart.
Renderer not found
Use `chart.show()` in Jupyter or `alt.renderers.enable('default')` to specify a renderer.

Best practices

  • Use Pandas DataFrames as input data for better integration.
  • Leverage declarative syntax to keep code clean and readable.
  • Combine charts with layering and faceting for richer visualizations.
  • Use selections and interactions to enhance user exploration.
  • Export charts to HTML or JSON for embedding in web applications.

Background

Why it exists, and what it was reacting to.

Altair was created by Jake VanderPlas and the Altair development team to provide a simple, declarative way to create rich visualizations in Python. Its focus is on producing high-quality, interactive charts with minimal code while maintaining clear semantics and good integration with Pandas.

Related libraries

Others in Data & Analytics for Python.

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