Altair < DIRECT | 2027 >

# Simple interactive tooltip alt.Chart(data).mark_bar().encode( x='a', y='b', tooltip=['a', 'b'] # Add tooltips on hover ).interactive() # Allow zooming/panning Use code with caution. Copied to clipboard 6. Saving Charts

Choose the chart type (e.g., mark_point() , mark_bar() , mark_line() ). altair

import altair as alt import pandas as pd # 1. Data data = pd.DataFrame({'a': ['A', 'B', 'C'], 'b': [28, 55, 43]}) # 2. & 3. Chart + Mark + Encoding chart = alt.Chart(data).mark_bar().encode( x='a', y='b' ) # Display (if in notebook) # chart.show() Use code with caution. Copied to clipboard 3. Data Transformation & Aggregation # Simple interactive tooltip alt

alt.Chart(data).mark_bar().encode( x=alt.X('a', title='Category'), y=alt.Y('b', title='Value'), color='a' # Color by category ).properties( title='My First Altair Chart', width=400, height=300 ) Use code with caution. Copied to clipboard 5. Interaction import altair as alt import pandas as pd # 1

Learn how to (e.g., lines and points)?

Altair is a declarative statistical visualization library for Python, built on the powerful Vega and Vega-Lite grammar. It allows you to create interactive, informative charts using a consistent API, where you describe the links between data columns and visual encoding channels (like x-axis, y-axis, color, size) rather than explicitly coding drawing commands.

Use chart.validate() to check for invalid specifications.