Altair Instant

# 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

You can refine your plot by adding titles, changing colors, and adjusting axes using .properties() and alt.Axis() .

Create a specific (e.g., click a bar to filter data)? altair

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

# Create a bar chart with the average of column 'b' alt.Chart(data).mark_bar().encode( x='a', y='mean(b)' # Aggregation ) Use code with caution. Copied to clipboard 4. Customizing Your Visualization # Simple interactive tooltip alt

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.

# Create and activate a virtual environment python -m venv altair-venv source altair-venv/bin/activate # On Windows: altair-venv\Scripts\activate # Install Altair and dependencies python -m pip install altair pandas notebook Use code with caution. Copied to clipboard 2. Core Concepts: The Chart Object Every Altair chart follows three basic steps: Pass a pandas DataFrame to alt.Chart() . Create a specific (e

You can save your chart as a JSON file (Vega-Lite spec) or render it as an image/HTML file. chart.save('chart.html') Use code with caution. Copied to clipboard