Altair Instant

# 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

Install Altair using pip . It is highly recommended to work within a Jupyter notebook environment (JupyterLab, VS Code, Colab) for automatic rendering.

If your data relies on a pandas index, use .reset_index() before passing it to Altair. altair

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

Choose the chart type (e.g., mark_point() , mark_bar() , mark_line() ). # Create a bar chart with the average of column 'b' alt

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

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 It is highly recommended to work within a

Map data columns to visual properties (e.g., .encode(x='column1', y='column2') ). Example: Simple Bar Chart