​
Coloring

You’re able to customize the color palette used in a plot. You can select a premade palette, or create your own. Colors in a palette are used in a plot sequentially per series or color group.

​
Specifying coloring groups

In the series settings, you’re able to specify a column to color plot markers by. This usually works best when using categorical columns in the source table In some plot types this will both color and group by the category.

​
Axis

​
Customizing ticks

You can customize X & Y how ticks are displayed on a plot axis. This includes the scale, main ticks, sub-ticks, and weight and size.

​
Size

You are able to adjust the height and width of a plot. To adjust the height use the height slider at the bottom of the plot. To adjust width there is an option to enable a maximum width size for the plot.

​
GraphPad Prism-Inspired Themes

For scientists who are familiar with GraphPad Prism, we provide an out-of-the-box Prism-inspired theme.

​
How to apply the theme

On the configuration sidebar of a no-code plot, simply select Theme and click GraphPad Inspired.

​
Customize your theme

If you want to further refine your plot, here are some tips to enhance the GraphPad-like appearance:
  1. Adjust font weight
  2. Tilt axis labels on the X or Y-axis to improve readability.
  3. Modify bar spacing for individual or grouped bars.
  4. Update the color palette to match your branding or study.

​
For developers

If you’re working directly with code, you can apply the GraphPad-Inspired Theme by passing template="graphpad_inspired_theme" to your Plotly figure. Here’s an example: 
import plotly.express as px

fig_scatter = px.scatter(df_2619, x='rqMean', y='rqSD', color='Target Name', 
                         hover_data=['Sample Name', 'rq'],
                         labels={'rqMean': 'Mean Relative Quantity', 
                                 'rqSD': 'Standard Deviation of Relative Quantity'},
                         title='Scatter Plot of Mean Relative Quantity vs Standard Deviation')

fig_scatter.update_layout(
    legend_title='Target Name',
    xaxis_title='Mean Relative Quantity',
    yaxis_title='Standard Deviation of Relative Quantity',
    template='graphpad_inspired_theme', ### APPLY GRAPHPAD PRISM THEME
    autosize=True
)