Use a scatter plot when you want to see how two numeric variables relate to each other. It displays your data points on a coordinate grid, making it easy to spot hidden patterns, clusters, or unusual values that might otherwise be missed in a standard table or list.
Understanding Relationships in Your Data
The scatter plot is a foundational tool for spotting correlations between variables. By plotting your data on X and Y axes, you can instantly see if a change in one factor corresponds to a change in another.
On Lattice, the scatter plot is designed to handle your data directly. It creates a clean visual representation of every data point, ensuring you don't lose sight of individual observations while looking for high-level trends.
Comparing Categories with Grouping
If your data includes categorical information, you can use the grouping feature to create distinct series. This allows you to differentiate between segments—such as different product lines, regions, or time periods—within a single view.
Each group is assigned a unique color, enabling you to identify whether specific groups cluster in certain areas of the plot or follow the same general trend as the rest of your data.
Creating Sub-views with Faceting
When you have many categories that would look too cluttered on a single chart, the faceting feature lets you split your scatter plot into smaller, side-by-side panels. This makes it easier to compare the behavior of different segments without them overlapping.
Lattice automatically manages these panels to ensure the layout remains readable. If you have a large number of categories, the platform intelligently focuses on the most prominent ones to keep your analysis clear and actionable.
Handling Missing Data
Data is rarely perfect, and a scatter plot must handle gaps carefully. Our approach uses listwise removal per series, meaning if a row is missing information for either the X or Y axis, that specific point is excluded from the plot.
This approach ensures that your visualization is always based on complete pairs, providing an accurate reflection of the relationship between variables without introducing distortions from incomplete data points.
1 · Intent → method
An LLM picks plot_scatter from a fixed catalog.
2 · Method → numbers
Deterministic Python engine runs the math. Same input → same output.
3 · Numbers → plain language
A second LLM translates the result into your domain’s vocabulary.
What does a scatter plot show?
A scatter plot shows the relationship between two numeric variables by placing each data row as a point on a chart. It helps you determine if one variable increases as the other increases, or if there is no clear connection between them.
How do I compare different groups on the same chart?
You can use the 'group by' option to color-code your points by category. This allows you to visualize multiple groups on one scatter plot so you can see if different segments of your data behave differently.
Tool input schema
Schema for plot_scatter not exported yet (run pnpm export:registry).