Scatter Plots

Scatter plots are useful for showing the relationship between columns. Scatter plots in Plotly Studio are highly customizable - you can map additional columns to color and marker size, and apply styles and customizations like trend lines, opacity, and axis labels.

Basic scatter example

Here's an example of how to structure a prompt to plot two columns against each other to see their relationship. The prompt specifies the chart type as well as the names of the columns from the dataset to use for the X and Y axes:

Create a scatterplot of <X> vs. <Y>.

The following example uses this prompt structure with the "weight" and "length" columns from the built-in Plotly Studio dataset:

Create a scatterplot of weight vs. length.

Scatter plot showing weight vs length

Color example

Use color to show different values of a categorical or numeric column and identify patterns.

Create a scatterplot of <X> vs. <Y>, and color by <Column Name>.

The following example uses this prompt structure with the "weight", "length", and "defect" columns from the built-in Plotly Studio dataset:

By category example

Create a scatterplot of weight vs. length, and color by defect.

Scatter plot colored by Defect category

By numeric value example

The following example plots "weight" vs "length", colored by a computed "shipping_time" field, using the built-in Plotly Studio dataset:

Create a scatterplot of weight vs. length by shipping time.
Shipping time is calculated as the difference between shipped 
date and created date in days.

Scatter plot colored by numerical shipping time

Size by column

Vary marker size based on a numeric column to add a third dimension to a scatter plot.

Create a scatterplot of <X> vs. <Y>. Size by <Column Name>.

The following example uses this prompt structure to plot "weight" and "length", with marker size representing "shipping time" (calculated as a computed field).

Create a scatterplot of weight vs. length by shipping time.
Shipping time is calculated as the difference between shipped
date and created date in days. Size by shipping time.

Scatter plot with marker size varying by shipping time

Scatter plots with categorical axes

Earlier examples show numeric values on both chart axes, but other data types can also be mapped to the axes. A scatter plot where one axis is categorical is often known as a dot plot. They are specified the same way as numeric values.

Create a scatterplot of <X> vs. <Y>.

The following example uses this prompt structure with the factory_location and weight columns.

Create a scatterplot of factory location vs. weight.

Dot plot showing weight distribution by factory location

Facet plots, also known as trellis plots or small multiples, create multiple subplots with the same axes, where each subplot shows a different subset of the data. This helps you compare patterns across categories. You can arrange facets by columns (horizontally), by rows (vertically), or both.

Create a scatterplot of <X> vs. <Y>. Facet 
<vertically/horizontally/both vertically and horizontally> by <Column Name>.

The following example uses this prompt structure with the weight, length, and factory_location columns from the built-in Plotly Studio dataset:

Create a scatterplot of weight vs. length by factory location.
Facet by factory location.

Faceted scatter plot showing weight vs length for each factory location

Prompt keywords reference

Use these keywords and phrases in your prompts to customize your scatter plot.

Chart

Here are some keyword suggestions to create and customize a chart:

Keyword/Phrase	Description	Example
X	The column to show on the horizontal axis	`Weight on the X-axis`
Y	The column to show on the vertical axis	`Length on the Y-axis`
Color	Color points by different groups or values	`Color by Factory location`
Size	Make scatter points larger or smaller based on a value from the dataset	`Size by weight`
Symbol	Map different marker shapes to categories	`Use different symbols for each defect`
Facet columns	Create multiple subplots side-by-side for each category	`Facet by factory location`
Facet rows	Create multiple subplots stacked vertically for each category	`Facet vertically by defect`

Data

Specify data instructions in your prompt to specify how to transform, filter, or aggregate your data for visualization.

Calculate shipping days as the difference between shipped date and 
created date in days.

Here are some keyword suggestions to use in this section:

Keyword/Phrase	Description	Example
Aggregation	Specify how to aggregate data	`Calculate the average weight by Factory location`
Calculate	Create new calculated fields from existing data	`Calculate shipping days as the difference between shipped date and created date in days`
Filter	Filter data to show only specific records	`Filter to show only defects`

Options

Specify options in your prompt to add interactive controls that allow you to dynamically filter, transform, and visualize data without regenerating the chart.

Add a dropdown for factory (All, Osaka, Seoul, Singapore) - Default All
Add a dropdown to filter by Weight range (All, 0-1 kg, 1-2 kg, 2-3 kg) - Default All

Here are some keyword suggestions to use with this section. See App Controls for a complete list of control types and additional examples.

Keyword/Phrase	Description	Example
Dropdown	Add a dropdown menu to filter by categories	`Add a dropdown to select factory (All, Osaka, Seoul) - Default All`

Chart styles

Specify chart styles in your prompt to control the visual appearance and formatting of your scatter plot.

Use these custom colors: #FF5733, #33FF57, #3357FF.
Set opacity to 0.5.
Add a linear trend line.
Label x-axis as "Product Weight (kg)".