scatter_plot
Visualize relationships in data by generating scatter plots directly from SQL queries on CSV or Parquet sources. Use column names for axes and optional color coding to enhance insights.
Instructions
Run query against specified source and make a scatter plot using result For both csv and parquet sources, use DuckDB SQL syntax Use 'CSV' as the table name in the SQL query for csv sources. Use 'PARQUET' as the table name in the SQL query for parquet sources.
This will return an image of the plot
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| color | No | Optional; column name from SQL result to use for coloring the points, with color representing another dimension | |
| query | Yes | SQL query to run on the data source | |
| source_id | Yes | The data source to run the query on | |
| x | Yes | Column name from SQL result to use for x-axis | |
| y | Yes | Column name from SQL result to use for y-axis |
Input Schema (JSON Schema)
{
"properties": {
"color": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"description": "Optional; column name from SQL result to use for coloring the points, with color representing another dimension",
"title": "Color"
},
"query": {
"description": "SQL query to run on the data source",
"title": "Query",
"type": "string"
},
"source_id": {
"description": "The data source to run the query on",
"title": "Source Id",
"type": "string"
},
"x": {
"description": "Column name from SQL result to use for x-axis",
"title": "X",
"type": "string"
},
"y": {
"description": "Column name from SQL result to use for y-axis",
"title": "Y",
"type": "string"
}
},
"required": [
"source_id",
"query",
"x",
"y"
],
"type": "object"
}