Dataset Schema
Retrieve the inferred schema and column metadata for a previously uploaded dataset.
/v1/datasets/{dataset_id}/schema
curl -X GET "https://analytics.toolkitapi.io/v1/datasets/dset_abc123/schema" \
-H "X-API-Key: YOUR_API_KEY"
import httpx
resp = httpx.get(
"https://analytics.toolkitapi.io/v1/datasets/dset_abc123/schema",
)
print(resp.json())const resp = await fetch("https://analytics.toolkitapi.io/v1/datasets/dset_abc123/schema", {
});
const data = await resp.json();
console.log(data);# See curl example{
"dataset_id": "dset_abc123",
"row_count": 1500,
"columns": [
{
"name": "order_date",
"dtype": "datetime64[ns]",
"sample_values": ["2024-01-15", "2024-02-03", "2024-03-22"],
"null_count": 0,
"unique_count": 312
},
{
"name": "revenue",
"dtype": "float64",
"sample_values": [149.99, 299.50, 89.00],
"null_count": 3,
"unique_count": 874
},
{
"name": "region",
"dtype": "object",
"sample_values": ["North", "South", "East"],
"null_count": 0,
"unique_count": 4
}
]
}Description
How to Use
1. Call `POST /v1/analyze` with your data source and receive a `dataset_id` in the response. 2. Use that `dataset_id` as the path parameter: `GET /v1/datasets/{dataset_id}/schema`. 3. Inspect the returned `columns` array to understand each field's name, inferred type, and data quality metrics. 4. Use column names and types to craft accurate queries with `POST /v1/query/{query_id}` or chart definitions with `POST /v1/visualize`.
About This Tool
The Dataset Schema endpoint returns the inferred column structure of a dataset you have previously uploaded via the `/v1/analyze` endpoint. Use it to inspect column names, data types, null counts, unique value counts, and representative sample values before writing queries or building visualisations.
This is especially useful when you upload data programmatically and need to confirm how the engine has interpreted each column's type before proceeding to the query or visualize steps.
Why Use This Tool
- Pre-query validation — Confirm column names and types before submitting natural-language or structured queries.
- Dynamic UI building — Populate column selectors or filter dropdowns in a dashboard based on the live schema.
- Data quality checks — Identify columns with high null counts before including them in aggregations.
- Onboarding pipelines — Programmatically inspect uploaded files to route them to the correct downstream workflow.
- Type-safe chart configuration — Verify that axes map to numeric or datetime columns before rendering a chart.
Start using Dataset Schema now
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