---
title: "Profiling a dataset"
sidebarTitle: "Aggregate"
description: "Counting, grouping and summarizing — finding out what your data actually contains."
icon: chart-column
keywords: ["aggregate", "group by", "class balance", "count", "profiling", "distinct"]
---

<Badge color="red">Enterprise</Badge> Aggregation is SQL, and
[SQL needs a deployment](/basics/sql#sql-is-remote).

Before curating anything, find out what you have. Most of the surprises in a
training set — a class that is 90% of the rows, a column that is half null, a
"unique" id that is not — are one `GROUP BY` away from being visible.

## How big, and how complete {#size}

```sql
SELECT
    count(*)            AS rows,
    count(embedding)    AS embedded,
    count(caption)      AS captioned
FROM images
```

`count(*)` counts rows; `count(col)` counts **non-null values**. The gap between
them is your coverage, which is the fastest way to check how far a
[column refresh](/feature-engineering/refresh-column) has got without waiting
for the job to report.

## Class balance {#balance}

The single most useful curation query:

```sql
SELECT label, count(*) AS n
FROM images
GROUP BY label
ORDER BY n DESC
```

If the top class is an order of magnitude larger than the rest, sampling
uniformly will reproduce that imbalance. Two ways out: draw a fixed number per
class with [windowing](/curation/windowing#per-group), or oversample the rare
ones and accept the duplication.

## Distinct values {#distinct}

```sql
SELECT count(DISTINCT source) FROM images
```

On a high-cardinality column this is expensive, because exact distinctness means
holding every value seen. When an estimate will do:

```sql
SELECT approx_distinct(source) FROM images
```

Cheap enough to run on a table where the exact version is not worth the wait.
The other approximate functions — `approx_median`, `approx_percentile_cont` —
are useful for the same reason when profiling a numeric column.

## Conditional counts {#conditional}

`FILTER` narrows one aggregate without narrowing the query, which is how you get
several slices in one pass rather than one query each:

```sql
SELECT
    label,
    count(*)                              AS total,
    count(*) FILTER (WHERE score > 0.9)   AS confident,
    count(*) FILTER (WHERE caption IS NULL) AS uncaptioned
FROM images
GROUP BY label
```

## Grouping by a bucket {#buckets}

Raw values are rarely the grouping you want. `date_bin` buckets timestamps, and
ordinary arithmetic buckets numbers:

```sql
SELECT date_bin(INTERVAL '1 day', created_at) AS day, count(*)
FROM images GROUP BY day ORDER BY day
```

A histogram of a score column is the same trick:

```sql
SELECT floor(score * 10) / 10 AS bucket, count(*)
FROM images GROUP BY bucket ORDER BY bucket
```

## Profiling a sample {#sampling}

On a very large table, profile a [sample](/curation/sampling) and scale:

```sql
SELECT label, count(*) * 100 AS estimated
FROM images TABLESAMPLE (1)
GROUP BY label
```

Treat the result as an estimate. A 1% sample will miss classes rarer than about
1 in 10,000 entirely, which is exactly the tail you are usually looking for — so
use this to check the shape of the head, not the existence of the tail.

## Reference {#reference}

The complete clause and function list is in
[Aggregation](/sql/dql/aggregation).