---
title: Distribution analysis overview | Tiger Data Docs
description: Functions for analyzing data distribution with histograms and approximate row counts
---

Distribution analysis functions help you understand how data is distributed across your datasets and perform fast approximate row counting on large tables.

## Samples

### Histogram distribution

Create a histogram showing the distribution of battery levels across devices:

```
SELECT device_id, histogram(battery_level, 20, 60, 5)
FROM readings
GROUP BY device_id
LIMIT 10;
```

The histogram partitions values into buckets between 20 and 60, with 5 equal-width buckets. The result includes an underflow bucket (values < 20) and an overflow bucket (values >= 60).

### Approximate row count

Get a fast approximate count of rows in a hypertable without a full table scan:

```
ANALYZE conditions;


SELECT * FROM approximate_row_count('conditions');
```

This uses database statistics to provide a quick estimate, which is particularly useful for very large tables where exact counts would be expensive.

## Available functions

### Distribution analysis

- [`histogram()`](/docs/reference/timescaledb/hyperfunctions/distribution-analysis/histogram/index.md): partition a dataset into buckets and get the number of counts in each bucket

### Approximate aggregation

- [`approximate_row_count()`](/docs/reference/timescaledb/hyperfunctions/distribution-analysis/approximate_row_count/index.md): estimate the number of rows in a table using catalog statistics