---
title: Find median values
description: Use the median() function to return a value representing the 0.5 quantile (50th percentile) or median of input data.
url: https://docs.influxdata.com/enterprise_influxdb/v1/flux/guides/median/
estimated_tokens: 1031
publisher: InfluxData
canonical: https://docs.influxdata.com/enterprise_influxdb/v1/flux/guides/median/
date: '2025-01-13T07:21:11-07:00'
lastmod: '2025-01-13T07:21:11-07:00'
---

Use the [`median()` function](/flux/v0/stdlib/universe/median/)to return a value representing the `0.5` quantile (50th percentile) or median of input data.

## Select a method for calculating the median

Select one of the following methods to calculate the median:

* [estimate\_tdigest](#estimate-tdigest)
* [exact\_mean](#exact-mean)
* [exact\_selector](#exact-selector)

### estimate\_tdigest

**(Default)** An aggregate method that uses a [t-digest data structure](https://github.com/tdunning/t-digest)to compute an accurate `0.5` quantile estimate on large data sources.
Output tables consist of a single row containing the calculated median.

**Given the following input table:**

|       \_time       |\_value|
|--------------------|-------|
|2020-01-01T00:01:00Z|  1.0  |
|2020-01-01T00:02:00Z|  1.0  |
|2020-01-01T00:03:00Z|  2.0  |
|2020-01-01T00:04:00Z|  3.0  |

**`estimate_tdigest` returns:**

|\_value|
|-------|
|  1.5  |

### exact\_mean

An aggregate method that takes the average of the two points closest to the `0.5` quantile value.
Output tables consist of a single row containing the calculated median.

**Given the following input table:**

|       \_time       |\_value|
|--------------------|-------|
|2020-01-01T00:01:00Z|  1.0  |
|2020-01-01T00:02:00Z|  1.0  |
|2020-01-01T00:03:00Z|  2.0  |
|2020-01-01T00:04:00Z|  3.0  |

**`exact_mean` returns:**

|\_value|
|-------|
|  1.5  |

### exact\_selector

A selector method that returns the data point for which at least 50% of points are less than.
Output tables consist of a single row containing the calculated median.

**Given the following input table:**

|       \_time       |\_value|
|--------------------|-------|
|2020-01-01T00:01:00Z|  1.0  |
|2020-01-01T00:02:00Z|  1.0  |
|2020-01-01T00:03:00Z|  2.0  |
|2020-01-01T00:04:00Z|  3.0  |

**`exact_selector` returns:**

|       \_time       |\_value|
|--------------------|-------|
|2020-01-01T00:02:00Z|  1.0  |

> [!Note]
> The examples below use the [example data variable](/enterprise_influxdb/v1/flux/guides/#example-data-variable).

## Find the value that represents the median

Use the default method, `"estimate_tdigest"`, to return all rows in a table that
contain values in the 50th percentile of data in the table.

```js
data
    |> median()
```

## Find the average of values closest to the median

Use the `exact_mean` method to return a single row per input table containing the
average of the two values closest to the mathematical median of data in the table.

```js
data
    |> median(method: "exact_mean")
```

## Find the point with the median value

Use the `exact_selector` method to return a single row per input table containing the
value that 50% of values in the table are less than.

```js
data
    |> median(method: "exact_selector")
```

## Use median() with aggregateWindow()

[`aggregateWindow()`](/flux/v0/stdlib/universe/aggregatewindow/)segments data into windows of time, aggregates data in each window into a single
point, and then removes the time-based segmentation.
It is primarily used to downsample data.

To specify the [median calculation method](#select-a-method-for-calculating-the-median) in `aggregateWindow()`, use the[full function syntax](/flux/v0/stdlib/universe/aggregatewindow/#specify-parameters-of-the-aggregate-function):

```js
data
    |> aggregateWindow(every: 5m, fn: (tables=<-, column) => tables |> median(method: "exact_selector"))
```
| _time | _value |
| --- | --- |
| _time | _value |
| 2020-01-01T00:01:00Z | 1.0 |
| 2020-01-01T00:02:00Z | 1.0 |
| 2020-01-01T00:03:00Z | 2.0 |
| 2020-01-01T00:04:00Z | 3.0 |

| _value |
| --- |
| _value |
| 1.5 |

| _time | _value |
| --- | --- |
| _time | _value |
| 2020-01-01T00:01:00Z | 1.0 |
| 2020-01-01T00:02:00Z | 1.0 |
| 2020-01-01T00:03:00Z | 2.0 |
| 2020-01-01T00:04:00Z | 3.0 |

| _value |
| --- |
| _value |
| 1.5 |

| _time | _value |
| --- | --- |
| _time | _value |
| 2020-01-01T00:01:00Z | 1.0 |
| 2020-01-01T00:02:00Z | 1.0 |
| 2020-01-01T00:03:00Z | 2.0 |
| 2020-01-01T00:04:00Z | 3.0 |

| _time | _value |
| --- | --- |
| _time | _value |
| 2020-01-01T00:02:00Z | 1.0 |