# Find percentile and quantile values

See the equivalent **InfluxDB v2.6** documentation: Find percentile and quantile values.

Use the `quantile()`

function
to return a value representing the `q`

quantile or percentile of input data.

## Percentile versus quantile

Percentiles and quantiles are very similar, differing only in the number used to calculate return values.
A percentile is calculated using numbers between `0`

and `100`

.
A quantile is calculated using numbers between `0.0`

and `1.0`

.
For example, the ** 0.5 quantile** is the same as the

**50th percentile**.

## Select a method for calculating the quantile

Select one of the following methods to calculate the quantile:

### estimate_tdigest

**(Default)** An aggregate method that uses a t-digest data structure
to compute a quantile estimate on large data sources.
Output tables consist of a single row containing the calculated quantile.

If calculating the `0.5`

quantile or 50th percentile:

**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 quantile value. Output tables consist of a single row containing the calculated quantile.

If calculating the `0.5`

quantile or 50th percentile:

**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 `q`

points are less than.
Output tables consist of a single row containing the calculated quantile.

If calculating the `0.5`

quantile or 50th percentile:

**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 |

The examples below use the example data variable.

## Find the value representing the 99th percentile

Use the default method, `"estimate_tdigest"`

, to return all rows in a table that
contain values in the 99th percentile of data in the table.

```
data
|> quantile(q: 0.99)
```

## Find the average of values closest to the quantile

Use the `exact_mean`

method to return a single row per input table containing the
average of the two values closest to the mathematical quantile of data in the table.
For example, to calculate the `0.99`

quantile:

```
data
|> quantile(q: 0.99, method: "exact_mean")
```

## Find the point with the quantile value

Use the `exact_selector`

method to return a single row per input table containing the
value that `q * 100`

% of values in the table are less than.
For example, to calculate the `0.99`

quantile:

```
data
|> quantile(q: 0.99, method: "exact_selector")
```

## Use quantile() with aggregateWindow()

`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 quantile calculation method in
`aggregateWindow()`

, use the full function syntax:

```
data
|> aggregateWindow(
every: 5m,
fn: (tables=<-, column) => tables
|> quantile(q: 0.99, method: "exact_selector"),
)
```

Was this page helpful?

Thank you for your feedback!

#### Support and feedback

Thank you for being part of our community! We welcome and encourage your feedback and bug reports for InfluxDB and this documentation. To find support, use the following resources:

**InfluxDB Cloud and InfluxDB Enterprise customers** can contact InfluxData Support.