> ## Documentation Index
> Fetch the complete documentation index at: https://axiom.co/docs/llms.txt
> Use this file to discover all available pages before exploring further.

<AgentInstructions>

## Submitting Feedback

If you encounter incorrect, outdated, or confusing documentation on this page, submit feedback:

POST https://axiom.co/docs/feedback

```json
{
  "path": "/apl/scalar-functions/time-series/series-stats-dynamic",
  "feedback": "Description of the issue"
}
```

Only submit feedback when you have something specific and actionable to report.

</AgentInstructions>

# series_stats_dynamic

> This page explains how to use the series_stats_dynamic function in APL.

The `series_stats_dynamic` function computes comprehensive statistical measures for a numeric dynamic array (series), returning results in a dynamic object format with named properties. This provides the same statistics as `series_stats` but with more convenient access through property names instead of array indices.

You can use `series_stats_dynamic` when you need statistical summaries with easier property-based access, better code readability, or when integrating with other dynamic data structures. This is particularly useful in complex analytical workflows where referring to statistics by name (`stats.min`, `stats.avg`) is clearer than using array indices.

## For users of other query languages

If you come from other query languages, this section explains how to adjust your existing queries to achieve the same results in APL.

<AccordionGroup>
  <Accordion title="Splunk SPL users">
    In Splunk SPL, you typically use multiple `stats` functions and store results as separate fields. In APL, `series_stats_dynamic` provides all statistics in a dynamic object that you can access by property names.

    <CodeGroup>
      ```sql Splunk example theme={null}
      ... | stats min(value) as min_val, max(value) as max_val, 
          avg(value) as avg_val, stdev(value) as stdev_val by user
      ```

      ```kusto APL equivalent theme={null}
      ['sample-http-logs']
      | summarize values = make_list(req_duration_ms) by id
      | extend stats = series_stats_dynamic(values)
      | extend min_val = stats.min, max_val = stats.max, avg_val = stats.avg
      ```
    </CodeGroup>
  </Accordion>

  <Accordion title="ANSI SQL users">
    In SQL, you calculate statistics separately and work with individual columns. In APL, `series_stats_dynamic` provides all statistics in a single dynamic object with named properties that you can query and transform.

    <CodeGroup>
      ```sql SQL example theme={null}
      SELECT 
          user_id,
          MIN(value) as min_val,
          MAX(value) as max_val,
          AVG(value) as avg_val,
          STDDEV(value) as std_val
      FROM measurements
      GROUP BY user_id;
      ```

      ```kusto APL equivalent theme={null}
      ['sample-http-logs']
      | summarize values = make_list(req_duration_ms) by id
      | extend stats = series_stats_dynamic(values)
      | extend min_val = stats.min, max_val = stats.max
      ```
    </CodeGroup>
  </Accordion>
</AccordionGroup>

## Usage

### Syntax

```kusto theme={null}
series_stats_dynamic(array)
```

### Parameters

| Parameter | Type    | Description                        |
| --------- | ------- | ---------------------------------- |
| `array`   | dynamic | A dynamic array of numeric values. |

### Returns

A dynamic object containing the following statistical properties:

| Property   | Description                                           |
| ---------- | ----------------------------------------------------- |
| `min`      | The minimum value in the input array.                 |
| `min_idx`  | The first position of the minimum value in the array. |
| `max`      | The maximum value in the input array.                 |
| `max_idx`  | The first position of the maximum value in the array. |
| `avg`      | The average value of the input array.                 |
| `variance` | The sample variance of the input array.               |
| `stdev`    | The sample standard deviation of the input array.     |

## Use case examples

<Tabs>
  <Tab title="Log analysis">
    In log analysis, you can use `series_stats_dynamic` to generate comprehensive statistical reports with readable property names.

    **Query**

    ```kusto theme={null}
    ['sample-http-logs']
    | summarize durations = make_list(req_duration_ms) by id
    | extend stats = series_stats_dynamic(durations)
    | extend performance_score = 100 - (stats.stdev / stats.avg * 100)
    | project id, 
        min = stats.min,
        max = stats.max,
        avg = stats.avg,
        performance_score
    | take 5
    ```

    [Run in Playground](https://play.axiom.co/axiom-play-qf1k/query?initForm=%7B%22apl%22%3A%22%5B'sample-http-logs'%5D%20%7C%20summarize%20durations%20%3D%20make_list\(req_duration_ms\)%20by%20id%20%7C%20extend%20stats%20%3D%20series_stats_dynamic\(durations\)%20%7C%20extend%20performance_score%20%3D%20100%20-%20\(stats.stdev%20%2F%20stats.avg%20*%20100\)%20%7C%20project%20id%2C%20min%20%3D%20stats.min%2C%20max%20%3D%20stats.max%2C%20avg%20%3D%20stats.avg%2C%20performance_score%20%7C%20take%205%22%7D)

    **Output**

    | id   | min | max | avg | performance\_score |
    | ---- | --- | --- | --- | ------------------ |
    | u123 | 15  | 245 | 95  | 52.4               |
    | u456 | 8   | 189 | 78  | 50.4               |

    This query uses property names to access statistics and calculate a custom performance score based on the coefficient of variation.
  </Tab>

  <Tab title="Security logs">
    In security logs, you can use `series_stats_dynamic` to build adaptive anomaly detection thresholds with clear, self-documenting property access.

    **Query**

    ```kusto theme={null}
    ['sample-http-logs']
    | summarize durations = make_list(req_duration_ms) by status
    | extend stats = series_stats_dynamic(durations)
    | extend lower_bound = stats.avg - (2 * stats.stdev)
    | extend upper_bound = stats.avg + (2 * stats.stdev)
    | extend range_ratio = (stats.max - stats.min) / stats.avg
    | project status, 
        avg_duration = stats.avg,
        stdev = stats.stdev,
        lower_bound, 
        upper_bound, 
        range_ratio
    ```

    [Run in Playground](https://play.axiom.co/axiom-play-qf1k/query?initForm=%7B%22apl%22%3A%22%5B'sample-http-logs'%5D%20%7C%20summarize%20durations%20%3D%20make_list\(req_duration_ms\)%20by%20status%20%7C%20extend%20stats%20%3D%20series_stats_dynamic\(durations\)%20%7C%20extend%20lower_bound%20%3D%20stats.avg%20-%20\(2%20*%20stats.stdev\)%20%7C%20extend%20upper_bound%20%3D%20stats.avg%20%2B%20\(2%20*%20stats.stdev\)%20%7C%20extend%20range_ratio%20%3D%20\(stats.max%20-%20stats.min\)%20%2F%20stats.avg%20%7C%20project%20status%2C%20avg_duration%20%3D%20stats.avg%2C%20stdev%20%3D%20stats.stdev%2C%20lower_bound%2C%20upper_bound%2C%20range_ratio%22%7D)

    **Output**

    | status | avg\_duration | stdev | lower\_bound | upper\_bound | range\_ratio |
    | ------ | ------------- | ----- | ------------ | ------------ | ------------ |
    | 200    | 52            | 12.5  | 27           | 77           | 6.44         |
    | 401    | 450           | 850.2 | -1250.4      | 2150.4       | 19.68        |
    | 500    | 125           | 95.3  | -65.6        | 315.6        | 4.36         |

    This query uses named properties to calculate confidence intervals and assess the relative range of values for adaptive security monitoring.
  </Tab>
</Tabs>

## List of related functions

* [series\_stats](/apl/scalar-functions/time-series/series-stats): Returns the same statistics as a 7-element array instead of a dynamic object with named properties.
* [series\_max](/apl/scalar-functions/time-series/series-max): Compares two arrays element-wise and returns the maximum values.
* [series\_min](/apl/scalar-functions/time-series/series-min): Compares two arrays element-wise and returns the minimum values.
* [todynamic](/apl/scalar-functions/conversion-functions/todynamic): Converts values to dynamic type for custom object construction.