The series_abs function transforms all values in a numeric dynamic array (series) into their absolute values. This means that it converts negative values to their positive equivalents while leaving non-negative values unchanged. You can use series_abs when you want to normalize data and remove the effect of directionality. For example, it’s useful in time-series scenarios where you want to analyze the magnitude of changes regardless of whether they’re positive or negative. Typical applications include error analysis, performance monitoring, and anomaly detection.

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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.

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Usage

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Syntax

series_abs(array)

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Parameters

ParameterTypeDescription
arraydynamicA dynamic array of numeric values.

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Returns

A dynamic array where each element is the absolute value of the corresponding input element.

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Use case examples

In log analysis, you can use series_abs to analyze request durations by focusing on their magnitude, regardless of whether values are represented as positive or negative deviations.Query
['sample-http-logs']
| summarize durations = make_list(req_duration_ms) by id
| extend abs_durations = series_abs(durations)
Run in PlaygroundOutput
iddurationsabs_durations
u123[-50, 30, -10, 20][50, 30, 10, 20]
u456[5, -7, -3, 9][5, 7, 3, 9]
This query collects request durations for each user, then converts them into absolute values for magnitude-based analysis.
  • series_acos: Returns the arc cosine of each element in an array. Use when you need to invert cosine transformations instead of sine.
  • series_asin: Applies the arc sine function element-wise to array values. Use this when you need the inverse sine instead of the inverse cosine.
  • series_atan: Returns the arc tangent of each element in an array. Useful for handling tangent-derived data.