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# atoti.agg.std()

### atoti.agg.std(operand: LevelOrVariableColumnConvertible, /, \*, mode: [Literal](https://docs.python.org/3/library/typing.html#typing.Literal)\['sample', 'population'] = 'sample') → MeasureDefinition

### atoti.agg.std(operand: VariableMeasureConvertible, /, \*, mode: [Literal](https://docs.python.org/3/library/typing.html#typing.Literal)\['sample', 'population'] = 'sample', scope: [CumulativeScope](./atoti.scope.cumulative_scope#atoti.CumulativeScope) | [SiblingsScope](./atoti.scope.siblings_scope#atoti.SiblingsScope) | [OriginScope](./atoti.scope.origin_scope#atoti.OriginScope)) → MeasureDefinition

Return a measure equal to the standard deviation of the passed operand across the specified scope.

* **Parameters:**
  * **operand** – The operand to get the standard deviation of.
  * **mode** –

    One of the supported modes:

    * The `sample` standard deviation, similar to Excel’s `STDEV.S`, is $\sqrt{\frac{\sum_{i=1}^{n} (X_i - m)^{2}}{n - 1}}$ where `m` is the sample mean and `n` the size of the sample.
      Use this mode if the data represents a sample of the population.
    * The `population` standard deviation, similar to Excel’s `STDEV.P` is $\sqrt{\frac{\sum_{i=1}^{n}(X_i - m)^{2}}{n}}$ where `m` is the mean of the `Xi` elements and `n` the size of the population.
      Use this mode if the data represents the entire population.
  * **scope** – The [`aggregation scope`](./atoti.scope#module-atoti.scope).