DataframeStd / Aggregation Layer

Calculate standard deviation across specified columns, similar to pandas std() or numpy std().

Mathematical form: $σ = \frac{\sum _{i = 1}^{N} ( x _{i} - x ˉ ) ^{2}}{N - ddof}$ Where:

$σ$ is the standard deviation
$N$ is the number of observations
$\overset{x}{ˉ}$ is the mean
ddof adjusts degrees of freedom

Common applications:

Measuring data variability
Quality control monitoring
Risk assessment in finance
Scientific measurement uncertainty
Process control in manufacturing
Performance variation analysis

Provides multiple ddof configurations for detailed statistical analysis.

Table

Select

[column, ...]

Numeric columns to compute standard deviation for. If empty, processes all numeric columns. Non-numeric columns are ignored. Selected columns should contain data suitable for variance calculation.

DdofValues

[, ...]

Configuration for standard deviation calculation with specific degrees of freedom adjustment. Choice of ddof affects the bias in estimation:

ddof=1 (default): Sample standard deviation (unbiased)
ddof=0: Population standard deviation (biased)

Ddof

Delta Degrees of Freedom (ddof) for standard deviation calculation. Common values:

1: For sample statistics (n-1 denominator)
0: For population statistics (n denominator) Higher values increase the estimated variance.