ZScaler#

ZScaler performs Z-score standardization, also known as standard scaling.

It transforms numeric values in selected columns so that each feature has zero mean and unit variance, using statistics learned during a fitting phase.

ZScaler is designed to work with Rowan tables and follows a stateful workflow: statistics are learned during Fit and later applied during Transform.

Overview#

ZScaler computes the mean and standard deviation for each selected feature and applies the following transformation formula:

scaled = (x - mean) / std

The scaler stores per-column statistics internally and applies transformations in a non-mutating manner, meaning the original table is never modified.

Only numeric columns are considered during fitting and transformation.

Lifecycle#

ZScaler follows a simple lifecycle:

1. Create a new scaler instance using NewZScaler()
2. Call Fit() to learn per-column mean and standard deviation
3. Call Transform() to standardize data using the learned statistics
4. Optionally call Reset() to clear the internal state

Internal State#

ZScaler maintains the following internal state:

• features
  A slice of column names that were fitted.

• min
  A map storing the mean value for each fitted column.

• max
  A map storing the standard deviation for each fitted column.

This state is updated only during Fit and cleared when Reset() is called.

Constructor#

NewZScaler()#

NewZScaler() creates and initializes a new ZScaler instance.

The returned scaler starts with an empty internal state and must be fitted before it can be used to transform data.

Example#

import (
	"github.com/go-rowan/rowan"
	"github.com/go-rowan/rowan/scale"
)

func main() {
	tbl, _ := rowan.FromExcel("data.xlsx")

	zScaler := scale.NewZScaler()
}

Available Methods#

Common Methods#

• (*ZScaler).Fit(t *Table, columns ...string)
• (*ZScaler).Transform(t *Table, columns ...string)
• (*ZScaler).Features()
• (*ZScaler).IsFitted()
• (*ZScaler).Reset()

ZScaler-Specific Methods#

• (*ZScaler).Mean(column string)
• (*ZScaler).Std(column string)

When to Use#

Use ZScaler when you need to:

• Standardize numeric features to zero mean and unit variance
• Prepare data for algorithms that assume normally distributed inputs
• Perform consistent standardization learnt from a reference dataset

ZScaler is commonly used in statistical modeling and machine learning workflows.

See Also#

• RangeScaler
• (*ZScaler).Fit(t *Table, columns ...string)
• (*ZScaler).Transform(t *Table, columns ...string)
• (*ZScaler).Mean(column string)