Shape · Statistics

Standardize

Rescales number columns to a common scale: z-score or 0 to 1. Use it before comparing measures in different units.

Also known as: Normalize, scale

How it works

Pick the Columns and a Method: Z-score subtracts the column mean and divides by the sample standard deviation; Min-max maps the column onto 0 to 1. Output Replace overwrites the columns in place; Append keeps them and adds column_z or column_norm. A column whose values are all equal has no spread, so its scaled values are NULL. No columns means nothing is scaled.

Inputs and outputs

Settings

{
  columns: string[]
  method: "zscore"|"minmax"
  output: "replace"|"append"
}

Example

Compare revenue in euros with headcount before clustering: columns revenue and headcount, method Z-score, output Append.

More in Statistics

• Describe — Summarizes each column in one row: count, blanks, mean, standard deviation, min, percentiles and max. A quick first look at a new dataset.
• Value Counts — Counts how often each value, or combination of values, appears: one row per distinct value with its count or share.
• Column Profile — Counts the distinct values in each chosen column, as long rows of column, value and count. Useful for spotting typos or dominant categories.
• Correlation Matrix — Shows how strongly each pair of number columns moves together, as a grid of values from −1 to 1.
• Covariance Matrix — Works out the covariance of every pair of number columns, as a grid.
• Linear Regression — Fits a straight-line model of one column from one or more others. Outputs the coefficients, or your rows with predictions and residuals.
• Outlier Flag — Adds a true/false column marking rows with unusually high or low values. Rows are kept, so you can filter or review them afterwards.