Shape · Statistics

Correlation Matrix

Shows how strongly each pair of number columns moves together, as a grid of values from −1 to 1.

Also known as: Pearson, Spearman

How it works

Columns limits the matrix; empty means every numeric column. Method is Pearson by default, or Spearman or Kendall. The output has one row per column, labelled in a column column, and one column per column, so the diagonal is 1. Min periods applies to Spearman and Kendall: each pair drops rows where either value is NULL and returns NULL when fewer rows than that remain; it defaults to 1. Pearson runs as SQL; the other two run on the desktop in Python.

Inputs and outputs

Settings

{
  columns: string[]
  method: 'pearson'|'spearman'|'kendall'
  minPeriods?: number
}

Example

Check whether price, footfall and sales move together: columns price, footfall, sales, method Pearson.

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