Shape · Statistics

Covariance Matrix

Works out the covariance of every pair of number columns, as a grid.

Also known as: Covariance

How it works

Columns limits the matrix; empty means every numeric column, and a chosen column that is missing or not numeric is skipped. Estimator is Sample by default (divides by n - 1) or Population (divides by n). The output has one row per column, labelled in a column column, and one column per column; the diagonal is each column's variance.

Inputs and outputs

Settings

{
  columns: string[]
  sample: boolean
}

Example

Daily returns of three funds for a risk calculation: columns fund_a, fund_b, fund_c, estimator Sample. Feed the result to the next step or read the diagonal for each fund's variance.

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.
• 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.