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min_max_scaler

min_max_scaler(arr)

Description

Performs min-max scaling on an array. It takes an array of numbers as input and returns an array of values scaled between 0 and 1.

Examples

Call or Deploy min_max_scaler ?
Call min_max_scaler directly

The easiest way to use bigfunctions

  • min_max_scaler function is deployed in 39 public datasets for all of the 39 BigQuery regions.
  • It can be called by anyone. Just copy / paste examples below in your BigQuery console. It just works!
  • (You need to use the dataset in the same region as your datasets otherwise you may have a function not found error)

Public BigFunctions Datasets

Region Dataset
eu bigfunctions.eu
us bigfunctions.us
europe-west1 bigfunctions.europe_west1
asia-east1 bigfunctions.asia_east1
... ...
Deploy min_max_scaler in your project

Why deploy?

  • You may prefer to deploy min_max_scaler in your own project to build and manage your own catalog of functions.
  • This is particularly useful if you want to create private functions (for example calling your internal APIs).
  • Get started by reading the framework page

Deployment

min_max_scaler function can be deployed with:

pip install bigfunctions
bigfun get min_max_scaler
bigfun deploy min_max_scaler

select bigfunctions.eu.min_max_scaler([1, 2, 3, 4, 5])

select bigfunctions.us.min_max_scaler([1, 2, 3, 4, 5])

select bigfunctions.europe_west1.min_max_scaler([1, 2, 3, 4, 5])

+-------------------------+
| scaled_array            |
+-------------------------+
| [0, 0.25, 0.5, 0.75, 1] |
+-------------------------+
Need help or Found a bug using min_max_scaler?
Get help using min_max_scaler

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Report a bug about min_max_scaler

If the function does not work as expected, please

  • report a bug so that it can be improved.
  • or open the discussion with the community on Slack.

We also provide professional suppport.

Use cases

Let's say you have a table of product prices and you want to compare their relative affordability. The prices range from $10 to $1000, but you need them on a normalized scale between 0 and 1 for a machine learning model or visualization. Here's how min_max_scaler can be used:

WITH ProductPrices AS (
    SELECT 'Product A' AS product, 10 AS price
    UNION ALL SELECT 'Product B' AS product, 50 AS price
    UNION ALL SELECT 'Product C' AS product, 200 AS price
    UNION ALL SELECT 'Product D' AS product, 1000 AS price
),
MinMaxScaledPrices AS (
  SELECT
      product,
      bigfunctions.us.min_max_scaler(ARRAY_AGG(price) OVER ()) AS scaled_prices
  FROM ProductPrices
)
SELECT
    product,
    scaled_price
FROM MinMaxScaledPrices, UNARRAY(scaled_prices) AS scaled_price;

This query first collects all prices into an array using ARRAY_AGG. Then, min_max_scaler normalizes these prices within the array. Finally, the UNARRAY function expands the resulting array so you get each product and its scaled price on separate rows.

This results in a table like this (the exact values might vary slightly due to floating-point precision):

product scaled_price
Product A 0
Product B 0.04
Product C 0.19
Product D 1

Now "Product A", with the lowest price, has a scaled price of 0, and "Product D", with the highest price, has a scaled price of 1. The other products have scaled prices in between, reflecting their relative affordability.

Another use case would be normalizing features in a machine learning preprocessing step directly within BigQuery before exporting the data for training. This can simplify your data pipeline.

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