min_max_scaler¶

Call or Deploy min_max_scaler ?

✅ You can call this min_max_scaler bigfunction directly from your Google Cloud Project (no install required).

This min_max_scaler function is deployed in bigfunctions GCP project in 39 datasets for all of the 39 BigQuery regions. You need to use the dataset in the same region as your datasets (otherwise you may have a function not found error).
Function is public, so it can be called by anyone. Just copy / paste examples below in your BigQuery console. It just works!
You may prefer to deploy the BigFunction in your own project if you want to build and manage your own catalog of functions --> Read Getting Started. This is particularly useful if you want to create private functions (for example calling your internal APIs).
For any question or difficulties, please read Getting Started.
Found a bug? Please raise an issue here

Public BigFunctions Datasets are like:

Region	Dataset
`eu`	`bigfunctions.eu`
`us`	`bigfunctions.us`
`europe-west1`	`bigfunctions.europe_west1`
`asia-east1`	`bigfunctions.asia_east1`
...	...

Description¶

Signature

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¶

EUUSeurope-west1

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] |
+-------------------------+

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.