Skip to content

bigfunctions > z_scores

z_scores

Call or Deploy z_scores ?

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

  • This z_scores 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. This is particularly useful if you want to create private functions (for example calling your internal APIs). Discover the framework

Public BigFunctions Datasets:

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

Description

Signature

z_scores(arr)

Description

Compute z_scores for each value of arr array.

The Z-Score is the number of standard deviations by which the value is above or below the mean value.

Examples

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

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

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

+-----------------------------------+
| z_scores                          |
+-----------------------------------+
| [-1.414, -0.707, 0, 0.707, 1.414] |
+-----------------------------------+
Need help using z_scores?

The community can help! Engage the conversation on Slack

For professional suppport, don't hesitate to chat with us.

Found a bug using z_scores?

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.

For professional suppport, don't hesitate to chat with us.

Use cases

A use case for the z_scores function is to identify outliers in a dataset. Let's imagine you have a table of website session durations in seconds:

CREATE OR REPLACE TABLE `your_project.your_dataset.session_durations` AS
SELECT * FROM UNNEST([
    10, 25, 30, 35, 40, 45, 50, 55, 60, 300, 65, 70, 75, 80, 85
]) AS session_duration;

You suspect that the session duration of 300 seconds is an outlier. You can use z_scores to confirm this:

SELECT
    session_duration,
    bigfunctions.your_region.z_scores(ARRAY_AGG(session_duration) OVER ()) as z_score
  FROM
    `your_project.your_dataset.session_durations`;

Replace your_region with your BigQuery region (e.g., us, eu, us_central1).

This query will calculate the z-score for each session duration. The session with a duration of 300 seconds will likely have a z-score significantly higher than other sessions (above 2 or 3, depending on your data distribution), indicating it's an outlier. You could then filter based on the z-score to identify and potentially remove or further investigate these outlier sessions.

Other use cases include:

  • Standardizing data: Transforming data to have a mean of 0 and a standard deviation of 1, useful for comparing variables measured on different scales.
  • Anomaly detection: Similar to outlier detection, but in a time-series context, identifying unusual fluctuations in metrics.
  • Machine learning preprocessing: Many machine learning algorithms benefit from standardized input data.
  • Ranking and scoring: Z-scores can provide a relative ranking of items based on their performance compared to the average. For example, ranking students based on their test scores.

Remember to choose the correct BigQuery region for the bigfunctions dataset based on where your data resides.

Spread the word

BigFunctions is fully open-source. Help make it a success by spreading the word!

Share on Add a on