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bigfunctions > quantize_into_bins

quantize_into_bins

Call or Deploy quantize_into_bins ?

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

  • This quantize_into_bins 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

quantize_into_bins(value, bin_bounds)

Description

Get the bin_range in which belongs value with bins defined by their bin_bounds.

Examples

select bigfunctions.eu.quantize_into_bins(-4, [0, 1, 5, 10])

select bigfunctions.us.quantize_into_bins(-4, [0, 1, 5, 10])

select bigfunctions.europe_west1.quantize_into_bins(-4, [0, 1, 5, 10])

+-----------+
| bin_range |
+-----------+
| ]-∞, 0[   |
+-----------+


select bigfunctions.eu.quantize_into_bins(3, [0, 1, 5, 10])

select bigfunctions.us.quantize_into_bins(3, [0, 1, 5, 10])

select bigfunctions.europe_west1.quantize_into_bins(3, [0, 1, 5, 10])

+-----------+
| bin_range |
+-----------+
| [1, 5[    |
+-----------+


select bigfunctions.eu.quantize_into_bins(9, [0, 1, 5, 10])

select bigfunctions.us.quantize_into_bins(9, [0, 1, 5, 10])

select bigfunctions.europe_west1.quantize_into_bins(9, [0, 1, 5, 10])

+-----------+
| bin_range |
+-----------+
| [5, 10]   |
+-----------+


select bigfunctions.eu.quantize_into_bins(130, [0, 1, 5, 10])

select bigfunctions.us.quantize_into_bins(130, [0, 1, 5, 10])

select bigfunctions.europe_west1.quantize_into_bins(130, [0, 1, 5, 10])

+-----------+
| bin_range |
+-----------+
| ]10, +∞[  |
+-----------+

Use cases

You could use this function to categorize website session durations into bins for analysis. Let's say you have a table of website session data with a session_duration_seconds column. You want to group these sessions into duration categories like "Short (0-30s)", "Medium (31-60s)", "Long (61-180s)", and "Very Long (181s+)".

SELECT
    user_id,
    bigfunctions.us.quantize_into_bins(session_duration_seconds, [0, 30, 60, 180]) AS session_duration_category
  FROM
    `your_project.your_dataset.your_session_table`

This query would add a session_duration_category column to your results. For a session lasting 20 seconds, the category would be "]−∞, 0[", since the lower bound isn't inclusive. For 45 seconds it would be "[30, 60[", for 150 seconds it would be "[60, 180]", and for 200 seconds it would be "]180, +∞[". You can then use this new category for aggregation and reporting, such as:

SELECT
    session_duration_category,
    COUNT(*) AS num_sessions,
    AVG(pages_viewed) AS avg_pages_viewed
  FROM (
    SELECT
        user_id,
        bigfunctions.us.quantize_into_bins(session_duration_seconds, [0, 30, 60, 180]) AS session_duration_category,
        pages_viewed
      FROM
        `your_project.your_dataset.your_session_table`
  )
  GROUP BY 1
  ORDER BY 1

This would give you a summary table showing the number of sessions and average pages viewed for each session duration category. This allows you to analyze user behavior based on how long they spend on your website.

Other use cases include:

  • Customer Segmentation by Purchase Value: Categorize customers based on their total spending into different tiers (e.g., low, medium, high spenders).
  • Lead Scoring: Assign leads to different score ranges based on factors like engagement and demographics.
  • Performance Analysis: Group employees into performance categories based on metrics like sales or customer satisfaction scores.
  • Data Visualization: Create histograms or other visualizations where data needs to be binned for clarity. The output of quantize_into_bins can be used directly for grouping in chart creation.
  • Data Preprocessing for Machine Learning: Binning continuous variables can be a useful preprocessing step for certain machine learning models.

Remember to replace bigfunctions.us with the appropriate dataset for your BigQuery region.