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quantize_into_fixed_width_bins

Call or Deploy quantize_into_fixed_width_bins ?

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

  • This quantize_into_fixed_width_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. 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

quantize_into_fixed_width_bins(value, min_bound, max_bound, nb_bins)

Description

Get the bin_range in which belongs value with bins defined so that there are nb_bins bins of same width between min_bound and max_bound plus a bin ]-∞, min_bound[ and a bin ]max_bound, +∞[

Examples

select bigfunctions.eu.quantize_into_fixed_width_bins(-4, 0, 100, 10)
select bigfunctions.us.quantize_into_fixed_width_bins(-4, 0, 100, 10)
select bigfunctions.europe_west1.quantize_into_fixed_width_bins(-4, 0, 100, 10)
+-----------+
| bin_range |
+-----------+
| ]-∞, 0[   |
+-----------+

select bigfunctions.eu.quantize_into_fixed_width_bins(5, 0, 100, 10)
select bigfunctions.us.quantize_into_fixed_width_bins(5, 0, 100, 10)
select bigfunctions.europe_west1.quantize_into_fixed_width_bins(5, 0, 100, 10)
+-----------+
| bin_range |
+-----------+
| [0, 10[   |
+-----------+

select bigfunctions.eu.quantize_into_fixed_width_bins(97, 0, 100, 10)
select bigfunctions.us.quantize_into_fixed_width_bins(97, 0, 100, 10)
select bigfunctions.europe_west1.quantize_into_fixed_width_bins(97, 0, 100, 10)
+-----------+
| bin_range |
+-----------+
| [90, 100] |
+-----------+

select bigfunctions.eu.quantize_into_fixed_width_bins(130, 0, 100, 10)
select bigfunctions.us.quantize_into_fixed_width_bins(130, 0, 100, 10)
select bigfunctions.europe_west1.quantize_into_fixed_width_bins(130, 0, 100, 10)
+-----------+
| bin_range |
+-----------+
| ]100, +∞[ |
+-----------+

Need help using quantize_into_fixed_width_bins?

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

Found a bug using quantize_into_fixed_width_bins?

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

Use Case: Customer Segmentation based on Purchase Value

An e-commerce company wants to segment its customers based on their total purchase value over the last year. They want to create 5 segments of equal width, ranging from the lowest purchase value to the highest.

Implementation with quantize_into_fixed_width_bins:

  1. Determine the minimum and maximum purchase values:

    SELECT MIN(total_purchase_value) AS min_value, MAX(total_purchase_value) AS max_value
    FROM customer_purchases;
    
    Let's assume min_value is 0 and max_value is 1000.

  2. Apply the quantize_into_fixed_width_bins function:

    SELECT customer_id, total_purchase_value,
           bigfunctions.us.quantize_into_fixed_width_bins(total_purchase_value, 0, 1000, 5) AS purchase_segment
    FROM customer_purchases;
    
    This will categorize each customer into one of the following segments:

  3. ]-∞, 0[ (unlikely in this case, as purchase value should be non-negative)

  4. [0, 200[
  5. [200, 400[
  6. [400, 600[
  7. [600, 800[
  8. [800, 1000]
  9. ]1000, +∞[

  10. Analyze and utilize the segments: The company can now use these segments for targeted marketing campaigns, personalized recommendations, and other business strategies. For example, customers in the highest segment ([800, 1000] and ]1000, +∞[) could receive exclusive offers or loyalty programs.

Benefits of using quantize_into_fixed_width_bins:

  • Simplified segmentation: Easily creates equally sized bins, making it straightforward to understand and interpret the segments.
  • Flexibility: The number of bins and the range can be adjusted to suit different segmentation needs.
  • Efficiency: The function handles the binning logic within the SQL query, eliminating the need for complex pre-processing steps.

Other Use Cases:

  • Categorizing website traffic: Segmenting users based on time spent on site, number of pages viewed, or other metrics.
  • Analyzing sensor data: Grouping sensor readings into bins for easier analysis and visualization.
  • Performance monitoring: Classifying response times or error rates into different severity levels.
  • Creating histograms: Generating histograms of data distributions using the binned values.

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