10. Derive Features from other Features
Derive feature from other features¶
Features can also be created based on other features.
In this tutorial we will create a feature that measures the Z-Score of the Latest invoice Amount for the customer compared to that customer's invoices over a 28d period. This feature is derived from 3 features we created previously: CUSTOMER_Latest_invoice_Amount, CUSTOMER_Avg_of_invoice_Amount_28d and CUSTOMER_Std_of_invoice_Amount_28d.
In [1]:
Copied!
import featurebyte as fb
# Set your profile to the tutorial environment
fb.use_profile("tutorial")
catalog_name = "Grocery Dataset SDK Tutorial"
catalog = fb.Catalog.activate(catalog_name)
import featurebyte as fb
# Set your profile to the tutorial environment
fb.use_profile("tutorial")
catalog_name = "Grocery Dataset SDK Tutorial"
catalog = fb.Catalog.activate(catalog_name)
11:13:45 | INFO | SDK version: 3.2.0.dev66 INFO :featurebyte:SDK version: 3.2.0.dev66 11:13:45 | INFO | No catalog activated. INFO :featurebyte:No catalog activated. 11:13:45 | INFO | Using profile: staging INFO :featurebyte:Using profile: staging 11:13:45 | INFO | Using configuration file at: /Users/gxav/.featurebyte/config.yaml INFO :featurebyte:Using configuration file at: /Users/gxav/.featurebyte/config.yaml 11:13:45 | INFO | Active profile: staging (https://staging.featurebyte.com/api/v1) INFO :featurebyte:Active profile: staging (https://staging.featurebyte.com/api/v1) 11:13:45 | INFO | SDK version: 3.2.0.dev66 INFO :featurebyte:SDK version: 3.2.0.dev66 11:13:45 | INFO | No catalog activated. INFO :featurebyte:No catalog activated. 11:13:45 | INFO | Catalog activated: Grocery Dataset SDK Tutorial INFO :featurebyte.api.catalog:Catalog activated: Grocery Dataset SDK Tutorial 16:08:27 | INFO | Using configuration file at: /Users/gxav/.featurebyte/config.yaml 16:08:27 | INFO | Active profile: tutorial (https://tutorials.featurebyte.com/api/v1) 16:08:27 | WARNING | Remote SDK version (1.1.0.dev7) is different from local (1.1.0.dev1). Update local SDK to avoid unexpected behavior. 16:08:27 | INFO | No catalog activated. 16:08:27 | INFO | Catalog activated: Grocery Dataset Tutorial
Get features we previously created and saved¶
In [2]:
Copied!
customer_latest_invoice_amount = catalog.get_feature("CUSTOMER_Latest_invoice_Amount")
customer_avg_of_invoice_amount_28d = catalog.get_feature("CUSTOMER_Avg_of_invoice_Amount_28d")
customer_std_of_invoice_amount_28d = catalog.get_feature("CUSTOMER_Std_of_invoice_Amount_28d")
customer_latest_invoice_amount = catalog.get_feature("CUSTOMER_Latest_invoice_Amount")
customer_avg_of_invoice_amount_28d = catalog.get_feature("CUSTOMER_Avg_of_invoice_Amount_28d")
customer_std_of_invoice_amount_28d = catalog.get_feature("CUSTOMER_Std_of_invoice_Amount_28d")
Derive a z-score feature from saved features¶
In [3]:
Copied!
customer_latest_invoice_amount_Z_score_to_invoice_amount_28d = (
customer_latest_invoice_amount
- customer_avg_of_invoice_amount_28d
) / customer_std_of_invoice_amount_28d
# Give a name to new feature
customer_latest_invoice_amount_Z_score_to_invoice_amount_28d.name = \
"CUSTOMER_Latest_invoice_Amount_Z_Score_to_invoice_Amount_28d"
customer_latest_invoice_amount_Z_score_to_invoice_amount_28d = (
customer_latest_invoice_amount
- customer_avg_of_invoice_amount_28d
) / customer_std_of_invoice_amount_28d
# Give a name to new feature
customer_latest_invoice_amount_Z_score_to_invoice_amount_28d.name = \
"CUSTOMER_Latest_invoice_Amount_Z_Score_to_invoice_Amount_28d"
Preview feature¶
In [4]:
Copied!
customer_latest_invoice_amount_Z_score_to_invoice_amount_28d.primary_entity
customer_latest_invoice_amount_Z_score_to_invoice_amount_28d.primary_entity
Out[4]:
[<featurebyte.api.entity.Entity at 0x177cf47c0>
{
'name': 'customer',
'created_at': '2025-10-15T03:06:58.482000',
'updated_at': '2025-10-15T03:07:01.153000',
'description': None,
'serving_names': [
'GROCERYCUSTOMERGUID'
],
'catalog_name': 'Grocery Dataset SDK Tutorial'
}]
In [5]:
Copied!
# Get observation table: 'Preview Table with 10 items'
preview_table = catalog.get_observation_table("Preview Table with 10 items")
# Get observation table: 'Preview Table with 10 items'
preview_table = catalog.get_observation_table("Preview Table with 10 items")
In [6]:
Copied!
# Preview CUSTOMER_Age
customer_latest_invoice_amount_Z_score_to_invoice_amount_28d.preview(preview_table)
# Preview CUSTOMER_Age
customer_latest_invoice_amount_Z_score_to_invoice_amount_28d.preview(preview_table)
Out[6]:
| POINT_IN_TIME | GROCERYINVOICEITEMGUID | CUSTOMER_Latest_invoice_Amount_Z_Score_to_invoice_Amount_28d | |
|---|---|---|---|
| 0 | 2023-04-07 13:41:35 | f017e72e-1645-4fbe-988e-a9c09920c506 | -1.230802 |
| 1 | 2023-01-08 15:40:02 | 879de04d-36ef-49a3-b1f7-a96495100dbe | -1.031903 |
| 2 | 2022-07-14 18:12:49 | a3245668-aeba-4259-87e1-1b99a1e7391c | NaN |
| 3 | 2022-12-01 13:28:57 | 600a7549-9f3e-42f9-8408-e3996d6c4750 | -1.184455 |
| 4 | 2022-10-03 12:02:23 | f25a7864-4e0d-43bb-8191-0ffdf56b5a21 | -0.422419 |
| 5 | 2022-12-12 10:27:30 | 9526bbd7-3b85-4bd5-99d7-2eecda85ada2 | -0.342691 |
| 6 | 2023-03-18 09:43:40 | 7ec0da36-b85f-47cf-873e-461b5c2b1cbf | 0.413984 |
| 7 | 2022-09-12 16:29:49 | f7de9fec-9e01-4478-8b2e-5a17427a53c1 | 1.963783 |
| 8 | 2022-10-30 09:08:11 | 5ee681b9-583c-4968-88bb-993704a6c54e | -1.000000 |
| 9 | 2023-05-14 15:41:46 | e904b0bc-b342-4491-a7d9-216489b6ac01 | 1.000000 |
Save feature¶
In [7]:
Copied!
customer_latest_invoice_amount_Z_score_to_invoice_amount_28d.save()
customer_latest_invoice_amount_Z_score_to_invoice_amount_28d.save()
Done! |████████████████████████████████████████| 100% in 6.1s (0.17%/s) Done! |████████████████████████████████████████| 100% in 6.1s (0.17%/s)
Add description¶
In [8]:
Copied!
# Add description
customer_latest_invoice_amount_Z_score_to_invoice_amount_28d.update_description(
"Z-Score of the Latest invoice Amount for the customer compared to customer invoices over a 28d period"
)
# Add description
customer_latest_invoice_amount_Z_score_to_invoice_amount_28d.update_description(
"Z-Score of the Latest invoice Amount for the customer compared to customer invoices over a 28d period"
)