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]:

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)

17:47:21 | INFO     | SDK version: 3.3.1
17:47:21 | INFO     | No catalog activated.
17:47:21 | INFO     | Using profile: tutorial
17:47:21 | INFO     | Using configuration file at: /Users/gxav/.featurebyte/config.yaml
17:47:21 | INFO     | Active profile: tutorial (https://tutorials.featurebyte.com/api/v1)
17:47:21 | INFO     | SDK version: 3.3.1
17:47:21 | INFO     | No catalog activated.
17:47:21 | INFO     | Catalog activated: Grocery Dataset SDK Tutorial
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]:

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]:

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]:

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 0x105fc7290>
 {
   'name': 'customer',
   'created_at': '2025-12-04T09:42:26.412000',
   'updated_at': '2025-12-04T09:42:31.346000',
   'description': None,
   'serving_names': [
     'GROCERYCUSTOMERGUID'
   ],
   'catalog_name': 'Grocery Dataset SDK Tutorial'
 }]

In [5]:

# 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]:

# 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	2023-03-24 09:58:13	9c7bb568-3237-4115-8e4b-ad1e2855299a	1.193831
1	2023-06-26 15:05:25	a3ac8a5e-8d9b-40b4-a835-38ccce024110	NaN
2	2022-07-26 09:12:59	75c6652a-fb95-4d36-aad1-e9a38ad2e4b0	0.009317
3	2022-09-30 17:39:57	a4a2b3f5-69fb-403b-8868-bc360b500add	-1.247233
4	2023-06-14 08:04:37	7b7b5ae1-4a84-449b-88f0-37bd54bc5c2c	1.179506
5	2022-10-03 20:03:17	20941a03-d666-4718-851c-5016640f2c87	-1.000000
6	2022-12-05 17:43:17	c2dc525f-9f21-4b98-87e8-8ded5f0bdd37	-0.329317
7	2023-03-27 14:11:18	f0f67ecc-38a0-4ddf-99a4-a3d531b00aa9	-1.799405
8	2022-09-11 11:55:15	0a9b41ff-baec-4e30-b4da-b7ade2a14176	1.311959
9	2023-02-14 11:48:38	889a2767-dbee-4ac0-a030-f2b921f0867c	0.287788

Save feature¶

In [7]:

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 6.2s (0.16%/s)         
Done! |████████████████████████████████████████| 100% in 6.1s (0.17%/s)         

Add description¶

In [8]:

# 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" )

Concepts in this tutorial¶

• Feature Transforms

SDK reference for¶

• Catalog.get_feature()