13. Create Feature List

Create a feature list¶

A feature list is an essential component in machine learning, comprising a collection of features that are used to train models. Let's compile a feature list using some of the features we've created.

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)

17:50:26 | INFO     | SDK version: 3.3.1
17:50:26 | INFO     | No catalog activated.
17:50:26 | INFO     | Using profile: tutorial
17:50:26 | INFO     | Using configuration file at: /Users/gxav/.featurebyte/config.yaml
17:50:26 | INFO     | Active profile: tutorial (https://tutorials.featurebyte.com/api/v1)
17:50:27 | INFO     | SDK version: 3.3.1
17:50:27 | INFO     | No catalog activated.
17:50:27 | INFO     | Catalog activated: Grocery Dataset SDK Tutorial
16:11:44 | INFO     | Active profile: tutorial (https://tutorials.featurebyte.com/api/v1)
16:11:44 | WARNING  | Remote SDK version (1.1.0.dev7) is different from local (1.1.0.dev1). Update local SDK to avoid unexpected behavior.
16:11:44 | INFO     | No catalog activated.
16:11:44 | INFO     | Catalog activated: Grocery Dataset Tutorial

List all features we created so far¶

In [2]:

catalog.list_features()

catalog.list_features()

Out[2]:

	id	name	dtype	readiness	online_enabled	tables	primary_tables	entities	primary_entities	created_at
0	693159012aa58fbff341f3f0	CUSTOMER_Mean_vector_of_item_product_ProductGr...	FLOAT	DRAFT	False	[GROCERYINVOICE, INVOICEITEMS, GROCERYPRODUCT]	[GROCERYINVOICE, INVOICEITEMS]	[customer]	[customer]	2025-12-04T09:50:02.683000
1	693158ca6569366adbcc0540	CUSTOMER_vs_OVERALL_item_TotalCost_across_prod...	FLOAT	DRAFT	False	[GROCERYINVOICE, INVOICEITEMS, GROCERYPRODUCT]	[INVOICEITEMS]	[customer]	[customer]	2025-12-04T09:48:03.895000
2	693158aaff830029b6b420cc	CUSTOMER_Latest_invoice_Amount_Z_Score_to_invo...	FLOAT	DRAFT	False	[GROCERYINVOICE]	[GROCERYINVOICE]	[customer]	[customer]	2025-12-04T09:47:27.658000
3	6931587c0a733a4262914a0e	CUSTOMER_Std_of_invoice_Amount_28d	FLOAT	DRAFT	False	[GROCERYINVOICE]	[GROCERYINVOICE]	[customer]	[customer]	2025-12-04T09:46:58.849000
4	6931587c0a733a4262914a0d	CUSTOMER_Std_of_invoice_Amount_14d	FLOAT	DRAFT	False	[GROCERYINVOICE]	[GROCERYINVOICE]	[customer]	[customer]	2025-12-04T09:46:58.559000
5	6931587c0a733a4262914a0c	CUSTOMER_Avg_of_invoice_Amount_28d	FLOAT	DRAFT	False	[GROCERYINVOICE]	[GROCERYINVOICE]	[customer]	[customer]	2025-12-04T09:46:58.271000
6	6931587c0a733a4262914a0b	CUSTOMER_Avg_of_invoice_Amount_14d	FLOAT	DRAFT	False	[GROCERYINVOICE]	[GROCERYINVOICE]	[customer]	[customer]	2025-12-04T09:46:57.981000
7	6931587c0a733a4262914a0a	CUSTOMER_Count_of_invoice_28d	INT	DRAFT	False	[GROCERYINVOICE]	[GROCERYINVOICE]	[customer]	[customer]	2025-12-04T09:46:57.708000
8	6931587c0a733a4262914a09	CUSTOMER_Count_of_invoice_14d	INT	DRAFT	False	[GROCERYINVOICE]	[GROCERYINVOICE]	[customer]	[customer]	2025-12-04T09:46:57.500000
9	6931587c0a733a4262914a08	CUSTOMER_Latest_invoice_Amount	FLOAT	DRAFT	False	[GROCERYINVOICE]	[GROCERYINVOICE]	[customer]	[customer]	2025-12-04T09:46:57.273000
10	6931587c0a733a4262914a04	CUSTOMER_x_PRODUCTGROUP_Sum_of_item_TotalCost_28d	FLOAT	DRAFT	False	[GROCERYINVOICE, INVOICEITEMS, GROCERYPRODUCT]	[INVOICEITEMS]	[customer, productgroup]	[customer, productgroup]	2025-12-04T09:46:56.985000
11	6931587c0a733a4262914a03	CUSTOMER_x_PRODUCTGROUP_Sum_of_item_TotalCost_14d	FLOAT	DRAFT	False	[GROCERYINVOICE, INVOICEITEMS, GROCERYPRODUCT]	[INVOICEITEMS]	[customer, productgroup]	[customer, productgroup]	2025-12-04T09:46:56.632000
12	6931587c0a733a4262914a07	CUSTOMER_x_PRODUCTGROUP_Time_Since_Latest_Time...	FLOAT	DRAFT	False	[GROCERYINVOICE, INVOICEITEMS, GROCERYPRODUCT]	[INVOICEITEMS]	[customer, productgroup]	[customer, productgroup]	2025-12-04T09:46:56.316000
13	6931585f0b7384835aa4879a	CUSTOMER_Age_band	VARCHAR	DRAFT	False	[GROCERYCUSTOMER]	[GROCERYCUSTOMER]	[customer]	[customer]	2025-12-04T09:46:19.490000
14	6931585f0b7384835aa48790	CUSTOMER_Age	INT	DRAFT	False	[GROCERYCUSTOMER]	[GROCERYCUSTOMER]	[customer]	[customer]	2025-12-04T09:46:12.777000

Get features from catalog¶

In [3]:

customer_age_band = catalog.get_feature("CUSTOMER_Age_band")
customer_latest_invoice_amount = catalog.get_feature("CUSTOMER_Latest_invoice_Amount")
customer_count_of_invoice_14d = catalog.get_feature("CUSTOMER_Count_of_invoice_14d")
customer_avg_of_invoice_amount_14d = catalog.get_feature("CUSTOMER_Avg_of_invoice_Amount_14d")
customer_std_of_invoice_amount_14d = catalog.get_feature("CUSTOMER_Std_of_invoice_Amount_14d")
customer_latest_invoice_amount_Z_score_to_invoice_amount_28d = catalog.get_feature(
    "CUSTOMER_Latest_invoice_Amount_Z_Score_to_invoice_Amount_28d"
)
customer_vs_overall_item_totalcost_across_product_productgroups_26w = catalog.get_feature(
    "CUSTOMER_vs_OVERALL_item_TotalCost_across_product_ProductGroups_26w"
)
customer_x_productgroup_sum_of_item_totalcost_14d = \
catalog.get_feature("CUSTOMER_x_PRODUCTGROUP_Sum_of_item_TotalCost_14d")
customer_x_productgroup_time_since_latest_timestamp = \
catalog.get_feature("CUSTOMER_x_PRODUCTGROUP_Time_Since_Latest_Timestamp")

customer_age_band = catalog.get_feature("CUSTOMER_Age_band") customer_latest_invoice_amount = catalog.get_feature("CUSTOMER_Latest_invoice_Amount") customer_count_of_invoice_14d = catalog.get_feature("CUSTOMER_Count_of_invoice_14d") customer_avg_of_invoice_amount_14d = catalog.get_feature("CUSTOMER_Avg_of_invoice_Amount_14d") customer_std_of_invoice_amount_14d = catalog.get_feature("CUSTOMER_Std_of_invoice_Amount_14d") customer_latest_invoice_amount_Z_score_to_invoice_amount_28d = catalog.get_feature( "CUSTOMER_Latest_invoice_Amount_Z_Score_to_invoice_Amount_28d" ) customer_vs_overall_item_totalcost_across_product_productgroups_26w = catalog.get_feature( "CUSTOMER_vs_OVERALL_item_TotalCost_across_product_ProductGroups_26w" ) customer_x_productgroup_sum_of_item_totalcost_14d = \ catalog.get_feature("CUSTOMER_x_PRODUCTGROUP_Sum_of_item_TotalCost_14d") customer_x_productgroup_time_since_latest_timestamp = \ catalog.get_feature("CUSTOMER_x_PRODUCTGROUP_Time_Since_Latest_Timestamp")

Create feature list¶

In [4]:

simple_feature_list = fb.FeatureList(
    [
        customer_age_band,
        customer_latest_invoice_amount,
        customer_count_of_invoice_14d,
        customer_avg_of_invoice_amount_14d,
        customer_std_of_invoice_amount_14d,
        customer_latest_invoice_amount_Z_score_to_invoice_amount_28d,
        customer_vs_overall_item_totalcost_across_product_productgroups_26w,
        customer_x_productgroup_sum_of_item_totalcost_14d,
        customer_x_productgroup_time_since_latest_timestamp,
    ],
    name="Customer x ProductGroup Simple FeatureList",
)

simple_feature_list = fb.FeatureList( [ customer_age_band, customer_latest_invoice_amount, customer_count_of_invoice_14d, customer_avg_of_invoice_amount_14d, customer_std_of_invoice_amount_14d, customer_latest_invoice_amount_Z_score_to_invoice_amount_28d, customer_vs_overall_item_totalcost_across_product_productgroups_26w, customer_x_productgroup_sum_of_item_totalcost_14d, customer_x_productgroup_time_since_latest_timestamp, ], name="Customer x ProductGroup Simple FeatureList", )

Preview feature list¶

In [5]:

# Check the primary entity of the feature list
simple_feature_list.primary_entity

# Check the primary entity of the feature list simple_feature_list.primary_entity

Out[5]:

[<featurebyte.api.entity.Entity at 0x107635210>
 {
   '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'
 },
 <featurebyte.api.entity.Entity at 0x108cfe6b0>
 {
   'name': 'productgroup',
   'created_at': '2025-12-04T09:42:27.759000',
   'updated_at': '2025-12-04T09:42:32.773000',
   'description': None,
   'serving_names': [
     'PRODUCTGROUP'
   ],
   'catalog_name': 'Grocery Dataset SDK Tutorial'
 }]

In [6]:

# 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 [7]:

# Preview simple_feature_list
simple_feature_list.preview(preview_table)

# Preview simple_feature_list simple_feature_list.preview(preview_table)

Out[7]:

	POINT_IN_TIME	GROCERYINVOICEITEMGUID	CUSTOMER_Age_band	CUSTOMER_Latest_invoice_Amount	CUSTOMER_Count_of_invoice_14d	CUSTOMER_Avg_of_invoice_Amount_14d	CUSTOMER_Std_of_invoice_Amount_14d	CUSTOMER_Latest_invoice_Amount_Z_Score_to_invoice_Amount_28d	CUSTOMER_vs_OVERALL_item_TotalCost_across_product_ProductGroups_26w	CUSTOMER_x_PRODUCTGROUP_Sum_of_item_TotalCost_14d	CUSTOMER_x_PRODUCTGROUP_Time_Since_Latest_Timestamp
0	2023-06-26 15:05:25	a3ac8a5e-8d9b-40b4-a835-38ccce024110	45-49	57.87	NaN	NaN	NaN	NaN	0.487491	NaN	677.198056
1	2023-03-24 09:58:13	9c7bb568-3237-4115-8e4b-ad1e2855299a	35-39	22.72	4.0	11.340000	8.430590	1.193831	0.740781	2.99	125.022500
2	2022-07-26 09:12:59	75c6652a-fb95-4d36-aad1-e9a38ad2e4b0	65-69	20.72	4.0	18.685000	10.511343	0.009317	0.774532	NaN	360.957222
3	2022-09-30 17:39:57	a4a2b3f5-69fb-403b-8868-bc360b500add	75-79	2.99	5.0	7.066000	3.461009	-1.247233	0.746415	NaN	2111.837778
4	2023-06-14 08:04:37	7b7b5ae1-4a84-449b-88f0-37bd54bc5c2c	60-64	40.54	2.0	26.695000	13.845000	1.179506	0.486674	NaN	767.908333
5	2023-03-27 14:11:18	f0f67ecc-38a0-4ddf-99a4-a3d531b00aa9	50-54	7.15	2.0	14.805000	7.655000	-1.799405	0.805892	10.00	286.953333
6	2022-12-05 17:43:17	c2dc525f-9f21-4b98-87e8-8ded5f0bdd37	30-34	19.96	3.0	31.283333	26.294727	-0.329317	0.608804	NaN	553.113611
7	2022-10-03 20:03:17	20941a03-d666-4718-851c-5016640f2c87	50-54	1.00	1.0	1.000000	0.000000	-1.000000	0.495303	NaN	NaN
8	2023-02-14 11:48:38	889a2767-dbee-4ac0-a030-f2b921f0867c	75-79	4.97	1.0	4.970000	0.000000	0.287788	0.355177	NaN	498.531111
9	2022-09-11 11:55:15	0a9b41ff-baec-4e30-b4da-b7ade2a14176	50-54	48.84	9.0	24.005556	16.348748	1.311959	0.823893	2.69	97.736667

Save feature list¶

In [8]:

# Save feature list
simple_feature_list.save()
# Add description
simple_feature_list.update_description("Simple feature list for the customer x productgroup engagement")

# Save feature list simple_feature_list.save() # Add description simple_feature_list.update_description("Simple feature list for the customer x productgroup engagement")

Done! |████████████████████████████████████████| 100% in 3.1s (0.32%/s)         
Done! |████████████████████████████████████████| 100% in 6.2s (0.16%/s)         
Loading Feature(s) |████████████████████████████████████████| 9/9 [100%] in 0.3s
Done! |████████████████████████████████████████| 100% in 6.1s (0.17%/s)         
Done! |████████████████████████████████████████| 100% in 6.1s (0.17%/s)         
Loading Feature(s) |████████████████████████████████████████| 9/9 [100%] in 0.4s

Concepts in this tutorial¶

• More on feature lists

SDK Reference for¶

• Feature List
• FeatureList.save()