4. Update descriptions to tables

(Optional) Updating descriptions to tables and columns¶

Table and column descriptions are automatically fetched from your Data Warehouse when they are available. If these descriptions are missing or incomplete, you have the option to edit and update them.

While not mandatory, updating concise descriptions to tables and columns can be immensely beneficial if you are using FeatureByte Enterprise. These annotations assist FeatureByte's feature ideation engine in generating insightful features.

Much like a data scientist, FeatureByte does its best to grasp the significance and purpose of various tables and columns, discerning their types and more. Based on this understanding, it suggests pertinent aggregations and feature combinations. While FeatureByte can operate effectively without these descriptions, having them certainly enhances the quality of its recommendations.

In [1]:

import featurebyte as fb

# Set your profile to the tutorial environment
fb.use_profile("tutorial")

catalog_name = "Grocery Dataset 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 Tutorial" catalog = fb.Catalog.activate(catalog_name)

15:28:31 | INFO     | SDK version: 1.0.2.dev46
15:28:31 | INFO     | No catalog activated.
15:28:31 | INFO     | Using profile: tutorial
15:28:31 | INFO     | Using configuration file at: /Users/gxav/.featurebyte/config.yaml
15:28:31 | INFO     | Active profile: tutorial (https://tutorials.featurebyte.com/api/v1)
15:28:31 | INFO     | SDK version: 1.0.2.dev46
15:28:31 | INFO     | No catalog activated.
15:28:32 | INFO     | Catalog activated: Grocery Dataset Tutorial

Get tables from the catalog first:

In [2]:

catalog.list_tables()

catalog.list_tables()

Out[2]:

	id	name	type	status	entities	created_at
0	662b577aaa13c89fa14554e3	GROCERYPRODUCT	dimension_table	PUBLIC_DRAFT	[product, productgroup]	2024-04-26T07:27:55.127000
1	662b5778aa13c89fa14554e2	INVOICEITEMS	item_table	PUBLIC_DRAFT	[item, invoice, product]	2024-04-26T07:27:52.998000
2	662b5775aa13c89fa14554e1	GROCERYINVOICE	event_table	PUBLIC_DRAFT	[invoice, customer]	2024-04-26T07:27:50.409000
3	662b5773aa13c89fa14554e0	GROCERYCUSTOMER	scd_table	PUBLIC_DRAFT	[customer, frenchstate]	2024-04-26T07:27:48.329000

In [3]:

customer_table = catalog.get_table("GROCERYCUSTOMER")
invoice_table = catalog.get_table("GROCERYINVOICE")
items_table = catalog.get_table("INVOICEITEMS")
product_table = catalog.get_table("GROCERYPRODUCT")

customer_table = catalog.get_table("GROCERYCUSTOMER") invoice_table = catalog.get_table("GROCERYINVOICE") items_table = catalog.get_table("INVOICEITEMS") product_table = catalog.get_table("GROCERYPRODUCT")

Let's discover the current descriptions of tables:¶

In [4]:

customer_table.description

customer_table.description

Out[4]:

'Customer details, including their name, address, and date of birth.'

In [5]:

invoice_table.description

invoice_table.description

Out[5]:

'Grocery invoice details, containing the timestamp and the total amount of the invoice.'

In [6]:

items_table.description

items_table.description

Out[6]:

'The grocery item details within each invoice, including the quantity, total cost, discount applied, and product ID.'

In [7]:

product_table.description

product_table.description

Out[7]:

'The product group description for each grocery product.'

Let's update descriptions of one table:¶

In [8]:

customer_table.update_description('Customer details')

customer_table.update_description('Customer details')

In [9]:

customer_table.description

customer_table.description

Out[9]:

'Customer details'

In [10]:

customer_table.update_description('Customer details, including their name, address, and date of birth.')

customer_table.update_description('Customer details, including their name, address, and date of birth.')

Let's discover the current descriptions of columns for each table:¶

You can either display all columns together

In [11]:

import pandas as pd
pd.DataFrame(customer_table.info(verbose=True)["columns_info"])

import pandas as pd pd.DataFrame(customer_table.info(verbose=True)["columns_info"])

Out[11]:

	name	dtype	entity	semantic	critical_data_info	description
0	RowID	VARCHAR	None	scd_surrogate_key_id	None	Unique identifier of each row in the table, in...
1	GroceryCustomerGuid	VARCHAR	customer	scd_natural_key_id	None	Unique identifier for each customer, in GUID f...
2	ValidFrom	TIMESTAMP	None	scd_effective_timestamp	None	GMT timestamp of when this version of a custom...
3	Gender	VARCHAR	None	None	None	The customer's gender. Can only have values ma...
4	Title	VARCHAR	None	None	None	The customer's title. Can only have values Dr....
5	GivenName	VARCHAR	None	None	None	The customer's given name.
6	MiddleInitial	VARCHAR	None	None	None	The first letter of the customer's middle name.
7	Surname	VARCHAR	None	None	None	The customer's family or surname.
8	StreetAddress	VARCHAR	None	None	None	The customer address's building number and str...
9	City	VARCHAR	None	None	None	The city name of the customer's address.
10	State	VARCHAR	frenchstate	None	None	The state name of the customer's address.
11	PostalCode	VARCHAR	None	None	None	The postal code of the customer's address. Con...
12	BrowserUserAgent	VARCHAR	None	None	None	The user agent details of the customer's inter...
13	DateOfBirth	TIMESTAMP	None	None	None	The customer's date of birth.
14	Latitude	FLOAT	None	None	None	The latitude of the customer's address.
15	Longitude	FLOAT	None	None	None	The longitude of the customer's address.
16	record_available_at	TIMESTAMP	None	record_creation_timestamp	None	A timestamp for when this row was added to the...
17	CurrentRecord	BOOL	None	scd_current_flag	None	Flags whether this version of the customer's d...

Or display each column one by one

In [12]:

for column in customer_table.columns:
    print(f"{column}: {customer_table[column].description}")

for column in customer_table.columns: print(f"{column}: {customer_table[column].description}")

RowID: Unique identifier of each row in the table, in GUID format. Uniquely identifies each customer and version combination.
GroceryCustomerGuid: Unique identifier for each customer, in GUID format.
ValidFrom: GMT timestamp of when this version of a customer's details becomes valid or live.
Gender: The customer's gender. Can only have values male or female.
Title: The customer's title. Can only have values Dr. Mr. Mrs. or Ms.
GivenName: The customer's given name.
MiddleInitial: The first letter of the customer's middle name.
Surname: The customer's family or surname.
StreetAddress: The customer address's building number and street name.
City: The city name of the customer's address.
State: The state name of the customer's address.
PostalCode: The postal code of the customer's address. Contains only digits but is a categorical variable.
BrowserUserAgent: The user agent details of the customer's internet browser, including the operating system name and version, and the browser name and version.
DateOfBirth: The customer's date of birth.
Latitude: The latitude of the customer's address.
Longitude: The longitude of the customer's address.
record_available_at: A timestamp for when this row was added to the database.
CurrentRecord: Flags whether this version of the customer's details is the latest known version.

If the description is incorrect or incomplete, you can edit it

In [13]:

# By using the table method: update_column_description
customer_table.update_column_description(
    "RowID",
    "Unique identifier of each row in the table, in GUID format. Uniquely identifies each customer and version combination."
)

# By using the table method: update_column_description customer_table.update_column_description( "RowID", "Unique identifier of each row in the table, in GUID format. Uniquely identifies each customer and version combination." )

In [14]:

# Or by using the column method: update_description
customer_table.RowID.update_description(
    "Unique identifier of each row in the table, in GUID format. Uniquely identifies each customer and version combination."
)

# Or by using the column method: update_description customer_table.RowID.update_description( "Unique identifier of each row in the table, in GUID format. Uniquely identifies each customer and version combination." )

That's it for this tutorial. Again, this is an optional step, but it can drastically improve FeatureByte's feature ideation.

SDK reference for¶

• Table.info

In [ ]: