Transformers

A transformer function reads data from one or more tables in the Tabsdata server, transforms the data, then writes the transformed data to tables in the server.

Transformers in Action

collection · world

collection · insights · data product

Table

country

//world

Committed

Table

city

//world

Committed

Transformer

find_big_cities

Table

big_cities

//insights

No versions yet

Transformer Code Anatomy

find_big_cities.py

1from tabsdatak.api import transformer, TableFrameSpec
2import tabsdatak.api.tableframe as tdf
3 
4 
1@transformer(
2    input_tables=["world/country", "world/city"],
3    output_tables=["big_cities"],
8)
45def find_big_cities(country: TableFrameSpec, city: TableFrameSpec) -> tuple[TableFrameSpec]:
6    countries = country.rename({"name": "country_name"}).drop(
11        ["population", "continent"]
12    )
13    joined = city.join(countries, left_on="country_code", right_on="code")
7    return (joined.filter(tdf.col("population") > 3_000_000),)

• Defines the input and output Tables for the Transformer.

  • Tabsdata Tables to read. Use collection/table to reference a Table in another Collection.

  • Tables where the transformed data is written. These belong to the Transformer's Collection.

• Reads the country and city data, joins it, and returns cities with a population above three million.

  • The arguments receive the data from input_tables in the order listed. An argument is None if the requested Table version does not exist.

  • Renames the country name column, removes unused columns, and joins the country and city data.

  • The returned data is written to the Tables listed in output_tables, in that order. Returning None for a Table leaves its current version unchanged.

For step-by-step guidance on building a transformer, see Transformers under How-to Guides.