Meanings#
The API offers methods to retrieve the list of meanings and their definition, to create a new meaning or update an existing one.
Listing meanings#
The list of all user-defined meanings can be fetched with the list_meanings() method of the DSSClient.
client.list_meanings()
Creating a meaning#
The dataikuapi.DSSClient.create_meaning() method can be used to create new meanings.
# Creating a declarative meaning
client.create_meaning("meaning_1", "Test meaning", "DECLARATIVE",
description="Test meaning description"
)
# Creating a value list meaning
client.create_meaning("meaning_2", "Test meaning", "VALUES_LIST",
description="Test meaning",
values=["mercury","venus","earth","mars","jupiter","saturn","uranus","neptune"],
normalizationMode="LOWERCASE"
)
# Creating a value mapping meaning
client.create_meaning("meaning_3", "Test meaning", "VALUES_MAPPING",
mappings=[
{"from": "0", "to": "no" },
{"from": "1", "to": "yes" },
{"from": "2", "to": "maybe"}
]
)
# Creating a pattern meaning
client.create_meaning("meaning_4", "Test meaning", "PATTERN", pattern="[A-Z]+")
Editing a meaning#
Existing meanings can be fetched by calling the dataikuapi.DSSClient.get_meaning() method with the meaning ID. It returns a meaning handle that can be used to get or set the meaning’s definition with get_definition() and set_definition(), as follows:
meaning = client.get_meaning("meaning_1")
definition = meaning.get_definition()
definition['label'] = "New label"
definition['description'] = "New description"
meaning.set_definition(definition)
Assigning a meaning to a column#
Meanings can be assigned to columns by editing the schema of their dataset and setting the meaning field of the column to the ID of the desired meaning.
dataset = client.get_project("TEST_PROJECT").get_dataset("TEST_DATASET")
schema = dataset.get_schema()
schema['columns'][2]['meaning'] = "meaning_1"
dataset.set_schema(schema)
Reference documentation#
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A user-defined meaning on the DSS instance |