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Documentation Index

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Represents a single column in a model schema with its metadata and constraints. A Column defines the structure and properties of a data column that will be used in a Fiddler model. It includes information about the column’s data type, value ranges, categorical values, binning configuration, and other metadata necessary for proper data validation and monitoring. This class is used within ModelSchema to define the complete structure of data that a model expects to receive.

Examples

Creating a numeric column:
column = Column(
    name="age",
    data_type=DataType.INTEGER,
    min=0,
    max=120
)
Creating a numeric column with custom histogram bins:
column = Column(
    name="credit_score",
    data_type=DataType.INTEGER,
    min=350,
    max=850,
    bins=[350, 450, 550, 650, 750, 850]
)
Updating bins on an existing model:
model = fdl.Model.get(id_="abc-123")
model.schema["credit_score"].bins = [350, 500, 650, 850]
model.update()
Note: Bin boundaries must span the column’s existing [min, max] range. If you are also changing the range, update min and max in the same call.
Creating a categorical column:
column = Column(
    name="category",
    data_type=DataType.CATEGORY,
    categories=["A", "B", "C"]
)
Creating a vector column:
column = Column(
    name="embedding",
    data_type=DataType.VECTOR,
    n_dimensions=128
)
Column name provided by the customer Data type of the column Min value of integer/float column Max value of integer/float column List of unique values of a categorical column Bin boundary values for integer/float column histograms. If omitted, uniform bins are auto-generated from min and max. Custom bins must be a strictly increasing list of numbers where the first value equals min and the last value equals max, with at least 2 and at most 16 boundary values (1 to 15 bins). For integer columns, all bin values must be integers. Replace the list of given values to NULL if found in the events data Number of dimensions of a vector column

class Config

smart_union = True