Custom Missing Values

Your data may contain missing values represented in nonstandard ways instead of null or NaN. For example, an upstream system might use "-1.0" or "-999" in a Float column to indicate missing data. Fiddler lets you specify custom missing value representations for each column when defining your model schema.

Customize Missing Data Values in Your Schema

To specify which values should be treated as nulls when publishing data to Fiddler:

You can modify your ModelSchema object just before onboarding your model to include details about which values should be replaced with nulls when publishing data to Fiddler.

# Assume an instantiated Fiddler Model:
# model = Model.from_data(...)

# Modify your ModelSchema object before calling model.create()
model.schema['my_column'].replace_with_nulls = [
  '-1.0',
  '-999'
]

This configuration tells Fiddler to automatically consider these values as null when processing your data and generating data integrity metrics.

For more information, see our in-depth guide on customizing your model schema before creating your Fiddler model.