> ## Documentation Index
> Fetch the complete documentation index at: https://docs.fiddler.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Custom Missing Values

> Learn how you can customize a model column to assign values to be treated as missing or null data in order to handle a value or token that is inserted in place of null.

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.

```python theme={null}
# 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](/developers/python-client-guides/model-onboarding/customizing-your-model-schema) on customizing your model schema before creating your Fiddler model.