# ModelSchema

Defines the complete schema structure for a model's input data.

ModelSchema contains the specification of all columns that a model expects to receive, including their data types, constraints, and metadata. This schema is used by Fiddler for data validation, monitoring, and analysis purposes.

The schema acts as a contract between your model and Fiddler, ensuring that incoming data conforms to expected formats and enabling proper drift detection, data quality monitoring, and other features.

## Examples

Creating a model schema:

```python
schema = ModelSchema(
    columns=[
        Column(name="age", data_type=DataType.INTEGER, min=0, max=120),
        Column(name="income", data_type=DataType.FLOAT, min=0),
        Column(name="category", data_type=DataType.CATEGORY,

        categories=["A", "B", "C"])
    ]
)
```

Accessing columns by name:

```python
age_column = schema["age"]
print(age_column.data_type)
```

Adding a new column:

```python
new_column = Column(name="score", data_type=DataType.FLOAT)
schema["score"] = new_column
```

Removing a column:

```python
del schema["age"]
```

Schema version

List of columns

## **getitem**(item)

Get column by name

**Return type:** *Column*

## **setitem**(key, value)

Set column by name

**Return type:** None

## **delitem**(key)

Delete column by name

**Return type:** None