[//]: # (title: cast)
<!---IMPORT org.jetbrains.kotlinx.dataframe.samples.api.Modify-->

Changes the type argument of the [`DataFrame`](DataFrame.md) instance without changing its contents.

```kotlin
cast<T>(verify = false)
```

Related operations: [](adjustSchema.md)

**Parameters:**
* `verify: Boolean = false` —
  when `true`, the function throws an exception if the [`DataFrame`](DataFrame.md) instance doesn't match the given schema. 
Otherwise, it just changes the format type without actual data checks.

Use this operation to change the formal type of a [`DataFrame`](DataFrame.md) instance
to match the expected schema and enable generated [extension properties](extensionPropertiesApi.md) for it.

```kotlin
@DataSchema
interface Person {
    val age: Int
    val name: String
}

df.cast<Person>()
```

To convert [`DataFrame`](DataFrame.md) columns to match given schema, use [`convertTo`](convertTo.md) operation.

**Reusing implicitly generated schema**

```kotlin
castTo<T>(df: DataFrame<T>)
```

In notebooks, dataframe types are implicitly generated.

![Implicitly generated schema](implicitlyGeneratedSchema.png)

This type can be referred to, but its name will change whenever you re-execute cells.
Here how you can do it in a more robust way:

<!---FUN castToGenerateSchema-->

```kotlin
val sample = DataFrame.readJson("sample.json")
```

<!---END-->

<!---FUN castTo-->

```kotlin
for (file in files) {
    // df here is expected to have the same structure as sample
    val df = DataFrame.readJson(file).castTo(sample)
    val count = df.count { perf > 10.0 }
    println("$file: $count")
}
```

<!---END-->