65 lines
1.6 KiB
Markdown
Vendored
65 lines
1.6 KiB
Markdown
Vendored
[//]: # (title: cast)
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<!---IMPORT org.jetbrains.kotlinx.dataframe.samples.api.Modify-->
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Changes the type argument of the [`DataFrame`](DataFrame.md) instance without changing its contents.
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```kotlin
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cast<T>(verify = false)
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```
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Related operations: [](adjustSchema.md)
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**Parameters:**
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* `verify: Boolean = false` —
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when `true`, the function throws an exception if the [`DataFrame`](DataFrame.md) instance doesn't match the given schema.
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Otherwise, it just changes the format type without actual data checks.
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Use this operation to change the formal type of a [`DataFrame`](DataFrame.md) instance
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to match the expected schema and enable generated [extension properties](extensionPropertiesApi.md) for it.
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```kotlin
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@DataSchema
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interface Person {
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val age: Int
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val name: String
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}
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df.cast<Person>()
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```
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To convert [`DataFrame`](DataFrame.md) columns to match given schema, use [`convertTo`](convertTo.md) operation.
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**Reusing implicitly generated schema**
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```kotlin
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castTo<T>(df: DataFrame<T>)
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```
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In notebooks, dataframe types are implicitly generated.
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This type can be referred to, but its name will change whenever you re-execute cells.
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Here how you can do it in a more robust way:
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<!---FUN castToGenerateSchema-->
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```kotlin
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val sample = DataFrame.readJson("sample.json")
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```
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<!---END-->
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<!---FUN castTo-->
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```kotlin
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for (file in files) {
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// df here is expected to have the same structure as sample
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val df = DataFrame.readJson(file).castTo(sample)
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val count = df.count { perf > 10.0 }
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println("$file: $count")
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}
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```
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<!---END-->
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