Adds Iterable<DataRowSchema>.toDataFrame() overload

core/src/main/kotlin/org/jetbrains/kotlinx/dataframe/api/toDataFrame.kt

@@ -6,6 +6,7 @@ import org.jetbrains.kotlinx.dataframe.DataFrame
 import org.jetbrains.kotlinx.dataframe.DataRow
 import org.jetbrains.kotlinx.dataframe.annotations.AccessApiOverload
 import org.jetbrains.kotlinx.dataframe.annotations.DataSchema
+import org.jetbrains.kotlinx.dataframe.annotations.DisableInterpretation
 import org.jetbrains.kotlinx.dataframe.annotations.Interpretable
 import org.jetbrains.kotlinx.dataframe.annotations.Refine
 import org.jetbrains.kotlinx.dataframe.columns.BaseColumn
@@ -31,6 +32,13 @@ public inline fun <reified T> Iterable<T>.toDataFrame(): DataFrame<T> =
         properties()
     }
 
+@JvmName("dataRowSchemaIterableToDataFrame")
+public inline fun <reified T : DataRowSchema> Iterable<T>.toDataFrame(): DataFrame<T> =
+    @DisableInterpretation
+    toDataFrame {
+        properties()
+    }
+
 @Refine
 @Interpretable("toDataFrameDsl")
 public inline fun <reified T> Iterable<T>.toDataFrame(noinline body: CreateDataFrameDsl<T>.() -> Unit): DataFrame<T> =

examples/projects/dev/kotlin-spark/src/main/kotlin/org/jetbrains/kotlinx/dataframe/examples/kotlinSpark/typedDataset.kt

@@ -5,6 +5,7 @@ package org.jetbrains.kotlinx.dataframe.examples.kotlinSpark
 import org.apache.spark.sql.Dataset
 import org.jetbrains.kotlinx.dataframe.annotations.DataSchema
 import org.jetbrains.kotlinx.dataframe.api.aggregate
+import org.jetbrains.kotlinx.dataframe.api.cast
 import org.jetbrains.kotlinx.dataframe.api.groupBy
 import org.jetbrains.kotlinx.dataframe.api.max
 import org.jetbrains.kotlinx.dataframe.api.mean
@@ -13,7 +14,7 @@ import org.jetbrains.kotlinx.dataframe.api.print
 import org.jetbrains.kotlinx.dataframe.api.schema
 import org.jetbrains.kotlinx.dataframe.api.std
 import org.jetbrains.kotlinx.dataframe.api.toDataFrame
-import org.jetbrains.kotlinx.dataframe.api.toListOf
+import org.jetbrains.kotlinx.dataframe.api.toList
 import org.jetbrains.kotlinx.spark.api.withSpark
 
 /**
@@ -45,6 +46,8 @@ fun main() = withSpark {
 
     // and convert it to DataFrame via a typed List
     val dataframe = dataset.collectAsList().toDataFrame()
+        // Due to #1908, we need to remove the platform type (`!`) by casting
+        .cast<Person>()
     dataframe.schema().print()
     dataframe.print(columnTypes = true, borders = true)
 
@@ -59,10 +62,11 @@ fun main() = withSpark {
 
     ageStats.print(columnTypes = true, borders = true)
 
-    // and when we want to convert a DataFrame back to Spark, we can do the same trick via a typed List
-    // Using the compiler plugin, it's important to specify the target data class explicitly!
-    // The local compiler-plugin type is not a data class that can be instantiated.
-    val sparkDatasetAgain = dataframe.toListOf<Person>().toDS()
+    // When we want to convert a DataFrame back to Spark, we can do the same trick via a typed List.
+    // Using the compiler plugin, it's important to check the subtype of the dataframe is a
+    // data class that can be instantiated. In this case, `dataframe: DataFrame<Person>`,
+    // so `toList()` will work.
+    val sparkDatasetAgain = dataframe.toList().toDS()
     sparkDatasetAgain.printSchema()
     sparkDatasetAgain.show()
 }

examples/projects/dev/kotlin-spark/src/main/kotlin/org/jetbrains/kotlinx/dataframe/examples/spark/typedDataset.kt

@@ -90,13 +90,11 @@ fun main() {
 /** Creates a [bean encoder][Encoders.bean] for the given [T] instance. */
 inline fun <reified T : Serializable> beanEncoderOf(): Encoder<T> = Encoders.bean(T::class.java)
 
-@DataSchema
 data class Name
     @JvmOverloads
     constructor(var firstName: String = "", var lastName: String = "") : Serializable
 
-// The @DataSchema annotation is optional for this specific example, but is generally recommended
-@DataSchema
+// The @DataSchema annotation is optional
 data class Person
     @JvmOverloads
     constructor(

samples/src/test/kotlin/org/jetbrains/kotlinx/dataframe/tests/toDataFrame.kt

@@ -0,0 +1,55 @@
+package org.jetbrains.kotlinx.dataframe.tests
+
+import io.kotest.assertions.throwables.shouldThrow
+import io.kotest.matchers.shouldBe
+import io.kotest.matchers.string.shouldContain
+import org.jetbrains.kotlinx.dataframe.DataFrame
+import org.jetbrains.kotlinx.dataframe.annotations.ColumnName
+import org.jetbrains.kotlinx.dataframe.annotations.DataSchema
+import org.jetbrains.kotlinx.dataframe.api.toDataFrame
+import org.jetbrains.kotlinx.dataframe.api.toList
+import org.jetbrains.kotlinx.dataframe.api.toListOf
+import org.junit.Test
+
+class ToDataFrameTests {
+
+    data class SimplePerson(val name: String, val age: Int)
+
+    @DataSchema
+    data class DataSchemaPerson(val name: String, val age: Int)
+
+    // Test case for #1880
+    @Test
+    fun `simple Iterable to DataFrame`() {
+        val people = listOf(
+            SimplePerson("John", 25),
+            SimplePerson("Jane", 30),
+        )
+
+        // DataFrame<SimplePerson_XX>
+        val df = people.toDataFrame()
+        df.name
+        df.age
+
+        shouldThrow<IllegalArgumentException> { df.toList() }
+            .message shouldContain "is not a data class. `toList` is supported only for data classes."
+
+        df.toListOf<SimplePerson>() shouldBe people
+    }
+
+    // Test case for #1880
+    @Test
+    fun `DataSchema Iterable to DataFrame`() {
+        val people = listOf(
+            DataSchemaPerson("John", 25),
+            DataSchemaPerson("Jane", 30),
+        )
+
+        // DataFrame<DataSchemaPerson>
+        val df: DataFrame<DataSchemaPerson> = people.toDataFrame()
+        df.name
+        df.age
+
+        df.toList() shouldBe people
+    }
+}