diff --git a/dataset/src/main/scala/frameless/TypedDataset.scala b/dataset/src/main/scala/frameless/TypedDataset.scala
index 99903753e..c9f33411d 100644
--- a/dataset/src/main/scala/frameless/TypedDataset.scala
+++ b/dataset/src/main/scala/frameless/TypedDataset.scala
@@ -1199,29 +1199,56 @@ class TypedDataset[T] protected[frameless](val dataset: Dataset[T])(implicit val
     import org.apache.spark.sql.functions.{explode => sparkExplode}
 
     val trans =
-      df.withColumn(column.value.name,
-        sparkExplode(df(column.value.name))).as[Out](TypedExpressionEncoder[Out])
+      df
+        .withColumn(column.value.name, sparkExplode(df(column.value.name)))
+        .as[Out](TypedExpressionEncoder[Out])
     TypedDataset.create[Out](trans)
   }
 
-  def explodeMap[A, B, TRep <: HList, OutMod <: HList, OutModValues <: HList, Out]
+  /**
+    * Explodes a single column at a time. It only compiles if the type of column supports this operation.
+    *
+    * @example
+    *
+    * {{{
+    *   case class X(i: Int, j: Map[Int, Int])
+    *   case class Y(i: Int, j: (Int, Int))
+    *
+    *   val f: TypedDataset[X] = ???
+    *   val fNew: TypedDataset[Y] = f.explodeMap('j).as[Y]
+    * }}}
+    * @param column the column we wish to explode
+    */
+  def explodeMap[A, B, V[_, _], TRep <: HList, OutMod <: HList, OutModValues <: HList, Out]
   (column: Witness.Lt[Symbol])
   (implicit
-   i0: TypedColumn.Exists[T, column.T, Map[A, B]],
+   i0: TypedColumn.Exists[T, column.T, V[A, B]],
    i1: TypedEncoder[A],
    i2: TypedEncoder[B],
    i3: LabelledGeneric.Aux[T, TRep],
-   i4: Modifier.Aux[TRep, column.T, Map[A,B], Tuple2[A,B], OutMod],
+   i4: Modifier.Aux[TRep, column.T, V[A,B], (A, B), OutMod],
    i5: Values.Aux[OutMod, OutModValues],
    i6: Tupler.Aux[OutModValues, Out],
    i7: TypedEncoder[Out]
   ): TypedDataset[Out] = {
     val df = dataset.toDF()
-    import org.apache.spark.sql.functions.{explode => sparkExplode}
-
+    import org.apache.spark.sql.functions.{explode => sparkExplode, struct => sparkStruct, col => sparkCol}
+
+    // preserve the original list of columns
+    val columns = df.columns.toSeq.map(sparkCol)
+    // select all columns, all original columns and [key, value] columns appeared after the map explode
+    // .withColumn(column.value.name, sparkExplode(df(column.value.name))) in this case would not work
+    // since the map explode produces two columns
+    val exploded = df.select(sparkCol("*"), sparkExplode(df(column.value.name)))
     val trans =
-      df.withColumn(column.value.name,
-        sparkExplode(df(column.value.name))).as[Out](TypedExpressionEncoder[Out])
+      exploded
+        // map explode explodes it into [key, value] columns
+        // the only way to put it into a column is to create a struct
+        // TODO: handle org.apache.spark.sql.AnalysisException: Reference 'key / value' is ambiguous, could be: key / value, key / value
+        .withColumn(column.value.name, sparkStruct(exploded("key"), exploded("value")))
+        // selecting only original columns, we don't need [key, value] columns left in the DataFrame after the map explode
+        .select(columns: _*)
+        .as[Out](TypedExpressionEncoder[Out])
     TypedDataset.create[Out](trans)
   }
 
diff --git a/dataset/src/test/scala/frameless/ExplodeTests.scala b/dataset/src/test/scala/frameless/ExplodeTests.scala
index 90a8ec05f..205b49038 100644
--- a/dataset/src/test/scala/frameless/ExplodeTests.scala
+++ b/dataset/src/test/scala/frameless/ExplodeTests.scala
@@ -7,7 +7,6 @@ import org.scalacheck.Prop._
 
 import scala.reflect.ClassTag
 
-
 class ExplodeTests extends TypedDatasetSuite {
   test("simple explode test") {
     val ds = TypedDataset.create(Seq((1,Array(1,2))))
@@ -49,18 +48,33 @@ class ExplodeTests extends TypedDatasetSuite {
     check(forAll(prop[String] _))
   }
 
-  test("explode on Maps") {
-    def prop[A: TypedEncoder: ClassTag](xs: List[X1[Map[A, A]]]): Prop = {
+  test("explode on maps") {
+    def prop[A: TypedEncoder: ClassTag, B: TypedEncoder: ClassTag](xs: List[X1[Map[A, B]]]): Prop = {
       val tds = TypedDataset.create(xs)
 
       val framelessResults = tds.explodeMap('a).collect().run().toVector
-      val scalaResults = xs.flatMap(_.a.toList).map(t => Tuple2(t._1, t._2)).toVector
+      val scalaResults = xs.flatMap(_.a.toList).map(t => Tuple1(Tuple2(t._1, t._2))).toVector
 
       framelessResults ?= scalaResults
     }
 
-    check(forAll(prop[Long] _))
-    check(forAll(prop[Int] _))
-    check(forAll(prop[String] _))
+    check(forAll(prop[Long, String] _))
+    check(forAll(prop[Int, Long] _))
+    check(forAll(prop[String, Int] _))
+  }
+
+  test("explode on maps preserving other columns") {
+    def prop[K: TypedEncoder: ClassTag, A: TypedEncoder: ClassTag, B: TypedEncoder: ClassTag](xs: List[X2[K, Map[A, B]]]): Prop = {
+      val tds = TypedDataset.create(xs)
+
+      val framelessResults = tds.explodeMap('b).collect().run().toVector
+      val scalaResults = xs.flatMap { x2 => x2.b.toList.map((x2.a, _)) }.toVector
+
+      framelessResults ?= scalaResults
+    }
+
+    check(forAll(prop[Int, Long, String] _))
+    check(forAll(prop[String, Int, Long] _))
+    check(forAll(prop[Long, String, Int] _))
   }
 }