diff --git a/python/pyspark/sql/pandas/group_ops.py b/python/pyspark/sql/pandas/group_ops.py
index 0945c0078a2a..c34a285144f0 100644
--- a/python/pyspark/sql/pandas/group_ops.py
+++ b/python/pyspark/sql/pandas/group_ops.py
@@ -231,9 +231,9 @@ def applyInPandasWithState(
         per-group state. The result Dataset will represent the flattened record returned by the
         function.
 
-        For a streaming Dataset, the function will be invoked first for all input groups and then
-        for all timed out states where the input data is set to be empty. Updates to each group's
-        state will be saved across invocations.
+        For a streaming :class:`DataFrame`, the function will be invoked first for all input groups
+        and then for all timed out states where the input data is set to be empty. Updates to each
+        group's state will be saved across invocations.
 
         The function should take parameters (key, Iterator[`pandas.DataFrame`], state) and
         return another Iterator[`pandas.DataFrame`]. The grouping key(s) will be passed as a tuple
@@ -257,10 +257,10 @@ def applyInPandasWithState(
         user-defined state. The value of the state will be presented as a tuple, as well as the
         update should be performed with the tuple. The corresponding Python types for
         :class:DataType are supported. Please refer to the page
-        https://spark.apache.org/docs/latest/sql-ref-datatypes.html (python tab).
+        https://spark.apache.org/docs/latest/sql-ref-datatypes.html (Python tab).
 
-        The size of each DataFrame in both the input and output can be arbitrary. The number of
-        DataFrames in both the input and output can also be arbitrary.
+        The size of each `pandas.DataFrame` in both the input and output can be arbitrary. The
+        number of `pandas.DataFrame` in both the input and output can also be arbitrary.
 
         .. versionadded:: 3.4.0
 
@@ -294,6 +294,7 @@ def applyInPandasWithState(
         ...         total_len += len(pdf)
         ...     state.update((total_len,))
         ...     yield pd.DataFrame({"id": [key[0]], "countAsString": [str(total_len)]})
+        ...
         >>> df.groupby("id").applyInPandasWithState(
         ...     count_fn, outputStructType="id long, countAsString string",
         ...     stateStructType="len long", outputMode="Update",
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/arrow/ArrowWriter.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/arrow/ArrowWriter.scala
index 2988c0fb5187..34e128a4925f 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/arrow/ArrowWriter.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/arrow/ArrowWriter.scala
@@ -98,16 +98,6 @@ class ArrowWriter(val root: VectorSchemaRoot, fields: Array[ArrowFieldWriter]) {
     count += 1
   }
 
-  def sizeInBytes(): Int = {
-    var i = 0
-    var bytes = 0
-    while (i < fields.size) {
-      bytes += fields(i).getSizeInBytes()
-      i += 1
-    }
-    bytes
-  }
-
   def finish(): Unit = {
     root.setRowCount(count)
     fields.foreach(_.finish())
@@ -142,10 +132,6 @@ private[arrow] abstract class ArrowFieldWriter {
     count += 1
   }
 
-  def getSizeInBytes(): Int = {
-    valueVector.getBufferSizeFor(count)
-  }
-
   def finish(): Unit = {
     valueVector.setValueCount(count)
   }