Document use of self_destruct with toArrowTable

python/docs/source/user_guide/sql/arrow_pandas.rst

@@ -435,9 +435,12 @@ be verified by the user.
 Setting Arrow ``self_destruct`` for memory savings
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-Since Spark 3.2, the Spark configuration ``spark.sql.execution.arrow.pyspark.selfDestruct.enabled`` can be used to enable PyArrow's ``self_destruct`` feature, which can save memory when creating a Pandas DataFrame via ``toPandas`` by freeing Arrow-allocated memory while building the Pandas DataFrame.
-This option is experimental, and some operations may fail on the resulting Pandas DataFrame due to immutable backing arrays.
-Typically, you would see the error ``ValueError: buffer source array is read-only``.
-Newer versions of Pandas may fix these errors by improving support for such cases.
-You can work around this error by copying the column(s) beforehand.
-Additionally, this conversion may be slower because it is single-threaded.
+Since Spark 3.2, the Spark configuration ``spark.sql.execution.arrow.pyspark.selfDestruct.enabled``
+can be used to enable PyArrow's ``self_destruct`` feature, which can save memory when creating a
+Pandas DataFrame via ``toPandas`` by freeing Arrow-allocated memory while building the Pandas
+DataFrame. This option can also save memory when creating a PyArrow Table via ``toArrowTable``.
+This option is experimental. When used with ``toPandas``, some operations may fail on the resulting
+Pandas DataFrame due to immutable backing arrays. Typically, you would see the error 
+``ValueError: buffer source array is read-only``. Newer versions of Pandas may fix these errors by
+improving support for such cases. You can work around this error by copying the column(s)
+beforehand. Additionally, this conversion may be slower because it is single-threaded.