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Co-authored-by: Hyukjin Kwon <[email protected]>

Author: lidavidm

python/docs/source/user_guide/arrow_pandas.rst

@@ -413,8 +413,8 @@ be read on the Arrow 0.15.0 release `blog <https://arrow.apache.org/blog/2019/10
 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.
+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.