Github user HyukjinKwon commented on a diff in the pull request:
https://github.com/apache/spark/pull/19575#discussion_r163855755
--- Diff: docs/sql-programming-guide.md ---
@@ -1640,6 +1640,154 @@ Configuration of Hive is done by placing your
`hive-site.xml`, `core-site.xml` a
You may run `./bin/spark-sql --help` for a complete list of all available
options.
+# Usage Guide for Pandas with Arrow
+
+## Arrow in Spark
+
+Apache Arrow is an in-memory columnar data format that is used in Spark to
efficiently transfer
+data between JVM and Python processes. This currently is most beneficial
to Python users that
+work with Pandas/NumPy data. Its usage is not automatic and might require
some minor
+changes to configuration or code to take full advantage and ensure
compatibility. This guide will
+give a high-level description of how to use Arrow in Spark and highlight
any differences when
+working with Arrow-enabled data.
+
+## Ensure pyarrow Installed
+
+If you install pyspark using pip, then pyarrow can be brought in as an
extra dependency of the sql
+module with the command "pip install pyspark[sql]". Otherwise, you must
ensure that pyarrow is
+installed and available on all cluster node Python environments. The
current supported version is
+0.8.0. You can install using pip or conda from the conda-forge channel.
See pyarrow
+[installation](https://arrow.apache.org/docs/python/install.html) for
details.
+
+## How to Enable for Conversion to/from Pandas
+
+Arrow is available as an optimization when converting a Spark DataFrame to
Pandas using the call
+`toPandas()` and when creating a Spark DataFrame from Pandas with
`createDataFrame(pandas_df)`.
+To use Arrow when executing these calls, it first must be enabled by
setting the Spark conf
--- End diff --
conf -> configuration
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