Github user HyukjinKwon commented on a diff in the pull request:
https://github.com/apache/spark/pull/19575#discussion_r164065094
--- Diff: docs/sql-programming-guide.md ---
@@ -1640,6 +1640,129 @@ 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.
+# PySpark 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 nodes. 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.
+
+## Enabling 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, users need to first set the Spark
configuration
+'spark.sql.execution.arrow.enabled' to 'true'. This is disabled by default.
+
+<div class="codetabs">
+<div data-lang="python" markdown="1">
+{% include_example dataframe_with_arrow python/sql/arrow.py %}
+</div>
+</div>
+
+Using the above optimizations with Arrow will produce the same results as
when Arrow is not
+enabled. Not all Spark data types are currently supported and an error
will be raised if a column
+has an unsupported type, see [Supported Types](#supported-types).
+
+## Pandas UDFs (a.k.a. Vectorized UDFs)
+
+With Arrow, we introduce a new type of UDF - pandas UDF. Pandas UDF is
defined with a new function
+`pyspark.sql.functions.pandas_udf` and allows users to use functions that
operate on `pandas.Series`
+and `pandas.DataFrame` with Spark. Currently, there are two types of
pandas UDF: Scalar and Group Map.
+
+### Scalar
+
+Scalar pandas UDFs are used for vectorizing scalar operations. They can be
used with functions such as `select`
+and `withColumn`. To define a scalar pandas UDF, use `pandas_udf` to
annotate a Python function. The Python
+function should take `pandas.Series` as inputs and return a
`pandas.Series` of the same length. Internally,
+Spark will split a column into multiple `pandas.Series` and invoke the
Python function with each `pandas.Series`,
+and concat the results together to be a new column.
+
+The following example shows how to create a scalar pandas UDF that
computes the product of 2 columns.
+
+<div class="codetabs">
+<div data-lang="python" markdown="1">
+{% include_example scalar_pandas_udf python/sql/arrow.py %}
+</div>
+</div>
+
+### Group Map
+Group map pandas UDFs are used with `groupBy().apply()` which implements
the "split-apply-combine" pattern.
+Split-apply-combine consists of three steps:
+* Split the data into groups by using `DataFrame.groupBy`.
+* Apply a function on each group. The input and output of the function are
both `pandas.DataFrame`. The
+ input data contains all the rows and columns for each group.
+* Combine the results into a new `DataFrame`.
+
+To use groupby apply, the user needs to define the following:
+* A Python function that defines the computation for each group.
+* A `StructType` object or a string that defines the schema of the output
`DataFrame`.
+
+The following example shows how to use groupby apply to subtract the mean
from each value in the group.
+
+<div class="codetabs">
+<div data-lang="python" markdown="1">
+{% include_example group_map_pandas_udf python/sql/arrow.py %}
+</div>
+</div>
+
+For detailed usage, please see `pyspark.sql.functions.pandas_udf` and
+`pyspark.sql.GroupedData.apply`.
--- End diff --
and this:
```
[`pyspark.sql.GroupedData.apply`](api/python/pyspark.sql.html#pyspark.sql.GroupedData.apply)
```
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