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Takuya UESHIN 594be7a911 [SPARK-27240][PYTHON] Use pandas DataFrame for struct type argument in Scalar Pandas UDF. 
## What changes were proposed in this pull request?

Now that we support returning pandas DataFrame for struct type in Scalar Pandas UDF.

If we chain another Pandas UDF after the Scalar Pandas UDF returning pandas DataFrame, the argument of the chained UDF will be pandas DataFrame, but currently we don't support pandas DataFrame as an argument of Scalar Pandas UDF. That means there is an inconsistency between the chained UDF and the single UDF.

We should support taking pandas DataFrame for struct type argument in Scalar Pandas UDF to be consistent.
Currently pyarrow >=0.11 is supported.

## How was this patch tested?

Modified and added some tests.

Closes #24177 from ueshin/issues/SPARK-27240/structtype_argument.

Authored-by: Takuya UESHIN <ueshin@databricks.com>
Signed-off-by: Bryan Cutler <cutlerb@gmail.com>
2019-03-25 11:26:09 -07:00
..
docs [SPARK-26856][PYSPARK] Python support for from_avro and to_avro APIs 2019-03-11 10:15:07 +09:00
lib [SPARK-25891][PYTHON] Upgrade to Py4J 0.10.8.1 2018-10-31 09:55:03 -07:00
pyspark [SPARK-27240][PYTHON] Use pandas DataFrame for struct type argument in Scalar Pandas UDF. 2019-03-25 11:26:09 -07:00
test_coverage [SPARK-7721][PYTHON][TESTS] Adds PySpark coverage generation script 2018-01-22 22:12:50 +09:00
test_support [SPARK-23094][SPARK-23723][SPARK-23724][SQL] Support custom encoding for json files 2018-04-29 11:25:31 +08:00
.coveragerc [SPARK-7721][PYTHON][TESTS] Adds PySpark coverage generation script 2018-01-22 22:12:50 +09:00
.gitignore [SPARK-3946] gitignore in /python includes wrong directory 2014-10-14 14:09:39 -07:00
MANIFEST.in [SPARK-26803][PYTHON] Add sbin subdirectory to pyspark 2019-02-27 08:39:55 -06:00
pylintrc [SPARK-13596][BUILD] Move misc top-level build files into appropriate subdirs 2016-03-07 14:48:02 -08:00
README.md [SPARK-25891][PYTHON] Upgrade to Py4J 0.10.8.1 2018-10-31 09:55:03 -07:00
run-tests [SPARK-8583] [SPARK-5482] [BUILD] Refactor python/run-tests to integrate with dev/run-tests module system 2015-06-27 20:24:34 -07:00
run-tests-with-coverage [SPARK-26252][PYTHON] Add support to run specific unittests and/or doctests in python/run-tests script 2018-12-05 15:22:08 +08:00
run-tests.py [SPARK-26132][BUILD][CORE] Remove support for Scala 2.11 in Spark 3.0.0 2019-03-25 10:46:42 -05:00
setup.cfg [SPARK-1267][SPARK-18129] Allow PySpark to be pip installed 2016-11-16 14:22:15 -08:00
setup.py [SPARK-26803][PYTHON] Add sbin subdirectory to pyspark 2019-02-27 08:39:55 -06:00

README.md

Apache Spark

Spark is a fast and general cluster computing system for Big Data. It provides high-level APIs in Scala, Java, Python, and R, and an optimized engine that supports general computation graphs for data analysis. It also supports a rich set of higher-level tools including Spark SQL for SQL and DataFrames, MLlib for machine learning, GraphX for graph processing, and Spark Streaming for stream processing.

http://spark.apache.org/

Online Documentation

You can find the latest Spark documentation, including a programming guide, on the project web page

Python Packaging

This README file only contains basic information related to pip installed PySpark. This packaging is currently experimental and may change in future versions (although we will do our best to keep compatibility). Using PySpark requires the Spark JARs, and if you are building this from source please see the builder instructions at "Building Spark".

The Python packaging for Spark is not intended to replace all of the other use cases. This Python packaged version of Spark is suitable for interacting with an existing cluster (be it Spark standalone, YARN, or Mesos) - but does not contain the tools required to set up your own standalone Spark cluster. You can download the full version of Spark from the Apache Spark downloads page.

NOTE: If you are using this with a Spark standalone cluster you must ensure that the version (including minor version) matches or you may experience odd errors.

Python Requirements

At its core PySpark depends on Py4J (currently version 0.10.8.1), but some additional sub-packages have their own extra requirements for some features (including numpy, pandas, and pyarrow).