History

Li Jin a34fce19bc [SPARK-23314][PYTHON] Add ambiguous=False when localizing tz-naive timestamps in Arrow codepath to deal with dst ## What changes were proposed in this pull request? When tz_localize a tz-naive timetamp, pandas will throw exception if the timestamp is during daylight saving time period, e.g., `2015-11-01 01:30:00`. This PR fixes this issue by setting `ambiguous=False` when calling tz_localize, which is the same default behavior of pytz. ## How was this patch tested? Add `test_timestamp_dst` Author: Li Jin <ice.xelloss@gmail.com> Closes #20537 from icexelloss/SPARK-23314.		2018-02-11 17:31:35 +09:00
..
docs	[SPARK-21866][ML][PYSPARK] Adding spark image reader	2017-11-22 15:45:45 -08:00
lib	[SPARK-21278][PYSPARK] Upgrade to Py4J 0.10.6	2017-07-05 16:33:23 -07:00
pyspark	[SPARK-23314][PYTHON] Add ambiguous=False when localizing tz-naive timestamps in Arrow codepath to deal with dst	2018-02-11 17:31:35 +09:00
test_coverage	[SPARK-7721][PYTHON][TESTS] Adds PySpark coverage generation script	2018-01-22 22:12:50 +09:00
test_support	[SPARK-19610][SQL] Support parsing multiline CSV files	2017-02-28 13:34:33 -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-18652][PYTHON] Include the example data and third-party licenses in pyspark package.	2016-12-07 06:09:27 +08:00
pylintrc	[SPARK-13596][BUILD] Move misc top-level build files into appropriate subdirs	2016-03-07 14:48:02 -08:00
README.md	[SPARK-22324][SQL][PYTHON][FOLLOW-UP] Update setup.py file.	2017-12-27 20:51:26 +09:00
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-7721][PYTHON][TESTS] Adds PySpark coverage generation script	2018-01-22 22:12:50 +09:00
run-tests.py	[SPARK-23300][TESTS] Prints out if Pandas and PyArrow are installed or not in PySpark SQL tests	2018-02-06 16:08:15 +09:00
setup.cfg	[SPARK-1267][SPARK-18129] Allow PySpark to be pip installed	2016-11-16 14:22:15 -08:00
setup.py	[SPARK-23319][TESTS] Explicitly specify Pandas and PyArrow versions in PySpark tests (to skip or test)	2018-02-07 23:28:10 +09:00

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 setup 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 0.10.6), but some additional sub-packages have their own extra requirements for some features (including numpy, pandas, and pyarrow).