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## What changes were proposed in this pull request? It provides a better error message when doing `spark_session.createDataFrame(pandas_df)` with no schema and an error occurs in the schema inference due to incompatible types. The Pandas column names are propagated down and the error message mentions which column had the merging error. https://issues.apache.org/jira/browse/SPARK-22566 ## How was this patch tested? Manually in the `./bin/pyspark` console, and with new tests: `./python/run-tests` <img width="873" alt="screen shot 2017-11-21 at 13 29 49" src="https://user-images.githubusercontent.com/3977115/33080121-382274e0-cecf-11e7-808f-057a65bb7b00.png"> I state that the contribution is my original work and that I license the work to the Apache Spark project under the project’s open source license. Author: Guilherme Berger <gberger@palantir.com> Closes #19792 from gberger/master. |
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| README.md | ||
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| setup.py | ||
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.
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).