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### What changes were proposed in this pull request? The PR is proposed for **pandas APIs on Spark**, in order to separate arithmetic operations shown as below into data-type-based structures. `__add__, __sub__, __mul__, __truediv__, __floordiv__, __pow__, __mod__, __radd__, __rsub__, __rmul__, __rtruediv__, __rfloordiv__, __rpow__,__rmod__` DataTypeOps and subclasses are introduced. The existing behaviors of each arithmetic operation should be preserved. ### Why are the changes needed? Currently, the same arithmetic operation of all data types is defined in one function, so it’s difficult to extend the behavior change based on the data types. Introducing DataTypeOps would be the foundation for [pandas APIs on Spark: Separate basic operations into data type based structures.](https://docs.google.com/document/d/12MS6xK0hETYmrcl5b9pX5lgV4FmGVfpmcSKq--_oQlc/edit?usp=sharing). ### Does this PR introduce _any_ user-facing change? No. ### How was this patch tested? Tests are introduced under pyspark.pandas.tests.data_type_ops. One test file per DataTypeOps class. Closes #32596 from xinrong-databricks/datatypeop_arith_fix. Authored-by: Xinrong Meng <xinrong.meng@databricks.com> Signed-off-by: Takuya UESHIN <ueshin@databricks.com> |
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| MANIFEST.in | ||
| mypy.ini | ||
| pylintrc | ||
| README.md | ||
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| setup.py | ||
Apache Spark
Spark is a unified analytics engine for large-scale data processing. 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 Structured 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 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, but some additional sub-packages have their own extra requirements for some features (including numpy, pandas, and pyarrow).