5f0113e3a6
### What changes were proposed in this pull request? The PR is proposed to support creating a Column of numpy literal value in pandas-on-Spark. It consists of three changes mainly: - Enable the `lit` function defined in `pyspark.pandas.spark.functions` to support numpy literals input. ```py >>> from pyspark.pandas.spark import functions as SF >>> SF.lit(np.int64(1)) Column<'CAST(1 AS BIGINT)'> >>> SF.lit(np.int32(1)) Column<'CAST(1 AS INT)'> >>> SF.lit(np.int8(1)) Column<'CAST(1 AS TINYINT)'> >>> SF.lit(np.byte(1)) Column<'CAST(1 AS TINYINT)'> >>> SF.lit(np.float32(1)) Column<'CAST(1.0 AS FLOAT)'> ``` - Substitute `F.lit` by `SF.lit`, that is, use `lit` function defined in `pyspark.pandas.spark.functions` rather than `lit` function defined in `pyspark.sql.functions` to allow creating columns out of numpy literals. - Enable numpy literals input in `isin` method Non-goal: - Some pandas-on-Spark APIs use PySpark column-related APIs internally, and these column-related APIs don't support numpy literals, thus numpy literals are disallowed as input (e.g. `to_replace` parameter in `replace` API). This PR doesn't aim to adjust all of them. This PR adjusts `isin` only, because the PR is inspired by that (as https://github.com/databricks/koalas/issues/2161). - To complete mappings between all kinds of numpy literals and Spark data types should be a followup task. ### Why are the changes needed? Spark (`lit` function defined in `pyspark.sql.functions`) doesn't support creating a Column out of numpy literal value. So `lit` function defined in `pyspark.pandas.spark.functions` is adjusted in order to support that in pandas-on-Spark. ### Does this PR introduce _any_ user-facing change? Yes. Before: ```py >>> a = ps.DataFrame({'source': [1,2,3,4,5]}) >>> a.source.isin([np.int64(1), np.int64(2)]) Traceback (most recent call last): ... AttributeError: 'numpy.int64' object has no attribute '_get_object_id' ``` After: ```py >>> a = ps.DataFrame({'source': [1,2,3,4,5]}) >>> a.source.isin([np.int64(1), np.int64(2)]) 0 True 1 True 2 False 3 False 4 False Name: source, dtype: bool ``` ### How was this patch tested? Unit tests. Closes #32955 from xinrong-databricks/datatypeops_literal. Authored-by: Xinrong Meng <xinrong.meng@databricks.com> Signed-off-by: Takuya UESHIN <ueshin@databricks.com> |
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| lib | ||
| pyspark | ||
| test_coverage | ||
| test_support | ||
| .coveragerc | ||
| .gitignore | ||
| MANIFEST.in | ||
| mypy.ini | ||
| pylintrc | ||
| README.md | ||
| run-tests | ||
| run-tests-with-coverage | ||
| run-tests.py | ||
| setup.cfg | ||
| 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).