Metadata-Version: 2.1
Name: pyspark
Version: 3.5.6
Summary: Apache Spark Python API
Home-page: https://github.com/apache/spark/tree/master/python
Author: Spark Developers
Author-email: dev@spark.apache.org
License: http://www.apache.org/licenses/LICENSE-2.0
Description: # 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,
        pandas API on Spark for pandas workloads, MLlib for machine learning, GraphX for graph processing,
        and Structured Streaming for stream processing.
        
        <https://spark.apache.org/>
        
        ## Online Documentation
        
        You can find the latest Spark documentation, including a programming
        guide, on the [project web page](https://spark.apache.org/documentation.html)
        
        
        ## 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"](https://spark.apache.org/docs/latest/building-spark.html).
        
        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](https://spark.apache.org/downloads.html).
        
        
        **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).
        See also [Dependencies](https://spark.apache.org/docs/latest/api/python/getting_started/install.html#dependencies) for production, and [dev/requirements.txt](https://github.com/apache/spark/blob/master/dev/requirements.txt) for development.
        
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: Implementation :: CPython
Classifier: Programming Language :: Python :: Implementation :: PyPy
Classifier: Typing :: Typed
Requires-Python: >=3.8
Description-Content-Type: text/markdown
Provides-Extra: connect
Provides-Extra: ml
Provides-Extra: mllib
Provides-Extra: pandas_on_spark
Provides-Extra: sql
