{"categories":[{"label":"Distributed Computing","url":"https://skillfed.io/packages/category/system-distributed-computing"}],"enrichment":{"capability":"Provides tools for distributing Pandas DataFrames and Series across Apache Spark clusters for large-scale data processing.","skillfed_tags":["abandoned","legacy"],"use_cases":["Distributing Pandas DataFrames across Spark clusters for parallel processing of large datasets","Combining Pandas data manipulation with Spark's distributed computing for legacy workflows","Prototyping distributed data analysis before migrating to modern solutions"],"what_it_does":"pyspark-pandas aims to bridge Pandas and Apache Spark by providing utilities to distribute Pandas DataFrames and Series across Spark clusters. It was designed to enable data analysis workflows that combine Pandas' ease-of-use with Spark's distributed processing power for handling large datasets.\n\nHowever, this project has been abandoned since 2014-10-14 and is no longer maintained. The package itself explicitly directs users to consider alternatives instead, suggesting this codebase may have been superseded. With no runtime dependencies listed and no recent activity, it represents a snapshot from the early Spark-Pandas integration era.","worth_installing":"No. This package is abandoned (last release 2014-10-14, no commits since), has an unclear license, and explicitly recommends users evaluate alternatives instead. Modern alternatives are far better choices for any current use case."},"id":"pyspark-pandas","links":{"html":"https://skillfed.io/packages/pyspark-pandas","md":"https://skillfed.io/packages/pyspark-pandas.md","pypi":"https://pypi.org/project/pyspark-pandas/"},"maintenance":{"status":"abandoned"},"meta":{"latest_release":"2014-10-14","license_spdx":null,"license_treatment":"unclear","name":"pyspark-pandas","python_support":"unspecified","summary":"Tools and algorithms for pandas Dataframes distributed on pyspark. Please consider the SparklingPandas project before this one"},"popularity":{"monthly_downloads":895169,"position":4791,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"0.0.7"}