{"categories":[{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/3"},{"label":"Front-Ends","url":"https://skillfed.io/packages/category/database-front-ends"}],"enrichment":{"capability":"GraphFrames Python wrapper provides graph processing and analysis on Apache Spark, enabling operations like centrality metrics, motif finding, community detection, and traversals on distributed graph data.","skillfed_tags":["distributed-computing","graph-algorithms","spark-ecosystem"],"use_cases":["Compute PageRank and other centrality metrics on large social networks or web graphs in a distributed setting.","Discover motifs and structural patterns in biological networks, citation graphs, or knowledge graphs.","Detect communities and clusters in large graphs to identify groups or sub-networks.","Perform graph traversals and connectivity analysis to find paths, reachability, or connected components.","Build recommendation systems or link-prediction models using graph algorithms on Spark."],"what_it_does":"GraphFrames is a Python wrapper for the Scala GraphFrames library, enabling distributed graph processing on Apache Spark. It lets you build and analyze graphs at scale using familiar DataFrame-like APIs, supporting operations such as centrality calculations (PageRank), motif discovery, community detection, and graph traversals. The package works with both classic Spark and Spark-Connect, choosing the implementation automatically based on your session type.\n\nThe package itself is a thin Python layer\u2014the heavy lifting happens in the JVM via Spark and GraphX. You need to supply your own Spark cluster or Spark-Connect server and ensure the JVM-core and GraphX dependencies are available. It's designed for developers working with network data, social graphs, knowledge graphs, or any large-scale graph problem that benefits from distributed computation.","worth_installing":"Yes, if you have a Spark cluster or Spark-Connect server and need distributed graph processing. The package is actively maintained, permissively licensed, and has no known vulnerabilities. The main gotcha is the external JVM dependency\u2014you cannot use this package standalone; it requires a working Spark environment. If you already have Spark and need graph algorithms at scale, it's a solid choice."},"id":"graphframes-py","links":{"html":"https://skillfed.io/packages/graphframes-py","md":"https://skillfed.io/packages/graphframes-py.md","pypi":"https://pypi.org/project/graphframes-py/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-06-17","license_spdx":null,"license_treatment":"permissive","name":"graphframes-py","python_support":"supports_current","summary":"GraphFrames: Graph Processing Framework for Apache Spark"},"popularity":{"monthly_downloads":1283535,"position":4114,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"0.12.1"}