{"categories":[{"label":"Internet","url":"https://skillfed.io/packages/category/internet/3"}],"enrichment":{"capability":"BigFrames provides a pandas-compatible DataFrame API and scikit-learn-like ML interface that executes queries and computations on BigQuery, letting you scale analytics and machine learning workloads without leaving Python.","skillfed_tags":["bigquery-integration","distributed-analytics","gcp-native"],"use_cases":["Migrate existing pandas analytics code to BigQuery by changing imports, scaling to datasets too large for local memory.","Build and train ML models (linear regression, classification, clustering) on BigQuery data without exporting to a separate ML platform.","Perform geospatial analysis on large datasets using geopandas-compatible operations backed by BigQuery compute.","Combine structured analytics with Gemini AI methods for feature engineering, predictions, or text analysis on BigQuery tables.","Prototype analytics in a notebook, then deploy the same code to production with BigQuery as the execution engine."],"what_it_does":"BigFrames is a Python library that translates pandas-like DataFrame operations and scikit-learn-style ML calls into BigQuery SQL, executing them server-side on Google's managed infrastructure. It bridges the gap between local pandas workflows and cloud-scale analytics by letting developers write familiar Python code while leveraging BigQuery's distributed compute engine.\n\nThe package includes three main APIs: bigframes.pandas for analytics (compatible with many pandas workloads), bigframes.ml for supervised and unsupervised learning (modeled after scikit-learn), and bigframes.bigquery.ai for AI methods powered by Gemini. It depends on the full Google Cloud Python ecosystem\u2014BigQuery client, Storage, Functions, IAM, and authentication libraries\u2014plus data science foundations like pandas, numpy, pyarrow, and geospatial tools (geopandas, shapely). Setup requires a GCP project, BigQuery API enablement, and Application Default Credentials.","worth_installing":"Yes, if you have a GCP project with BigQuery and need to scale pandas-like analytics or train ML models on large datasets without managing infrastructure. The active maintenance, permissive license, and low install friction support adoption. No if you lack GCP access, work entirely with small local datasets, or need full pandas API coverage\u2014some operations may not be supported or may require workarounds."},"id":"bigframes","links":{"html":"https://skillfed.io/packages/bigframes","md":"https://skillfed.io/packages/bigframes.md","pypi":"https://pypi.org/project/bigframes/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-08-12","license_spdx":null,"license_treatment":"permissive","name":"bigframes","python_support":"supports_current","summary":"BigQuery DataFrames -- scalable analytics and machine learning with BigQuery"},"popularity":{"monthly_downloads":878643,"position":4826,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"2.48.0"}