{"categories":[{"label":"Internet","url":"https://skillfed.io/packages/category/internet/2"}],"enrichment":{"capability":"kagglehub provides Python access to Kaggle datasets, models, and notebook outputs with integrated caching and native support for the Kaggle notebook environment.","skillfed_tags":["kaggle-integration","dataset-download","model-management"],"use_cases":["Download pre-trained models from Kaggle to use in local ML projects without manual browser downloads","Load Kaggle competition datasets directly into pandas DataFrames or Polars for analysis in Jupyter notebooks","Automate dataset and model retrieval in CI/CD pipelines or scheduled data processing workflows","Upload trained models to Kaggle with version notes and licensing information for sharing with the community","Access private or consent-gated Kaggle resources programmatically after authentication"],"what_it_does":"kagglehub is a Python client library that simplifies downloading and uploading Kaggle resources\u2014datasets, models, and notebook outputs\u2014directly from your code. It wraps the Kaggle API and provides a straightforward interface for authentication and resource management.\n\nThe library behaves differently depending on where it runs: inside a Kaggle notebook, resources are automatically attached and served from Kaggle's shared cache; outside a notebook, files are downloaded to a local cache folder. It supports multiple authentication methods (API token, environment variable, config file, Google Colab secrets) and can load datasets directly into pandas, Hugging Face, or Polars objects via optional adapters. It also handles model uploads with versioning, licensing, and file-ignore patterns.","worth_installing":"Yes. kagglehub is actively maintained, has low install friction, carries a permissive Apache 2.0 license, and no known vulnerabilities. Install it if you regularly work with Kaggle datasets or models and want to automate downloads in Python code. The native Kaggle notebook integration and multi-format dataset loading (pandas, Polars, Hugging Face) make it a natural choice for data science workflows."},"id":"kagglehub","links":{"html":"https://skillfed.io/packages/kagglehub","md":"https://skillfed.io/packages/kagglehub.md","pypi":"https://pypi.org/project/kagglehub/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-06-09","license_spdx":null,"license_treatment":"permissive","name":"kagglehub","python_support":"supports_current","summary":"Access Kaggle resources anywhere"},"popularity":{"monthly_downloads":2536225,"position":3014,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"1.0.2"}