--- name: kaggle-api-guide-c7bfa5d5fdb3 description: "Download datasets, manage competitions and notebooks via Kaggle API" metadata: openclaw: emoji: "📈" category: "tools" subcategory: "code-exec" keywords: ["kaggle", "datasets", "competitions", "notebooks", "data-science", "machine-learning"] source: "https://www.kaggle.com/docs/api" --- # Kaggle API Guide ## Overview Kaggle is the world's largest data science and machine learning community, hosting thousands of datasets, competitions, and computational notebooks. The Kaggle API provides programmatic access to these resources, enabling researchers to download datasets, submit competition entries, manage kernels (notebooks), and explore the Kaggle ecosystem from the command line or scripts. For academic researchers, Kaggle is a valuable resource for accessing curated, well-documented datasets across diverse domains including healthcare, natural language processing, computer vision, economics, and social sciences. Many published research papers use Kaggle datasets as benchmarks, and the platform's competition infrastructure provides standardized evaluation frameworks for comparing methods. The Kaggle API is available as a Python CLI tool and library. It requires a free Kaggle account and API token for authentication. The API supports dataset search and download, competition data retrieval, kernel management, and model access. ## Authentication A free Kaggle API token is required. Generate one from your Kaggle account settings at https://www.kaggle.com/settings. Download the `kaggle.json` credentials file and place it in the standard location: ```bash # The kaggle.json file should be at ~/.kaggle/kaggle.json # It contains your username and key from your Kaggle account settings mkdir -p ~/.kaggle # Move your downloaded kaggle.json to ~/.kaggle/kaggle.json chmod 600 ~/.kaggle/kaggle.json ``` Alternatively, use environment variables: ```bash export KAGGLE_USERNAME=$KAGGLE_USERNAME export KAGGLE_KEY=$KAGGLE_KEY ``` Install the CLI tool: ```bash pip install kaggle ``` ## Core Endpoints ### Search Datasets Find datasets by keyword, file type, or license. ```bash # Search for datasets kaggle datasets list -s "climate change" --sort-by votes # Search with specific criteria kaggle datasets list -s "medical imaging" --file-type csv --max-size 1000000 ``` ### Download a Dataset ```bash # Download and unzip a dataset kaggle datasets download -d "heptapod/titanic" --unzip -p ./data/titanic/ # Download a specific file from a dataset kaggle datasets download -d "yelp-dataset/yelp-dataset" -f "yelp_academic_dataset_review.json" -p ./data/ ``` ### List and Join Competitions ```bash # List active competitions kaggle competitions list # Download competition data (must accept rules on kaggle.com first) kaggle competitions download -c "house-prices-advanced-regression-techniques" -p ./data/house-prices/ ``` ### Submit to a Competition ```bash # Submit predictions kaggle competitions submit -c "house-prices-advanced-regression-techniques" \ -f ./submission.csv -m "Random forest baseline v1" # Check submission status kaggle competitions submissions -c "house-prices-advanced-regression-techniques" ``` ### Manage Notebooks (Kernels) ```bash # Search for notebooks kaggle kernels list -s "transformer nlp" --sort-by voteCount # Pull a notebook to local kaggle kernels pull "username/notebook-name" -p ./notebooks/ # Push a notebook to Kaggle kaggle kernels push -p ./my-notebook/ ``` ### Python Example: Automated Dataset Discovery and Download ```python import subprocess import json import os def search_kaggle_datasets(query, sort_by="votes", max_results=10): """Search Kaggle datasets and return structured results.""" cmd = [ "kaggle", "datasets", "list", "-s", query, "--sort-by", sort_by, "--max-size", "50000000", "--csv" ] result = subprocess.run(cmd, capture_output=True, text=True) lines = result.stdout.strip().split("\n") if len(lines) < 2: return [] headers = lines[0].split(",") datasets = [] for line in lines[1:max_results + 1]: values = line.split(",") dataset = dict(zip(headers, values)) datasets.append(dataset) return datasets def download_dataset(dataset_ref, output_dir="./data"): """Download a Kaggle dataset by reference.""" os.makedirs(output_dir, exist_ok=True) cmd = [ "kaggle", "datasets", "download", "-d", dataset_ref, "--unzip", "-p", output_dir ] result = subprocess.run(cmd, capture_output=True, text=True) if result.returncode == 0: print(f"Downloaded {dataset_ref} to {output_dir}") else: print(f"Error: {result.stderr}") # Search for NLP benchmark datasets datasets = search_kaggle_datasets("nlp text classification benchmark") for ds in datasets[:5]: print(f" {ds.get('ref', 'N/A')}") print(f" Size: {ds.get('totalBytes', 'N/A')} bytes") print(f" Votes: {ds.get('voteCount', 'N/A')}") print() ``` ### Python Example: Using the Kaggle Python API Directly ```python from kaggle.api.kaggle_api_extended import KaggleApi api = KaggleApi() api.authenticate() # Search datasets datasets = api.dataset_list(search="genomics", sort_by="updated") for ds in datasets[:5]: print(f"{ds.ref}: {ds.title} ({ds.size})") # Get dataset metadata metadata = api.dataset_view("nih-chest-xrays/data") print(f"Title: {metadata.title}") print(f"Size: {metadata.totalBytes}") print(f"Description: {metadata.description[:200]}") # Download dataset files api.dataset_download_files( "nih-chest-xrays/sample", path="./data/chest-xrays/", unzip=True ) ``` ## Common Research Patterns **Benchmark Dataset Access:** Download well-established datasets used in published research for reproducibility studies. Kaggle hosts canonical versions of many benchmark datasets referenced in ML papers. **Competition as Evaluation Framework:** Use Kaggle competitions as standardized evaluation environments with leaderboards and held-out test sets. Submit predictions from novel methods to compare against state-of-the-art approaches. **Data Exploration Notebooks:** Search for and pull community notebooks that explore datasets relevant to your research. These often contain valuable preprocessing code, exploratory analysis, and baseline models. **Collaborative Research Datasets:** Upload processed research datasets to Kaggle for sharing with collaborators and the broader community, enabling others to reproduce and extend your work. **Cross-Domain Transfer:** Search across Kaggle's diverse dataset collection to find datasets from adjacent domains that could be useful for transfer learning or cross-domain validation studies. ## Rate Limits and Best Practices - **API rate limits:** Kaggle imposes daily limits on API calls; typical free accounts allow several hundred requests per day - **Download limits:** Large datasets may take significant time and disk space; check sizes before downloading - **Competition rules:** Always accept competition rules on the Kaggle website before attempting to download competition data via API - **Kernel push format:** When pushing notebooks, include a `kernel-metadata.json` file specifying the kernel type, language, and datasets - **Authentication security:** Never commit `kaggle.json` to version control; use environment variables in CI/CD pipelines - **Dataset versioning:** Kaggle datasets support versions; specify version numbers for reproducibility in research - **Large files:** For datasets over 10GB, consider using the Kaggle CLI rather than the Python API for more reliable downloads ## References - Kaggle API Documentation: https://www.kaggle.com/docs/api - Kaggle API GitHub Repository: https://github.com/Kaggle/kaggle-api - Kaggle Datasets: https://www.kaggle.com/datasets - Kaggle Competitions: https://www.kaggle.com/competitions - Kaggle Notebooks: https://www.kaggle.com/code --- Source attribution: wentorai Original project: https://github.com/wentorai/research-plugins/blob/bf44b3cd617fa94c8a1b254c5d1987142ca3d631/skills/tools/code-exec/kaggle-api-guide/SKILL.md License: MIT License information: https://opensource.org/license/mit Original upstream instructions; downloaded from Mega AI Hub. ## Upstream license notice MIT License Copyright (c) 2026 Wentor AI Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. 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