Kaggle Api GuideSAFE

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---
name: kaggle-api-guide
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 Framewo

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