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---
dataset_info:
features:
- name: code
dtype: string
- name: repo_name
dtype: string
- name: path
dtype: string
- name: language
dtype: string
- name: license
dtype: string
- name: size
dtype: int64
splits:
- name: train
num_bytes: 360682958
num_examples: 45001
- name: validation
num_bytes: 37445138
num_examples: 5000
download_size: 141557496
dataset_size: 398128096
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
---
# 🐍 CodeParrot Python Only
This dataset contains **Python-only source code** extracted from the larger [CodeParrot](https://huggingface.co/datasets/codeparrot) corpus. It includes high-quality `.py` files filtered from public GitHub repositories and curated for use in training large language models (LLMs) on Python code generation tasks.
## 📦 Dataset Summary
- ✅ Filtered to include **only Python code**
- 🧹 Cleaned to remove non-source content (e.g., binaries, notebooks, scripts with mixed languages)
- 🧠 Ideal for training or evaluating code generation models
- 🧾 Each entry is a code snippet or full Python file stored as raw text
## 💡 Use Cases
This dataset is best suited for:
- Training/fine-tuning LLMs for:
- Python code generation
- Auto-completion
- Bug detection
- Code summarization
- Evaluating code-specific capabilities of general LLMs
- Research in code intelligence, representation learning, and software engineering
## 📁 Example Format
Each record is a single code block:
```json
{
"code": "def hello_world():\n print('Hello, world!')"
}
```
🚀 How to Use
```
from datasets import load_dataset
ds = load_dataset("theothertom/codeparrot-python-only")
print(ds["train"][0]["code"])
```
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