semvec/pypi-packages huggingface dataset:
pytadata-entriz 2.1.4
Uniform DataFrame writer with optional AWS or GCP back-ends
License: MIT
Requires Python: <4.0.0,>=3.11
Keywords: data-engineering, pandas, aws, gcp, bigquery, s3

<!-- Lightweight DataFrame library for reading, validating, and writing data entries with unified API for local, AWS, and GCP storage backends -->

# DataFrame Library
A lightweight DataFrame library designed for reading, validating, and writing data entries with a unified API for local, AWS, and GCP storage backends.
## Features
- **Unified API**: Interact with data across local, AWS, and GCP storage seamlessly.
- **Data Validation**: Ensure data integrity with built-in validation mechanisms.
- **Flexible Storage**: Read and write data from various storage backends without changing your code.
- **Lightweight**: Minimal dependencies and overhead for efficient data handling.
## Installation
```bash
pip install dataframe-library
```
## Usage
```python
from dataframe_library import DataFrame
# Create a DataFrame instance
df = DataFrame()
# Read data from a local file
df.read('data.csv')
# Validate the data
df.validate()
# Write data to an AWS S3 bucket
df.write('s3://my-bucket/data.csv')
# Read data from a GCP bucket
df.read('gs://my-bucket/data.csv')
# Write data to a local file
df.write('output.csv')
```
## Documentation
For detailed documentation, please visit [DataFrame Library Documentation](https://example.com/docs).
## Contributing
We welcome contributions! Please read our [Contributing Guidelines](https://example.com/contributing) for more information on how to get involved.
## License
This project is licensed under the MIT License. See the [LICENSE](https://example.com/license) file for details.
## Contact
For any questions or issues, please open an issue on our [GitHub repository]