DataRefiner: An Advanced Toolkit for Data Transformation and Processing
DataRefiner is a cutting-edge Python library designed to streamline and enhance the data transformation and
processing workflow. Whether you're dealing with complex datasets in machine learning, data analysis, or any
data-driven domain, DataRefiner provides an extensive suite of tools to ensure your data is clean, well-structured,
and ready for insightful analysis.
Key features of DataRefiner include:
- Advanced Data Transformation: Perform sophisticated data manipulation tasks with ease, including normalization, scaling, encoding, and more.
- Customizable Processing Pipelines: Create flexible and reusable data processing pipelines tailored to your specific needs.
- Integration with Popular Libraries: Seamlessly integrate with libraries such as scikit-learn, pandas, and NumPy to enhance your existing data processing workflows.
- User-Friendly API: Enjoy an intuitive and easy-to-use interface that simplifies complex data transformation tasks.
DataRefiner is designed for data scientists, machine learning engineers, and researchers who demand precision and efficiency in their data preparation processes. Empower your data projects with DataRefiner and unlock the full potential of your data.
| DataRefiner | An Advanced Toolkit for Data Transformation and Processing |
|---|---|
| Free software | GNU General Public License (GPL) V3 license |
| Documentation | https://datarefiner.readthedocs.io |
| Python versions | >= 3.8.x |
| Dependencies | numpy, scipy, scikit-learn, pandas, permetrics |
Usage
- Install the current PyPI release:
$ pip install datarefiner
After installation, you can check DataRefiner version:
$ python
>>> import datarefiner
>>> datarefiner.__version__
Please go check out the examples folder. You'll be surprised by what this library can do for your data. You can also read the documentation for more detailed installation instructions, explanations, and examples.
Citation Request
If you use this library for your project, please cite us with:
@software{thieu_2024_12820732,
author = {Nguyen Van Thieu},
title = {DataRefiner: An Advanced Toolkit for Data Transformation and Processing},
month = jul,
year = 2024,
publisher = {Zenodo},
doi = {10.5281/zenodo.12820731},
url = {https://github.com/thieu1995/DataRefiner}
}
Official Links (Get support for questions and answers)
1 maintainer
Release files for datarefiner 0.1.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| datarefiner-0.1.0.tar.gz | 25.3 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| datarefiner-0.1.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 47.7 kB
Release files / datarefiner-0.1.0.tar.gz
| Download URL | datarefiner-0.1.0.tar.gz |
|---|---|
| Size | 25.3 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
a52b1460620851eb002c9916ce028a6b54de3b284a4f02ee23e7009ae1bac6c3
|
|
BLAKE2b-256 checksum How to use checksums |
8939d690f40dbc66a93653c20c15fc7093e359442bcf6f627cfc7a2a9f1405b8
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/5.1.1 CPython/3.9.19
|
Release files / datarefiner-0.1.0-py3-none-any.whl
| Download URL | datarefiner-0.1.0-py3-none-any.whl |
|---|---|
| Size | 22.4 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
4a1dc223f74884dc52b395842c5138fd0f886bcda347ab4b88cdbf02f4bb7bcc
|
|
BLAKE2b-256 checksum How to use checksums |
3e821fe2cd2c8406af097e9f0e06ecd359d064831dea1a9b0e9d12cf2b28f8c3
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/5.1.1 CPython/3.9.19
|
