| title: README | |
| emoji: π» | |
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| <img src="https://www.datawaza.com/en/latest/_static/datawaza_logo_name_trans.svg" alt="datawaza_logo_name_trans.svg" width="300"/> | |
| [Datawaza](http://www.datawaza.com) streamlines common Data Science tasks. It's a collection of tools for data exploration, visualization, data cleaning, pipeline creation, hyper-parameter searching, model iteration, and evaluation. It builds upon core libraries like [Pandas](https://pandas.pydata.org/), [Matplotlib](https://matplotlib.org/), [Seaborn](https://seaborn.pydata.org/), [Scikit-Learn](https://scikit-learn.org/stable/), [TensorFlow](https://www.tensorflow.org), and [PyTorch](https://pytorch.org). | |
| ## Open Source Library | |
| You can find the [Datawaza repo](https://github.com/jbeno/datawaza/) on Github, and the [latest release](https://pypi.org/project/datawaza/) on PyPi. The [user guide](https://www.datawaza.com/en/latest/userguide.html) is a Jupyter notebook that walks through how to use the Datawaza functions. It's probably the best place to start. | |
| ## What is Waza? | |
| Waza (ζ) means "technique" in Japanese. In martial arts like Aikido, it is paired with words like "suwari-waza" (sitting techniques) or "kaeshi-waza" (reversal techniques). So we've paired it with "data" to represent Data Science techniques: γγΌγΏζ "data-waza". |