NOTE: THIS IS NOT YET RELEASE READY, PLEASE BE PATIENT.
This repository contains tools that make working with scikit-learn and pandas easier.
What is this?
dstoolbox is not one big tool but rather an amalgamation of small re-usable tools. They are intended to work well with scikit-learn and pandas make the integration of those libraries easier.
The best way to get started is to have a look at the notebooks folder, especially at the showcase notebook.
The tools included here are used by us at Otto Group BI for our production services, as well as by individual members for machine learning related things, such as participating in Kaggle competitions.
Installation instructions
Using pip:
pip install dstoolbox
There is a conda recipe for those who want to build their own conda package.
Contributing
Pull requests are welcome. Here are some directions:
Tests
To run the tests, you need to install the dev requirements using pip:
pip install -r requirements-dev.txt
or conda:
conda install --file requirements-dev.txt
Next you should check that all unit tests and all static code checks pass:
py.test pylint dstoolbox
Guidelines
Python 3 only.
Code should be re-usable and succinct.
Where applicable, it should be compatible with scikit-learn, pandas, and Palladium.
It should be documented and unit-tested using pytest (100% code coverage desired).
It should conform to the coding standards prescribed by pylint (where it makes sense).
There should be usage examples that cover the most common use cases (the best place would be an IPython/Jupyter notebook).
Don’t add dependencies unless absolutely necessary.
5 maintainers
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