This package helps to setup your Data Science environment in single line.
Developed by Ashish Patel(c) 2020.
datascienv is a python package offering a single line Data Science Environment setup.
datascienv is tested to work under Python 3.7+ and greater. The dependency requirements are based on the datascienv package update release:
pandas(latest) - https://pandas.pydata.org/numpy(latest) - https://numpy.org/install/scipy(latest) - https://www.scipy.org/scikit-learn(latest) - https://scikit-learn.org/joblib(latest) - https://joblib.readthedocs.io/en/latest/statmodels(latest) - https://www.statsmodels.org/stable/index.htmlmatplotlib(latest) - https://matplotlib.org/seaborn(latest) - https://seaborn.pydata.org/xgboost(latest) - https://xgboost.ai/sponsorsimbalanced-learn(latest) - https://imbalanced-learn.org/bokeh(latest) - https://docs.bokeh.org/en/latest/Boruta(latest) - https://github.com/scikit-learn-contrib/boruta_pyjupyter(latest) - https://jupyter.org/spyder(latest) - https://www.spyder-ide.org/mlxtend(latest) - http://rasbt.github.io/mlxtend/lightgbm(lightgbm) - https://lightgbm.readthedocs.io/en/latest/catboost(latest) - https://catboost.ai/pycaret(latest) - https://pycaret.org/tensorflow(latest)- https://www.tensorflow.org/tutorialsflask(latest)- https://flask.palletsprojects.com/en/2.0.x/fastapi(latest)- https://fastapi.tiangolo.com/tutorial/kats(latest)- https://facebookresearch.github.io/Kats/keras(latest)- https://keras.io/examples/
- datascience is currently available on the PyPi's repository and you can install it via pip:
pip install -U datascienv- If you prefer, you can clone it and run the setup.py file. Use the following commands to get a copy from GitHub and install all dependencies:
git clone https://github.com/ashishpatel26/datascienv.git
cd datascienv
pip install .- Or install using pip and GitHub:
pip install -U git+https://github.com/ashishpatel26/datascienv.git- Warnings: If you find this type of warning then ignore that warning.
