Data Science Environment Setup in single line

This package helps to setup your Data Science environment in single line.

Developed by Ashish Patel(c) 2020.

datascienv

datascienv is a python package offering a single line Data Science Environment setup.

Installation

Dependencies

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.html
• matplotlib(latest) - https://matplotlib.org/
• seaborn(latest) - https://seaborn.pydata.org/
• xgboost(latest) - https://xgboost.ai/sponsors
• imbalanced-learn(latest) - https://imbalanced-learn.org/
• bokeh(latest) - https://docs.bokeh.org/en/latest/
• Boruta(latest) - https://github.com/scikit-learn-contrib/boruta_py
• jupyter(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/

Installation

• 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.