datascienv 0.1.1

Data Science package for setup data science environment in single line

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

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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

datasciecne is currently available on the PyPi's repository and you can install it via pip:

pip install -U datascienv

The package is release also in Anaconda Cloud platform:

conda install -c conda-forge 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