Metadata-Version: 2.1
Name: datascienv
Version: 0.1
Summary: Data Science package for setup data science environment in single line
Home-page: http://github.com/ashishpatel26/datascienv
Author: ashishpatel26
Author-email: ashishpatel.ce.2011@gmail.com
License: UNKNOWN
Description: 
        # 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
        
        datasciecne is currently available on the PyPi's repository and you can install it via pip:
        
        ```bash
        pip install -U datascienv
        ```
        
        The package is release also in Anaconda Cloud platform:
        
        ```bash
        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:
        
        ```bash
        git clone https://github.com/ashishpatel26/datascienv.git
        cd datascienv
        pip install .
        ```
        
        Or install using pip and GitHub:
        
        ```bash
        pip install -U git+https://github.com/ashishpatel26/datascienv.git
        ```
Keywords: pandas,numpy,scipy,matplotlib,seaborn,scikit-learn,statsmodels,pyforest,pycaret,jupyter,xgboost,imbalanced-learn,bokeh,Boruta,spyder,mlxtend,lightgbm,catboost,Data Science,Package,Data science environment setup package,Package,datascienv
Platform: UNKNOWN
Classifier: Development Status :: 1 - Planning
Classifier: Intended Audience :: Developers
Classifier: Programming Language :: Python :: 3
Classifier: Operating System :: Unix
Classifier: Operating System :: MacOS :: MacOS X
Classifier: Operating System :: Microsoft :: Windows
Description-Content-Type: text/markdown
