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https://github.com/ashishpatel26/datascienv
datascienv is package that helps you to setup your environment in single line of code with all dependency and it is also include pyforest that provide single line of import all required ml libraries
https://github.com/ashishpatel26/datascienv
catboost data-science data-science-env datascienv imbalanced-data lightgbm matplotlib numpy pandas pycaret scikit-learn seaborn tensorflow2 xgboost
Last synced: 26 days ago
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datascienv is package that helps you to setup your environment in single line of code with all dependency and it is also include pyforest that provide single line of import all required ml libraries
- Host: GitHub
- URL: https://github.com/ashishpatel26/datascienv
- Owner: ashishpatel26
- License: mit
- Created: 2021-10-24T05:43:15.000Z (about 3 years ago)
- Default Branch: main
- Last Pushed: 2021-11-01T11:29:36.000Z (about 3 years ago)
- Last Synced: 2024-09-27T20:22:41.609Z (about 1 month ago)
- Topics: catboost, data-science, data-science-env, datascienv, imbalanced-data, lightgbm, matplotlib, numpy, pandas, pycaret, scikit-learn, seaborn, tensorflow2, xgboost
- Language: Python
- Homepage: https://pypi.org/project/datascienv/
- Size: 229 KB
- Stars: 58
- Watchers: 4
- Forks: 12
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Data Science Environment Setup in single line
# [](https://badge.fury.io/py/datascienv) [](https://badge.fury.io/gh/ashishpatel26%2Fdatascienv) [](https://pypi.python.org/pypi/ansicolortags/) [](https://www.python.org/)
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/
- `tensorflow(latest)` - https://www.tensorflow.org/tutorials
- `flask(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/
#### Installation
* datascience is currently available on the PyPi's repository and you can install it via pip:
```bash
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:
```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
```
* **Warnings:** If you find this type of warning then ignore that warning.
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