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The boilerplate and utilities for Data Science in Python3

JavaScript 19.69% Jupyter Notebook 79.73% HTML 0.58%

data-urchin's Introduction

The boilerplate and utilities for Data Science in Python3.

Features

  • Python3
  • Hand-picked, de facto standard Python libraries
  • Use IPython Notebook
  • Time-proven practices in Initium Lab
  • Boilerplates, sample codes, cheat sheets, quick hacks, ...
  • [TODO] Integration with production-ready Javascript libraries
  • [TODO] Workflow for Continuous Deployment of mining results

Usage -- Docker Compose

  • docker-compose up.
  • Visit port 8888 on docker-machine ip XXXX to operate the notebook.
  • Be default, this current dir is mapped into the container as working folder. You can pass code/data in directly.

Usage -- Docker

Get the docker image

Choose either way:

  • docker pull hupili/urchin
  • docker build -t hupili/urchin .

Run The Notebook

docker run -v urchin:/app -p 8888:8888 -it hupili/urchin ipython notebook --ip 0.0.0.0

You can find the volumen on your docker-machine in following folder: /var/lib/docker/volumes/urchin/_data

Usage -- Plain Format

Initial setup:

  • Fork and Clone this repository
  • Instal Python3
  • virtualenv -p python3 venv or pyvenv venv
  • source venv/bin/activate
  • pip install -r requirements.txt

Following usage:

  • source venv/bin/activate (save time if you use virtualenvwrapper. .venv is configured)
  • Use ipython notebook to launch the environment
  • Copy any interesting stuff from boilerplates to the root and hack away

Sample pages

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