If you are messing with data then, you can’t beat a Jupyter notebook as a working environment. Not only are they super easy to use but they are effective ways of sharing your workings and findings.
Even better, the Jupyter project have a created a bunch of Docker containers that contain all the tooling you need which, makes getting up and running as simple as running a two simple commands:
cd <your working directory> docker run --rm -it -p 8888:8888 -v %cd%:/home/jovyan/work jupyter/datascience-notebook
In the console you will then see a URL looking something like
http://127.0.0.1:8888/?token=xxxxxxx. Cut and paste this into your browser and you are away laughing.
For Linux you will need to tweak the
%cd% bit and use
pwd instead so:
cd <your working directory> docker run --rm -it -p 8888:8888 -v $(pwd):/home/jovyan/work jupyter/datascience-notebook
If you need to
sudo to install something you will need to run the container as root and set the GRANT_SUDO environment variable:
docker run --rm -it -p 8888:8888 -u root -e GRANT_SUDO="yes" -v %cd%:/home/jovyan/work jupyter/datascience-notebook
jupyter/datascience-notebook container generally has all you need but if you are doing something specfic, there are a bunch of other options, including ones that contain R, Tensorflow, Scipy and Apache Spark support. See https://jupyter-docker-stacks.readthedocs.io/en/latest/using/selecting.html for more info