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Data Science Environment using Conda

This is the Conda virtual environment for the projects in

I recommend you use this environment, as it will contain the exact same versions of libraries I'm using, avoiding the version hell, or Works on my machine syndrome.

To start off, install Anaconda for Python 3. This is what I recommend:

There is a miniconda version for those short of space, but I haven't tried it.


Download the environment.yml file in this directory.

Next, open a command prompt. On Windows, make sure you use the the one provided by Conda. If you go to the

Start menu->All programs - > Anaconda -> Anaconda Command Prompt

and run that.

On Linux, Conda just works in normal bash, last time I tried.

Create Environment

conda env create -f environment.yml

Activate the new environment:

Linux, OS X: source activate data

Windows: activate data

For details, see here:

Now, when you run python or ipython notebook, it will use the data environment's version of Python.

Once you have created the conda environment, the 2nd time around, you don't need to run activate again, as (at least on Windows) Anaconda creates a shortcut to your environment. See the highlighted parts below, Anaconda has created short cuts to the Data environment:


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