I personally do most of my coding on my laptop, which is a Mac. Eventually that code gets run on a Linux server, but all initial coding, exploratory data analysis, etc is done on my laptop. And since I advocate for Python, I thought I would lay out all the steps I needed to do to setup my Mac in the easiest manner. (Note: probably similar steps on Windows, but I haven’t used a Windows computer in so long that I don’t know the potential differences).

Unfortunately, the Python 2.x vs 3.x divide exists and so far, I have yet to be able to completely commit to 3.x due to a few packages with legacy issues. Luckily, there is a pretty easy solution below. Note, your Mac has Python preinstalled (go to terminal and type python to start coding…). However, if you want to update any packages, you can quickly run into issues. So it is easiest to install your own version of Python.

1. Install Anaconda (I advocate version 2.7, Anaconda will call this environment root)
2. I recommend using Anaconda Navigator and using Spyder for an IDE
3. Install version 3.5 and make an environment (in Anaconda Navigator or terminal commands below):

   $ conda create -n python3.5 python=3.5 anaconda

4. You can switch between python environments  {root, python3.5}

   $ source activate {insert environment name here}

5. To add new python packages use conda or pip (anaconda has made its own pip the default)
6. WARNING: always close Spyder before using conda update or pip. I got stuck in some weird place where Spyder would no longer launch. Apparently it can happen if Spyder is open and underlying packages get changed.

To get around the 2.x vs 3.x issue, go to your terminal and use pip install for the following packages: future, importlib, unittest2, and argparse. See the package’s website for details of any differences. Then, start your Python code with the following two lines:

from __future__ import (absolute_import, division, print_function, 
unicode_literals)

from builtins import *

For nearly all scientific computing applications, you are essentially writing Python 3 code. So make sure to read the correct documentation!

Personally, I found Anaconda to be a lifesaver. Otherwise, I got stuck in some weird infinite update loop to install all required packages for machine learning (specifically Theano).

Now you are ready to code! If you aren’t familiar with Python, my recommended tutorials will be in a future post.