Installing Python on OSX (and the necessary modules)

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If you need help installing python on OSX, read on.

For the last three years, I’ve used a mac for all my development. I love the fact that everything ‘just works’ on the platform. That said, when you get into scientific computing and data analytics, especially with python, you can  run into some issues.

Just like linux, python is included with the operating system. Unlike linux, this can cause problems long-term for you due to upgrades and changes that Apple may make to the python ecosystem.

On OS X, I recommend those of you starting out to go with Anaconda or Enthought Canopy.  As I said in “Installing python on Windows“, I prefer Canopy over Anaconda for scientific computing / data analytics but either will work for you.  Installing Canopy on the mac is very similar to installing it on Windows…so I’ll let this post be your guide for installing Canopy.

If you want to get into the nitty-gritty and install and configure python and the modules yourself, you can easily do so, but be prepared to spend some time on the command line.

Before we get started installing python on your Mac, we need to install homebrew, which is a package manager for OS X (it acts similar to the ‘apt’ package manager on ubuntu / debian).

To install homebrew, open a terminal and paste the following:

/usr/bin/ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)"

This command installs the homebrew ecosystem onto your machine and preps your machine to be ready to install various packages, including python.

Installing Python on OSX

Step 1: Let’s get python installed via homebrew.  In your terminal, type:

brew install python

This will install a version of python onto your machine and set up your environment to use that version. This helps mitigate any issues you might have down the road if / when Apple makes changes to the system provided python.   Additionally, brew installs pip into the system to make it easy to get the necessary modules onto your machine.

From this point on, we are generally going to follow exactly the same steps that I outline in Installing Python on Linux except we don’t need to install any additional tools.

Step 2: Not required, but highly recommended – install a virtual environment.  I recommend virtualenv. Install it with this command:

pip install virtualenv

When you are ready to get started on a new project, type the below command to install python into a new virtual environment (the ‘env’ is the name of the environment). You only have to do this once per project. Note: You should use a folder per project to keep your virtual environments separated.

virtualenv env

Whenever you want to work on a specific project, change into that folder and type the following. This will set up your environment with all of your installed python modules:

source env/bin/activate

For the purpose of this walk-through let’s create a new directory, set up a new virtual environment and then install the necessary modules.

  • Create a folder in your home directory called ‘projects’.
  • Type “mkdir projects” to do this from the command line.
  • Change into that folder and then type “mkdir install_example” to create another folder inside the projects folder.
  • Type “virtualenv env” to create your virtual environment.
  • Type “source env/bin/activate” to begin using this environment

Now that we have our environment ready to go, we need to install some of the modules that are most often used when doing data work inside python. These modules are:

The above modules can be installed with one pip command.

pip install pandas scipy scikitlearn statsmodels sympy matplotlib jupyter

You’re ready to start working with python for data analysis on your mac. Just remember, for each virtualenv you create, you’ll need to reinstall these modules if you wish to use them.

Check back here often for more information on using the above modules to actually DO something.

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