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1. INSTALL PYTHON ON MAC USING BREW HOW TO
2. INSTALL PYTHON ON MAC USING BREW MAC OS X
3. INSTALL PYTHON ON MAC USING BREW INSTALL
4. INSTALL PYTHON ON MAC USING BREW DOWNLOAD

As long as you’re at it, you can also add the directory where Python scripts get installed. The brew command and any executables it installs will go in the directory /usr/bin/local so you want to make sure that goes at the front of your system’s PATH.

INSTALL PYTHON ON MAC USING BREW INSTALL

To install Homebrew paste the following in a terminal: ruby -e "$(curl -fsSL )" It builds packages from source, intelligently re-uses libraries that are already part of OS X, and encourages best practices like installing Python packages with pip. Homebrew is my favorite package manager for OS X. If you’ve installed Xcode on Snow Leopard then you already have the command line tools. If you’ve already installed Xcode on Lion or Mountain Lion then you can install the command line tools from the preferences.

INSTALL PYTHON ON MAC USING BREW DOWNLOAD

Head over to /downloads, register for a free account, and download (then install) the latest “Command Line Tools for Xcode” for your version of OS X. These include important things like development headers, gcc, and git. The first order of business is to install the Apple command line tools. If you need other libraries they can most likely be installed via pip and any dependencies can probably be installed via Homebrew. I’ll show how I install Python and the basic scientific Python stack: I do this because Homebrew makes it easier to compile these with non-standard options that work around an issue with SciPy on OS X. These instructions differ from my previous set primarily in that I now use Homebrew to install NumPy, SciPy, and matplotlib. I’m running the latest OS X Mountain Lion (10.8) but I think these instructions should work back to Snow Leopard (10.6). You can always check the Install Python page for other installation options. These instructions detail how I install the scientific Python stack on my Mac.

INSTALL PYTHON ON MAC USING BREW MAC OS X

The Mac OS X backend will not be able to function correctly if Python is not installed as a framework. If you installed python on mac and tried to use matplotlib to make beautiful plots for your papers, you might get into this error RuntimeError: Python is not installed as a framework. Now, you only need to remember to install modules using this slightly different line: python -m pip install MODULENAME -userĭone! Enjoy python on your supercomputer. Note the –user flag, which is good if you can’t sudo (as it was for me on the Caltech supercomputer).

INSTALL PYTHON ON MAC USING BREW HOW TO

So, how to get pip and install modules anyway? The python people have a script precisely for this: wget This was because I was on a supercomputer which already had a python distribution installed and I didn’t want to deal with the scipy dependancies (e.g.

I recently got across the situation that I did not want to use a virtual environment. You have created the box, but you’re still out of it. The virtual environment is physically located in ~/box.

Again, if you screw something up, you can just delete the box and start again. You can have different boxes for different projects, or a single box for all your python stuff.

Python’s virtual environments are kind of separate boxes, where you can install modules and packages locally. The easiest way to safely install python on a MAC is homebrew. Homebrew installs a new version of python (by default the latest 2.x version available) and set is as default. I will also stay with python 2 for now, but see below for some info on python 3 (see also here for more). However, it is advisable to install a local distribution and do your scientific stuff from there… If you screw something up, you can just delete everything and your OS is safe. Most (if not all) unix systems already come with a python distribution installed. This is a step-by-step guide to the best way I found to install and use python for science, the easy (less hackable) way comes first, and the long way comes next. All sort of scientific algorithms are already there (written and debugged!), you just have to use them. Python is cool, especially if you a scientist.