Installing Python for Scientists and Engineers (and maybe Mathematicians too)

Any platform (Mac/Windows/Linux)

At this point in time, I believe that it is very clear that the hands-down winner for installing Python as a novice is the Anaconda distribution by Continuum Analytics. Their website has plenty of information that you can peruse for getting an installation of everything you will need. The CEO, Travis Oliphant, is widely credited for molding Python into a computational language through his development of Numpy, which became the basis for so much more. While arguments are sometimes made that using a Continuum Analytics is insufficiently "pure" for the open source community, I strongly disagree. [1]

Other available distributions, that I don't personally have experience with, are

1. Active State Python is a Commercial distribution with a "community edition". Active State is a solid supporter of the broader community by being a sponsor of the Python Software Foundation.

2. Enthought Canopy Python. Reviews for this version of Python seem not to be as good as those for the Anaconda version. Enthought was slow to move to Python 3, and it may still not be here (I say here, because we are here!). I don't see the point of a new user starting on a language being phased out (although it seems old languages never die).

3. Python(x,y) I'm not a fan of this as it is still not using Python 3. Due to inertia and some of the revolutionary improvements to Python 3, many with codebases have been slower to adopt Python 3 (now at 3.6!). For those doing scientific computing, the new features now clearly motivate one to start with Python 3. Overcoming legacy inertia can be a substantial challenge, though, so I don't judge anyone stuck on 2.7.

Windows

One the neatest distributions fits wholly on a thumb drive. The WinPython is completely portable an can can be moved to any location (local, network, USB drive). This means you can take it with you, give it to a group of people pre-configured, distribute it over a network, etc. It can be completely "non-invasive", meaning you don't need to even uninstall it. It's not even on your hard drive. This is a great tool for anyone who has to work at different computers on a regular basis.

Mac

I personally have had luck using MacPort, but just to get Python running, it's a bit much. Other package managers for free software exist, including Fink, Homebrew. These all do more than you are likely interested in, and choosing one is far from a simple task (I've use each at one time or another, and they all seem to work fine.)

That said: If you want to install Python and run Python today, use the Anaconda distribution.

Linux

There are really two types of Linux users:

1. I want a cheap operating system, or one that will still run fast on old hardware
2. I'm hard core. I would program in assembly language but I don't have enough time.

If you are type 1, install the Anaconda. If you are type 2, God bless you, and good luck. You may have luck using the linux package managers (specific to your distribution), but I will simply wish you well. You're not my target audience.

Government labs

I've a bit of experience here, but not with Python explicitly, and not in any way that would make me worth kidnapping.

Working with anything in a SCIF can be daunting. If you work in one, you know that. If you don't, you only are reading this to get a bit of help convincing your tech people that you should be able to get this installed.

The big argument about open source software is that "anybody" can embed code in the software, risking the security of your systems.

Yup. That's true. However, it's also irrelevant. Here's why. Every single unix system contains open source software on it. Yes, that includes the Mac, which comes with an old underwhelming version of Python. Windows is gradually becoming more open source as well. Why? Because over time we've begun to realize that closed source software is more vulnerable than open source software because vulnerabilities are found faster, and there are more good guys looking than bad guys. The beauty in open source software is that you, the user, can look for vulnerabilities yourself. You may not be qualified, but surely your IT person is (unlikely, but they often act that way by espousing security mantras that have been refuted.).

What about the risk of foreign nationals embedding code in the software? Well, couldn't that be happening to Windows/MacOS/Linux right now? Do you think they don't have programmers oversees working on their operating systems? Call tech support and find out. Commercial packages that are core also have foreign nationals working on them, certainly many on H1B visas. This is also a red herring. The problem with security is it's never as easy as the security people try to tell you it is with platitudes of rules and more rules. Most real vulnerabilities are of the human type, phishing for example. It's getting the door ajar that is the real threat.

So, I'm not an expert in Cyber security, but security people shouldn't make it easy for me to prove them wrong. However, this document won't convince them. So, instead, point out that there is a process already in place to get open source software in a secure environment: get commercial support. Further, you can have them contact other SCIFs and ask them how they are using open source. It's inconceivable that Lawrence Livermore is writing open source software and not using it where it needs to be used. What may actually convince some of them is that open source software is actually safer than commercial software. This is illustrated by the Coverity Scan report which shows that Open source software had a defect density of 0.61, compared to commercial software’s 0.76. [2]

That said, there are valid reasons not to use a dynamic language for code that must be secure. However, one has to ask: is this what's really going on? Are you using Python for analysis, or for delivering code? Those are two different issues that very well may lead to two different security responses.

To specifically address at least one instance: Enthought Canopy at the United States Air Force Research Lab is already approved and ready to be installed on almost any network including RDT&E, enclave networks, and AFNet, but don't trust me. Look it up yourself!

Questions?

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