Three years ago I wrote a series of tutorials for setting up Python/Django on Windows.
Despite taking great pains to make it all work and then meticulously documenting the details, I abandoned that idea in favor of an Ubuntu VirtualBox soon after those posts went live. It’s a long story, but at some point you need to cut your losses and stop throwing good time after bad.
But this summer marks a return to Windows. I decided our data intern should learn Python or R, so he can experience a world beyond the proprietary stats packages they use at colleges. We decided on Python and decided that Windows is the best option; no need to add Linux to his already long list of things to pick up.
To test things out for him, I crossed my fingers and installed Enthought Canopy, a canned Python environment for data viz and analysis, hoping it would take away the pain of installing Python packages on Windows. For the most part, it did.
Canopy (which has a free version) makes it easy to get up and running quickly. If you’re getting started with Python data analysis, use it, and don’t spend hours of your life installing all the packages yourself. That way lies madness.
That said, some of the latest and greatest Python data viz packages aren’t included in the Canopy distribution. If you want to learn those, you’ll have to install them yourself, which is where things can go awry. For example, if you’re on Windows and the package you’re installing needs a 64-bit C compiler, you have to follow these 6 simple steps to get one: http://springflex.blogspot.com/2014/02/how-to-fix-valueerror-when-trying-to.html
The Python data ecosystem is extremely compelling, but there’s still too many barriers for a beginner to jump right in, especially on Windows.