Installing python on Windows

If you’ve done any work with python on Windows, you may be cringing right now at the thought of trying to do any type of python development work on the platform.  Have no fear though…there is hope for python developers on Windows, especially if you are only going to be using python for data analysis, machine learning, etc and not doing any major web development work (with flask, django, etc). In this post, I describe the steps necessary for installing python on Windows.

There’s really only one method for using / installing python on windows that is convenient and works for 99.9% of the people on Windows who are focused on scientific computing — downloading Enthought Canopy orAnaconda and installing it. For those of you getting started with data analytics, Canopy gets you started faster and makes it very easy to get modules like panda, numpy, scipy, etc installed and configured (in most cases, these are already installed when Canopy is installed).

For those of you running on Mac or Linux, you can also install Canopy for your platforms. I personally don’t use Canopy on the Mac or Linux platform, but only because I prefer to manage things a bit differently on those platforms. There’s nothing wrong with using Canopy on Mac or Linux, I just prefer not to.

Installing Python on Windows using Canopy

For the purposes of this post, we are going to install Canopy(accurate as of November 2016).

  • Step 1 – visit the Enthought Canopy website and click the “Get Canopy” button.
  • Step 2 – select the “download” option for Canopy Express – FREE. This lets you get the platform without paying any additional money. If you are going to be using Canopy for heavy duty scientific work, I’d recommend buying one of their subscriptions since you get more modules, etc to work with. If you are a student or work in academia, you can ask for an academic license for free.  Note – Direct link for downloading Canopy Express.
  • Click the “Download Canopy” button. A web form will pop up asking for information…you can ignore that. Your download has started. Note: Canopy is available in 64-bit and 32-bit versions. I recommend the 64-bit if you are on a modern computer / operating system.

 

Installing python on Windows - Canopy Download

  • Once your download completes, run the executable to begin the installation process. A wizard will be displayed…hit “next” through the wizard and install the software. Once installation is complete, the final screen (see below) will have a ‘finish’ button and a ‘Launch Canopy when setup exits” checkbox. Leave the checkbox selected and click “finish” to complete the installation and launch Canopy.

Installing python on Windows - Canopy Final Installation Screen

  • The first  time you run Canopy, you will be presented with an ‘environment’ window (see below).  You can leave this at the default or select another location to store your environment information. I suggest leaving it at default to begin with. Click “Continue” to begin using Canopy.

Installing python on Windows - Canopy Environment Window

  • The first time you load Canopy, it will take some time to load the various modules into memory and setting up your Canopy environment. Each time after this first start, the platform should load up fairly quickly.
  • Once Canopy completes loading your environment for the first time, you’ll be asked if you want to make Canopy your default Python environment. Select “Yes” and click “Start using Canopy”.  If you select “no”, you will have to do a some manual configuration to begin using Canopy.

Installing python on Windows - Canopy Default Python

  • When Canopy starts, you’ll see the window below.

Installing python on Windows - Canopy Start Page

  • You now have Canopy installed and ready to go.  To start programming, click the ‘Editor’ button and Canopy will load up an editor to you can begin work. Below is a screenshot of the editor window.

Installing python on Windows - Editor Window

Check out the other posts on this website for more information on how to get started actually DOING something with python.

Data Analytics & Python

data analytics & pythonSo you want (or need) to analyze some data. You’ve got some data in an excel spreadsheet or database somewhere and you’ve been asked to take that data and do something useful with it. Maybe its time for data analytics & Python?

Maybe you’ve been asked to build some models for predictive analytics. Maybe you’ve been asked to better understand your customer base based on their previous purchases and activity.  Perhaps you’ve been asked to build a new business model to generate new revenue.

Where do you start?

You could go out and spend a great deal of money on systems to help you in your analytics efforts, or you could start with tools that are available to you already.  You could open up excel, which is very much overlooked by people these days for data analytics. Or…you could install open source tools (for free!) and begin hacking away.

When I was in your shoes in my first days playing around with data, I started with excel. I quickly moved on to other tools because the things I needed to do seemed difficult to accomplish in excel. I then installed R and began to learn ‘real’ data analytics (or so I thought).

I liked (and still do like) R, but it never felt like ‘home’ to me.  After a few months poking around in R, I ran across python and fell in love. Python felt like home to me.

With python, I could quickly cobble together a script to do just about anything I needed to do. In the 5+ years I’ve been working with python now, I’ve not found anything that I cannot do with python and freely available modules.

Need to do some time series analysis and/or forecasting? Python and statsmodels (along with others).

Need to do some natural language processing?  Python and NLTK (along with others).

Need to do some machine learning work? Python and sklearn (along with others).

You don’t HAVE to use python for data analysis. R is perfectly capabale of doing the same things python is – and in some cases, R has more capabilities than python does because its been used an analytics tool for much longer than python has.

That said, I prefer python and use python in everything I do. Data analytics & python go together quite well.