If you already know Python, you can probably skip the next two sections. However, if you need a refresher, or if you haven't done Python before, you'll want to go through these. There are a few quirky things about the Python scripting language that you need to know, so let's dive in and just jump into the pool and learn some Python by writing some actual code.
Like I said before, in the requirements for this book, you should have some sort of programming background to be successful in this book. You've coded in some sort of language, even if it's a scripting language, JavaScript, I don't care whether it is C++, Java, or something, but if you're new to Python, I'm going to give you a little bit of a crash course here. I'm just going to dive right in and go right into some examples in this section.
There are a few quirks about Python that are a little bit different than other languages you might have seen; so I just want to walk through what's different about Python from other scripting languages you may have worked with, and the best way to do that is by looking at some real examples. Let's dive right in and look at some Python code:
If you open up the DataScience folder for this class, which you downloaded earlier in the earlier section, you should find a Python101.ipynb file; go ahead and double-click on that. It should open right up in Canopy if you have everything installed properly, and it should look a little bit something like the following screenshot:
One cool thing about Python is that there are several ways to run code with Python. You can run it as a script, like you would with a normal programming language. You can also write in this thing called the IPython Notebook, which is what we're using here. So it's this format where you actually have a web browser-like view where you can actually write little notations and notes to yourself in HTML markup stuff, and you can also embed actual code that really runs using the Python interpreter.