In 2008, Nate Silver came within one percentage point of perfectly predicting the popular vote results of the Presidential Election. In 2014, Silver’s team at FiveThirtyEight recorded exactly how many clouds Bob Ross painted over the course of his show’s 31 seasons.

FiveThirtyEight is not just a crack team of data-driven journalists. They’re also caretakers of a treasure trove of data readily available for our consumption on GitHub. Using Python, we can easily parse through the data to find nuggets of wisdom on everything from Bob Ross, to political polls, to how to break FIFA.

What You’ll Need

You’ll need Python 2 or 3 for this tutorial. I’m using Python 2.7.

You’ll also need Pandas (not the black and white fluffy bears, but the Python friendly kind)

Employing The Help of Pandas

Python, along with Pandas, is going to do a lot of our heavy lifting. The pandas data analysis library gives us a very easy, intuitive way to sort through our csv data, straight from the command line. Once we get set up, we can name what we’re looking for, and get a ton of information with simple commands.

Examining Mission Critical Data — How Many Bushes Did Bob Ross Paint?

Fire up your terminal and create a file called FiveThirtyEight.py

Next, let’s get a python environment going. Type the command python

We need to create an environment fit for pandas. Let’s run the following command: