What I'm watching on YouTube

What I’m Watching on YouTube

I love Netflix.  I am sure that there are people close to me that got very tired of my Netflix obsession.  A few months ago Netflix’s original shows, business model, debt, etc. were a dominant theme in my conversations.    I think that Netflix knocking off long time Emmy kingpin HBO this year (in number of Emmy nominations) somewhat vindicates my effusive Netflix praise.

But I have faced up to a surprising fact:  I watch more hours of YouTube than anything else.   Whether working at my desk, using the elliptical machine, or cleaning house – there is a good chance YouTube is streaming something to me.  I usually watch or listen to a few hours of  the JRE via YouTube each week, I watch multiple history shows, handyman videos, stream music – I could go on…  Suffice to say that if I am by myself and watching a screen, it’s a good bet it’s YouTube.


2018 Brand Heat Check

Once I learned Python and Pandas I tried to come up with unique and creative ways to use them.  The Baller Index is a good example of what I am talking about.  Throwing new tools in the mix opened up more possibilities, and one of the best things about Python is that there are allot of tools for it.  For this project I learned Beautiful Soup and Selenium. This allowed me send my bots out, across the interwebs, to get the data I was looking for myself before feeding it to Pandas.

I collected data from Twitter, Youtube, Wikipedia, Kaggle, Google News, Tumblr, TechCrunch, Engadget, Forbes, Google Trends, and the independent.co.uk.  I parsed this data to get it into a consistent format conducive for manipulating with Pandas data frames.  I finally got it into a state where I could create a snapshot of the hottest brands on the internet based on brand saturation ( size ) and trendiness ( heat ).