If you are reading this blog then you are most likely aware that I’ve been collecting information from across the web since the first of the year. I’ve been manipulating and massaging said data to glean insights from it.
Today I am posting, for the first time, my research into what are the most popular or influential brands online.
One of the things that my bots are tracking, in my data collection / data analysis efforts, are the places where news is happening.
I am tracking mainstream and alternative news sources as well as keeping a good mix of left and right news sources. I’m calling the results: Where It’s At.
Every morning my bots gather information about news from across the web. I gather meta news if you will…
I do a number of different things with this data, but it all boils down to turning that raw meta data into information. One of the first functions I wrote was:
One of my many personal Python projects involves sending my bots out to scour the web’s most popular news sites. I am looking to gather, track, and log all kinds of data and turn that data into information…
I’ve discovered that tracking who is in the news – and how often they are mentioned in various stories and posts – gives one a good taste of who are the most news-worthy people at any given time.
If you were trying to come up with a list of iconic programming idioms, “For Loops” would probably make the cut. For someone like me that learned about loops via COBOL’s crazy “Perform” loop syntax – changing paradigms (yet again) can sometimes prove difficult.
I was attracted to Python initially because it is different. So I have done my best to leave my preconceived notions about writing code behind as I embraced the challenge of becoming an effective Python coder.
List Comprehension proved to be a concept that I had to invest more time into studying than other Pythonic approaches.