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.
If you have worked with Pandas for any length of time you have probably come across the need to stick a dataframe together with another dataframe. It turns out it’s not as simple as you might think.
I’m going to show you a couple of ways to accomplish putting one or more dataframes together in Pandas depending on the situation and desired results.
One of the reasons that I was initially attracted to Python is that it is beautiful.
If you’ve never stared at lines of cryptic Perl and scratched your head trying figure out what is even going on, you might not understand this.
If you have never gronked through hundreds of lines of html that works but was written by someone that has never heard of semantics or a validator; I’ll forgive you for thinking my opening sentence is hyperbolic.
For those of you that understand that code is poetry, read on…
Today I got my certificate for the Python for Data Science and Machine Learning Bootcamp on Udemy. This was a great course and I really enjoy the way Jose Portilla teaches.
The class covers topics from Pandas and visualizations through machine learning, cloud computing with PySpark, all the way to TensorFlow. I can now train test split in my sleep 🙂
Hey Now – I am happy to be writing you from the new home of this website – you may have noticed some changes…
Without boring you with the details tripkendall.com has been moved from the hosting provider that shall not be named over to where the other sites are hosted… Hopefully I will get it hooked up to CloudFlare and the remaining issues cleaned up today.
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 ).