While following a Learn.co tutorial designed to teach us how much more efficent Numpy is than pain-stakingly writing your own stand-in functions in Python (didn’t have to pursuade me). It was using the timeit.timeit() method to measure two identical large array manipuations against eachother.
Here’s a brief summary of Section 10 of the online data science coursework for Flatiron School:
This post is not concerning Seaborn, but it was inspired by it. I was given this link to a Pokemon themed tutorial and although content covered mostly seaborn I found myself more interested with the data set. The best part about learning new things is that there are so many great discoveries to be made. True, it would have been effective for me to code along with the tutorial, I would have learned a lot about Seaborn. Instead I bookmarked the tutorial and went on my own quest to build the radar charts. Great for comparing stats for the best battlers using this data set of All the Pokemon through generation 6
At 18 years old we are asked “what do you want to do with your life?”, I was still working with that. We graduate college and we ask ourselves, “what do we do now with my life?”, I still had more to learn. After returning from the Peace Corps most volunteers ask themselves “what have I done with my life?!”, and for once I had an answer that resonated. I had found myself.