Matplotlib, scikit-learn, and pandas
June 11, 2018
Besides launching this site, in the past month or so I've mostly been heads down in learning matplotlib, scikit-learn, and pandas --> all via Jupyter Labs in Python. That's a mouthful. I could have just said data science tools. Unfortunately, life has been busy too and the trade off hasn't allowed as much time as I'd like.
Matplotlib is a library used to visualize data. It's old (relatively). It has some boiler plate. It's diverse. Based on what I've seen, it sure seems customizable. Although there are quite a few other tools, I'm taking the advice of others and making sure I have a foundation in this workhorse tool that has been a staple for some time. Here is an excellent article on other python visualization tools. I'm planning on diving into altair sometime soon.
I timed getting into this library pretty well I think! It's mentioned in most any conversation of python and data science. I'm glad I waited until I read up on some statitics before I started playing around with this library. When one is armed with the correct knowledge (aka they understand some of the underlying concepts of what the library does) - it seems to take care some of the simple yet tedious parts of creating statistical methods.
Comfort level with this library is increasing and that makes me so jazzed! I am racing ahead of myself and imagining all the neat data wrangling I can potentially do with pandas. My Ford F-150 Gas Log data set is ripe for some data cleaning and then manipulating - I can't wait to apply pandas to it.
All my pursuits so far have been learning these libraries via tutorials. It'll be that way a little longer, and I'm hoping when I dive into my own applications soon I'll have climbed a substantial part of the learning curve due to what I'm learning now.