Saturday - February 22, 2020
Today, I am going to be live streaming some Python exercises from my computational analytics class from my master's program in data analytics from Georgia Tech. This will be my first stream, okay, well technically my second, but now I know what I'm doing. I'll be using OBS and streaming to Twitch. Hope you enjoy! I'll document any thoughts on the streaming or the homework on this post and hopefully post to Git as well. Here we go! Enjoy.
Today's topic is module 7: tidying data. Let's open the homework and see what we got. I don't think I'll watch this week's lectures as I'm pretty proficient in Pandas already. I guess I'll reference to it if I get stuck.
I got busy this week with work...and this homework is actually due tomorrow night. Going to see how much I can get done. My wife and puppy are already asleep; I'm in bed with them. The lighting is a bit strong from my computer. I use this app called flux to change how my screen looks. This DarkMode is a lot easier on my eyes, and makes coding look a lot cooler IMO.
This computation analysis class is all Python based. The primary way this class is executed is through these Jupyter Notebook's. Jupyter Notebook's are a great way to chunk code up and complete individual tasks at a time with getting live feedback on what your code is doing. We also use this system called Vocareum which I don't know a lot about. But it uses automatic testing cases to test my work. I can submit as much as I want as well which is nice.
Some friendly reminders from this module:
- Tidy Table = Tibble
- Joins:
Inner-join (A, B) (default): Keep only rows of A and B where the on-keys match in both. Outer-join (A, B): Keep all rows of both frames, but merge rows when the on-keys match. For non-matches, fill in missing values with not-a-number (NaN) values.
Left-join (A, B): Keep all rows of A. Only merge rows of B whose on-keys match A.
Right-join (A, B): Keep all rows of B. Only merge rows of A whose on-keys match B.
- Data Frame Column applies is an efficient way to manipulate columns of frame
* I'll be honest, I don't use df['col'].apply() very often and I'm being asked to use it to do something I'm not sure what to do; use it on two columns. I'll google in search of truth.
Sorry folks, not my greatest Google session ever. I've been up since 5 AM and am really feeling it. Wow, that took me much longer than I would've liked it to. But bam! Done with that problem. On to the next.
I'm always amazed at how useful and how powerful Pandas is. It's truly incredible.
"Unlike Pandas data frames, tibbles conceptually do not have row labels. So you should ignore row labels." That's new; didn't know that!
Alright. That's all the energy I have for today. I didn't get very far. I'll have to finish tomorrow. Bye!
Saturday - February 22, 2020
Alrighty, my wife is taking a nap so let's do some of my computational analysis hw that is due tonight. Welcome back! I think the last live stream I did wrong (again) because I didn't have the sizing set up well. Hopefully this one works better! Vamanos!
I'm getting owned by this exercise. I need to go watch the video lecture to see if I can understand how to address this better. Logging off streaming for now.