Okay, so here’s the story about how I messed around with some 1985 baseball data. It was a fun little dive, and I learned a bunch.
First, I grabbed a dataset about the 1985 baseball season. Just googled around until I found a decent CSV file. Nothing fancy, just the usual stats – team names, wins, losses, all that jazz.
Then, I fired up my Python environment. You know, the usual Pandas and Matplotlib setup. I imported the CSV using Pandas. Took a peek at the data, printed the first few rows to make sure everything loaded correctly. Always gotta do that, right?
Next, I started messing around with the data. I wanted to see who the top teams were. So, I sorted the DataFrame by wins and printed the top 5. Classic stuff. Then, I wanted to find out who had the worst season. So, I just sorted it the other way and, again, printed the bottom 5. Man, some teams really had a rough year!
But just looking at numbers is boring. I wanted to visualize things. So, I created a bar chart of wins for each team. Used Matplotlib for that, of course. Labeled the axes, added a title – the whole nine yards. It looked pretty cool, actually. You could easily see the best and worst teams at a glance.
I got a bit more ambitious then. I decided to compare wins and losses. I created a scatter plot with wins on the x-axis and losses on the y-axis. Then, I added a line representing the average win/loss ratio. That was a bit trickier, had to calculate the average first, then plot the line. It showed how teams performed relative to the average.
I even tried to calculate some basic stats like batting average and ERA (Earned Run Average), but I quickly realized the dataset wasn’t detailed enough. It only had team-level data, not individual player stats. Bummer. That’s a reminder to always check what data you actually have before you start trying to do fancy calculations.
Lastly, I saved the plots as PNG files. Just because. Maybe I’ll use them for something later, or maybe they’ll just sit on my hard drive gathering dust. Who knows?
Learnings from this Adventure
- Always inspect your data after loading it. Head(), tail(), info() – use them all!
- Visualization makes data way more understandable. Bar charts and scatter plots are your friends.
- Double-check the data fields available before trying to calculate complex stats. You might be missing something.
- Saving plots is always a good idea. You never know when they might come in handy.
Overall, it was a fun little project. Nothing groundbreaking, but it was a good way to practice my data analysis skills. Plus, I learned a little bit about the 1985 baseball season. Go [insert winning team name here]! (Even though I had to google who won… I’m not a huge baseball guy, haha).