Alright, so I decided to dive into some NBA data today. It was kinda spur-of-the-moment, I just wanted to see if I could answer some random NBA questions using, you know, data stuff.
First, I needed a place to start. I mean, I can’t just magically pull NBA facts out of thin air. So, I spent some time looking around. I needed a good, clean dataset. I wanted something with, like, player stats, game results, maybe even some team history.
Finding the Data
This part was a bit of a pain, to be honest. There are tons of websites with NBA stats, but getting the data in a usable format? That’s a whole other story. I stumbled around a bit, hitting a few dead ends.
- First attempt: Some websites had tables, but they were all messy and jumbled. Copy-pasting was out of the question.
- Second try: I found some APIs, but they were either too complicated or wanted me to pay. Nope.
Eventually, I hit upon this CSV. Perfect! Nice, clean columns, lots of data. I downloaded it, feeling pretty good about myself.
Cleaning and Exploring
Of course, the data wasn’t perfectly clean. There’s always something, right? I opened it up.
I noticed a few things that needed fixing:
- Some missing values – blank cells where there should have been numbers. I decided to just fill those with zeros, figuring it wouldn’t mess things up too much.
- Weird date formats – I converted those to something more standard, so I could actually work with them.
Once the data was cleaned up, I started just playing around. I calculated some basic stuff: average points per game, total rebounds, things like that. Just to get a feel for the dataset.
Answering Questions
Now for the fun part! I came up with a few questions I wanted to answer:
- “Which player had the highest average points per game?”
- “Which team had the most wins in a particular season?”
- “Which player had the most 3 points in the season?”
I wrote some code to answer these. It wasn’t anything fancy, just some basic filtering and sorting, to get the answers I was looking for.
It was pretty cool to see the answers pop up. I felt like a real data detective, uncovering hidden insights from the numbers.
So, that’s basically it. I took some raw data, cleaned it up, and used it to answer some simple NBA questions. It was a fun little project, and it definitely made me want to explore data analysis more in the future. Maybe next time I’ll tackle something even more challenging!
