Alright, let me walk you through how I tackled that Yankees vs Red Sox match player stats project. It was a fun one, actually.
First things first, I started by figuring out where to grab the data. No use in coding if you ain’t got the goods, right? I poked around some sports stats websites, looking for APIs or even just well-structured HTML I could scrape. Found a decent one that had the raw player stats for each game. It wasn’t perfect, but good enough to get started.
Next up, I fired up my Python environment. Gotta have my tools ready! I installed the usual suspects: `requests` for grabbing the data, `BeautifulSoup` for parsing the HTML (if needed), and `pandas` for wrangling the data into something usable. I’m telling ya, `pandas` is a lifesaver.
Then came the fun part: writing the script to actually fetch and parse the data. This is where I spent most of my time. The website’s HTML was a bit messy, so I had to write some custom BeautifulSoup selectors to get just the stats I needed. It was a lot of “inspect element” in the browser, copying selectors, and testing them in my code. Rinse and repeat. I focused on getting the key stats: player name, hits, runs, RBIs, and maybe a few others.
Once I had the data in a workable format, I dumped it into a pandas DataFrame. This made it way easier to clean up and analyze. I handled missing values, converted data types (making sure numbers were actually numbers!), and generally tidied things up. You know, the usual data cleaning stuff. It’s never pretty, but it’s gotta be done.
After that, I started messing around with the data. I calculated some basic stats, like batting averages and total RBIs for each team. I even threw in some simple visualizations using `matplotlib` to get a quick overview of the top performers. Nothing fancy, just some bar charts and maybe a scatter plot or two.
Finally, I saved the processed data to a CSV file. Just so I could easily access it later or share it with someone else. I also wrote a little summary report with the key findings, highlighting the top players and any interesting trends I spotted. It’s always good to have something to show for your work, right?
Honestly, it wasn’t a super complex project, but it was a good exercise in web scraping, data cleaning, and basic data analysis. Plus, I got to learn a bit more about baseball stats along the way. It’s all about learning by doing, ya know?
Learnings:
- Web scraping can be a pain, especially when dealing with poorly structured HTML.
- `pandas` is your friend for data wrangling.
- Data cleaning is crucial for getting accurate results.
- Visualizations can help you understand the data better.
That’s pretty much it. Hope that gives you a good overview of how I approached the project!
