Alright folks, let’s dive into something I messed around with recently – figuring out the scores from the Vienna Open. It was a bit of a rabbit hole, but hey, that’s half the fun, right?
First off, I started by trying to find a clean, downloadable dataset. You know, something I could just load into a spreadsheet and start poking around. No such luck! The official website had the results, but it was all HTML tables scattered across different pages. Ugh.
So, Plan B: web scraping. I fired up Python and used BeautifulSoup to parse the HTML. I had to inspect the page source carefully to figure out the right tags and attributes to target. Turns out, the tables were pretty consistently structured, which made things a bit easier. I wrote a script to loop through each tournament section (men’s, women’s, etc.) and extract the player names, scores, and other relevant details.
The scraping part was actually the easiest bit. The real pain came with cleaning the data. Names were sometimes inconsistent, scores had weird formatting, and there were all sorts of little quirks. I spent a good chunk of time writing Python code to normalize the names (handling different capitalizations, nicknames, etc.) and convert the scores into a consistent numerical format. I also had to deal with missing values – some players didn’t have all their scores listed, so I had to decide how to handle those (I ended up imputing them with the average score for that round).
Once the data was relatively clean, I loaded it into Pandas and started doing some basic analysis. I wanted to see things like the average score per round, the distribution of scores across different player categories, and who the top performers were. I also tried to identify any interesting trends or patterns in the data, but honestly, it was mostly just what you’d expect – the top players consistently scored well, and the scores tended to get tighter as the tournament progressed.
I even played around with visualizing the data using Matplotlib and Seaborn. I made some scatter plots of score vs. round, histograms of score distributions, and box plots to compare the performance of different player groups. Nothing earth-shattering, but it helped me get a better feel for the data.
One thing I learned is that web scraping is never a one-and-done thing. Websites change, data formats evolve, and you always have to be prepared to adapt your code. I had to tweak my script several times as I encountered new issues. It’s a constant game of whack-a-mole.
In the end, I didn’t discover any groundbreaking insights about the Vienna Open. But I did get some good practice with web scraping, data cleaning, and basic data analysis. Plus, it was just a fun little project to sink my teeth into. Maybe next time I’ll try to build a predictive model to forecast the tournament results… who knows!
