Alright, so, today I’m gonna walk you through this little project I tackled – basically messing around with data related to the Maori rugby team. Nothing too fancy, just wanted to see what I could do.
First off, I grabbed some data. Found a few okay-ish sources online, nothing super clean, you know how it is. Mostly Wikipedia and some sports stats sites. Had to do a fair bit of cleaning in Excel, the usual stuff – removing weird characters, standardizing date formats, that kind of headache. I saved it all as a CSV. Figured that’d be easiest to work with.
Next, I fired up Python. Yeah, yeah, predictable, I know. But Pandas is just too good for this kind of stuff. So, imported Pandas, read in the CSV, and started exploring. Printed out the first few rows, looked at the column names, that whole shebang. Just trying to get a feel for what I had.
Did some basic analysis. Like, how many games have they played in total? What’s their win percentage? Stuff like that. Really simple stuff, just using Pandas built-in functions. Grouped the data by year to see how they’ve performed over time. Nothing groundbreaking, but it was cool to see the trends.
Then I thought, “Hey, let’s make this a bit more visual.” So, pulled in Matplotlib. Created a few basic charts. A line graph showing wins and losses over the years. A bar chart comparing wins against different opponents. Again, super simple, but it helped tell the story a bit better.
I wanted to get a little fancier, so I looked into making a map. I wanted to see where they played most of their games geographically. Found a library called GeoPandas, which is basically Pandas but for geospatial data. Turns out, it’s a bit of a pain to get set up correctly, especially with all the dependencies. But after some fiddling, I got it working.
I merged the data with some geographical information I found online. And then I plotted the games on a map, using different colors to represent wins and losses. It looked pretty cool, actually. You could see where they had strongholds and where they struggled.
Wrapped up by saving all the charts and analysis into a little report. Just a simple Markdown file with some headings and the charts embedded. Nothing too polished, but it gets the job done.
Learned a few things along the way. GeoPandas is powerful, but definitely has a learning curve. Data cleaning is always more time-consuming than you think. And sometimes, the simplest visualizations are the most effective.
It was a fun little project. Might try something more complex next time, maybe involving some machine learning to predict game outcomes. But for now, I’m happy with what I’ve got.
