Alright, buckle up folks, because I’m about to spill the beans on my little “Jim McElwain and the Shark” adventure. Let me tell you, it was a wild ride from start to finish.
So, where did this all start? Well, I stumbled upon this article talking about Jim McElwain getting bit by a shark. Now, being the curious cat that I am, I wanted to see if there was some interesting data on shark attacks. You know, maybe there’s a pattern, a hot spot, something juicy.
First things first, I needed data. I went straight to the source – Google. I started searching for “shark attack datasets” and wouldn’t you know it, I found a few promising leads. I landed on this site, and downloaded a CSV file that looked like it had a ton of info. Score!
Next up, cleaning the data. Ugh, the bane of every data enthusiast’s existence. I fired up my trusty Python notebook and started importing the usual suspects: Pandas, NumPy, the whole gang. I loaded up the CSV and immediately saw it was a mess. Missing values everywhere, inconsistent formatting, you name it.
I started with the missing values. After taking a closer look, I decided to fill some of the missing location data with “Unknown” since I didn’t want to just drop those rows. Then, I tackled the date column. It was all over the place – different formats, weird abbreviations. I used Pandas to standardize it into a consistent format. Trust me, that alone took a good chunk of time.
Now for the fun part: analysis! I wanted to see where these attacks were happening, so I grouped the data by country and plotted it on a map. Turns out, the US and Australia were leading the pack, which wasn’t too surprising. I also looked at the types of activities people were doing when they got attacked. Surfing seemed to be a common theme, which, again, isn’t exactly breaking news.
I was curious if there was any seasonality to the attacks, so I extracted the month from the date and plotted the frequency of attacks per month. Seemed like the summer months had more activity, probably due to more people being in the water. Obvious, but hey, gotta confirm the hypothesis, right?
But here’s where it got interesting. I decided to dig into the “species” column to see what kind of sharks were involved. This was a total free-for-all. The data was super inconsistent, with different names for the same shark, misspellings, you name it. I had to do some serious cleaning and grouping to make sense of it. I ended up creating categories like “White Shark,” “Tiger Shark,” “Bull Shark,” and “Other.”
After all that wrangling, I could finally see which shark species were the most frequent offenders. Surprise, surprise, White Sharks were at the top of the list. Then I cross-tabulated species and activity to see if certain sharks were more likely to attack people doing specific things. No earth-shattering revelations, but it was cool to see the data visualized.
In the end, did I find anything groundbreaking about Jim McElwain or shark attacks? Not really. But I learned a lot about data cleaning, analysis, and visualization. Plus, I got to spend a few hours geeking out with a dataset, which is always a win in my book.
The biggest takeaway? Data is messy, and cleaning it is half the battle. But once you get through that, you can uncover some pretty interesting insights. And who knows, maybe one day I’ll find something truly mind-blowing in a dataset. Until then, I’ll keep digging!
