Okay, so today I’m gonna walk you through this thing I was messing with the other day – getting something running with “elle brooke car park.” It’s a bit of a story, not gonna lie.
First off, I needed to figure out what the heck “elle brooke car park” even was. I started by just looking it up, y’know, a basic search. Turns out, it’s probably some kind of location or name related to parking. I was hoping for something more specific, but hey, gotta start somewhere.
Then I thought, alright, if it’s a place, maybe there’s some public data or an API that has info about parking spots. So I started digging around for open data portals, anything that might list car parks and their details. It was kinda tedious, bouncing between sites, but eventually, I stumbled upon a dataset that looked promising. It had locations, addresses, and even the number of spaces for a bunch of car parks. I was like, “Yes! Maybe I’m onto something.”
So, I grabbed the dataset and started cleaning it up. This was probably the most annoying part. The data was a mess! Missing values, weird formatting, you name it. I used Python with Pandas – gotta love Pandas for cleaning data, right? I spent a good chunk of time just wrangling the data into a usable form.
Next, I wanted to actually do something with this data. I decided to visualize it on a map. I figured, if I could plot all the car parks on a map, I could then highlight “elle brooke car park” (assuming I could find it in the dataset) and maybe show some info about it. I used Folium, a Python library that lets you create interactive maps. It’s pretty cool because you can just drop markers on the map with pop-up info.
I looped through my cleaned dataset, creating a marker for each car park. The pop-up for each marker included the address and the number of spaces. Finally, I added a special marker, a different color, for “elle brooke car park” if I could find it in the dataset. And… bingo! It was there!
I ended up with a map that showed all the car parks in the area, with “elle brooke car park” highlighted. I could click on it and see the address and space count. It wasn’t super fancy, but it was a pretty neat way to visualize the data.
If I had more time, I’d probably try to hook it up to a real-time data source, so I could see how many spaces are available at “elle brooke car park” right now. But for a quick project, I was pretty happy with how it turned out. It just shows you what you can do with a bit of data and some Python skills!
