Alright, let’s talk about this “expensive zip codes in us” thing. I kinda stumbled into this while I was messing around with some real estate data – you know, just poking around, seeing what’s what.
So, first thing I did was grab some data. I found a couple of datasets online, one with zip codes and median home prices, and another with some demographic info. Nothing too fancy, just CSV files.
Next up, cleaned that data up. You know how it is, missing values, weird formatting, all that jazz. I used Python with Pandas, mostly because it’s what I’m most comfortable with. Took a bit to wrangle everything into shape, but got there eventually.
Okay, here’s where it got interesting. I sorted the zip codes by median home price. Simple enough, but seeing those numbers, man, some of those places are crazy expensive. I mean, who can afford that stuff?
Then, I thought, “Okay, let’s see what’s going on in these places.” So I merged the price data with the demographic data. Wanted to see if there were any common threads – high income, low population density, whatever.
After that, I started poking around with visualizations. Made some scatter plots, bar charts, you name it. Tried to see if anything jumped out. And some things did. Like, a lot of these expensive zip codes are near big cities, or in areas with really good schools. No surprise there, I guess.
But it wasn’t just about the numbers. I actually looked up some of these zip codes on Google Maps. Wanted to get a feel for the area. Some were exactly what I expected – fancy mansions, manicured lawns. Others were a bit more surprising – older neighborhoods with a lot of character, but still super pricey.
And finally, just for fun, I compared the most expensive zip codes to the least expensive ones. The difference was just… staggering. It really highlights the inequalities that exist in our society. Makes you think, you know?
- Data Acquisition: Found and downloaded the datasets.
- Data Cleaning: Used Pandas to clean and format the data.
- Sorting: Sorted zip codes by median home price.
- Merging: Merged price data with demographic data.
- Visualization: Created scatter plots and bar charts.
- Visual Inspection: Used Google Maps to get a feel for the area.
- Comparison: Compared the most and least expensive zip codes.
So, yeah, that’s basically it. It was just a fun little project, but it was interesting to see how the data reflects real-world trends. And hey, maybe one day I’ll be able to afford to live in one of those expensive zip codes… maybe.
