Okay, so today I’m gonna talk about my little adventure with Carlos Báez. I was messing around with some data stuff, trying to clean up a dataset I found online. It was a total mess, like someone just barfed numbers and dates into a spreadsheet.
First thing I did, obviously, was load the damn thing into Pandas. I love Pandas, it’s like a Swiss Army knife for data. Anyway, I ran to see what kinda horrors I was dealing with. Turns out, a lot. Missing values everywhere, dates in all sorts of crazy formats, and strings that looked like they’d been copy-pasted from a ransom note.
So, step one: missing values. I decided to fill ’em with the mean for the numerical columns. Simple did the trick. For the string columns, I just filled them with “Unknown”. Not elegant, but it works for now.
Next up: dates. These were a nightmare. Some were YYYY-MM-DD, some were MM/DD/YYYY, some were just… gibberish. I ended up using *_datetime() with a bunch of different formats until I got them all standardized. It took a while, and I definitely swore a few times, but I got there.
Then came the fun part: the strings. These were all over the place. Some had extra spaces, some had weird characters, some were just inconsistent. I used df[col].*() to get rid of the extra spaces. For the weird characters, I used a bunch of regular expressions to clean ’em up. I’m not a regex expert, so I probably did it the hard way, but hey, it worked.
After all that cleaning, I finally had a dataset that was somewhat usable. I saved it to a CSV file using *_csv('clean_*'). Then I started doing some actual analysis. I made a few plots using Matplotlib and Seaborn, nothing fancy, just some histograms and scatter plots to get a feel for the data.
Honestly, the whole thing was a bit of a slog, but it was also kinda satisfying. Taking a messy dataset and turning it into something useful is like solving a puzzle. And now I have a clean dataset that I can actually use for something. So yeah, that was my Carlos Báez adventure. Next time, I’ll try to find a dataset that’s not quite so… challenging.
